Selecting the Perfect DAM for Your Organization

Choosing the right Digital Asset Management (DAM) system can feel overwhelming, especially with the myriad of options and the complexities involved in the selection process. If you’re like many organizations, you may find yourself questioning your choices, feeling uncertain about your requirements, or unsure of how to navigate the procurement landscape. This guide dives deep into the essential steps and considerations for selecting the ideal DAM system for your organization.

Understanding Your Needs

The first step in selecting a DAM solution is understanding your organization’s specific needs. This process involves more than just listing features; it requires a comprehensive assessment of your current assets, workflows, and user requirements.

• Stakeholder Engagement: Engage with different teams to gather insights about their needs and pain points. This ensures that the selected system will cater to the diverse requirements of all users.
• Discovery Process: Conduct interviews and surveys to identify what users expect from the DAM system. This step is crucial as it uncovers needs that might not be immediately obvious.
• Documentation: Document all findings in a clear manner. This will serve as a reference throughout the selection process.

The Importance of a Structured RFP Process

A well-structured Request for Proposal (RFP) is vital in the DAM selection process. It not only communicates your needs to potential vendors but also sets the tone for how they will respond.

• Clarity in Requirements: Clearly outline your requirements using user stories or scenarios. This helps vendors understand the context behind your needs.
• Prioritization: Prioritize your requirements into mandatory, preferred, and nice-to-have categories. This helps vendors focus on what’s most important to your organization.
• Engagement: Allow stakeholders to participate in the RFP process. Their involvement increases the likelihood of buy-in and adoption later on.

Common Pitfalls in DAM Selection

Many organizations fall into common traps when selecting a DAM system. Avoiding these pitfalls can save you time and money.

• Ignoring User Needs: Skipping the discovery process can lead to selecting a system that does not meet the actual needs of users.
• Over-Reliance on Recommendations: Choosing a system based solely on a colleague’s recommendation can be misleading. What works for one organization may not work for another.
• Underestimating Costs: Focusing only on the initial purchase price without considering implementation, training, and ongoing costs can lead to budget overruns.

Evaluating Vendor Responses

Once you’ve sent out your RFP, the next step is to evaluate the responses from vendors. This involves more than just looking at prices; it requires a thorough analysis of how each vendor meets your specific needs.

• Scoring System: Develop a scoring system to compare vendor responses based on how well they meet your requirements. This allows for an apples-to-apples comparison.
• Demos: Schedule vendor demos focused on your specific use cases. This helps you see how the system performs in real-world scenarios relevant to your organization.
• Qualitative Feedback: Collect feedback from stakeholders who attend the demos to gauge their impressions and preferences.

Understanding Customization and Configuration

Many organizations grapple with the concepts of customization and configuration during the DAM selection process. Understanding the difference is crucial.

• Configuration: This involves setting up the system using available features without altering the underlying code. It’s generally easier and cheaper to implement.
• Customization: This entails modifying the software to meet specific needs, which can be more complex and costly. Be sure to inquire about the implications of customization during vendor discussions.

Managing the Implementation Timeline

Timing is everything in the DAM selection process. Many organizations underestimate how long it takes to select and implement a new system.

• Anticipate Delays: Factor in time for vendor responses, stakeholder feedback, and potential procurement delays.
• Implementation Timeline: A typical DAM selection process can take several months, so start early to avoid rushed decisions.
• Post-Selection Support: Ensure that you have a plan for training and onboarding users once the system is selected.

Conclusion: Making the Right Choice

Selecting the right DAM system is a significant decision that can impact your organization for years to come. By following a structured process, engaging stakeholders, and carefully evaluating options, you can make an informed choice that meets your organization’s needs. Remember, the goal is not just to choose a system, but to select a solution that enhances your workflows and improves the management of your digital assets.

For more resources on DAM selection, including checklists and guides, visit AVP’s Free Resources.

Transcript

Chris Lacinak: 00:00

Amy Rudersdorf, welcome to the DAM right podcast.

Amy Rudersdorf: 02:15

Yeah, thanks for the opportunity.

Chris Lacinak: 02:17

I’m really excited to be here. So for folks that don’t know, you are the Director of Consulting Operations at AVP. And I’ve asked you to come on today because you’ve just written a piece called Creating a Successful Dam RFP, and you’ve included with it a bunch of really useful handouts. And so I wanted to just dive into that and have our listeners better understand what the process is, what the value of it is, why it’s important, what happens if you don’t do it, so on and so forth. But I’d love to just start, if you could tell us, what is the expertise and experience and background that you bring to this topic?

Amy Rudersdorf: 02:54

Sure. So before I came to AVP, I was working in government and academic institutions where we had to go through a procurement process to buy large technologies. And so I’ve seen this process from the client side. I know what the challenges are. I know that this can be a really time-consuming process and really challenging if you don’t know how to do it. And then when I came to AVP, I had the opportunity to help guide clients through this process. And over the years, we’ve really refined what I think is a great workflow for ensuring that our clients get the right technology that they need.

Chris Lacinak: 03:33

And you’ve been doing this for years, as you say. So I’m curious, why now? What inspired you to write this piece after refining this for so many years? Why is now a good time to do it?

Amy Rudersdorf: 03:44

I think the main… Well, there are a couple of reasons, but one of them is that there’s just been in the last couple of years a proliferation of systems. There are hundreds of systems out there that we call DAM or MAM or PIM or PAM or digital preservation. There’s all kinds of systems. From a pricing standpoint, DAMS range from as low as $100 a month to six figures annually. And the market is really catering to a diverse set of needs from B2B to cultural heritage to Martech, and then your general purpose asset management systems. And I’ve seen organizations recognize that it’s really important to do it right. They want to make sure that when they acquire technology, it’s something that’s going to work for their institution for the long term. But they really struggle with how to do it. So what I hope through this piece is that I can help individuals and organizations with this step-by-step guide to successfully procure their own technology without us, and maybe in addition, see the value of working with an organization like AVP.

Chris Lacinak: 05:00

And how would you describe who this piece and these checklists are for?

Amy Rudersdorf: 05:06

Well, I would say specifically, they’re for organizations looking to procure a DAM. And this could be your first DAM or moving from a DAM to an enterprise DAM technology or MAM. So that’s the specific audience. But really, if someone’s looking to procure a technology, the process is going to be very similar. And so many of these checklists will be useful to those folks as well.

Chris Lacinak: 05:38

Yeah, I think it is important. You’ve kind of touched a couple times on, you know, the piece is called, or calls out specifically DAM. As you mentioned, and it’s worth reiterating, we’ve talked about it here on the podcast before, but we use a very broad interpretation of DAM to include things like you mentioned MAM, PIM, PAM, digital preservation, so on and so forth. So it’s good to know that folks looking for any of those technologies in the broader category of DAM that this is useful for. For someone out there considering procuring a DAM and thinking, you know, we don’t need an RFP process or we don’t need to use this complex, time-consuming process, is it still useful for them? Or are they things that they can grab out of this piece, even if they don’t want to go through the full process?

Amy Rudersdorf: 06:32

Well, my initial response to this question is, you should be considering the RFP process. And if not a full RFP process, at least an RFI, which is a request for information as opposed to a request for proposals. The RFI is a much more lightweight approach. But in either case, I feel like this document, this set of checklists is useful for anyone thinking about getting a DAM. Because the checklists step you through not just how to write an RFP, but also how to gather the information you need to communicate to vendors. So if you look at checklist number two, for instance, it really focuses on discovery and how to undertake the stakeholder engagement process, which you’ll want to do whether or not you’re writing an RFP. You really need to understand your user needs before you set out to identify systems that you might want to procure.

Chris Lacinak: 07:39

Yeah, that’s a good point. And maybe it’s worth saying that for smaller organizations maybe that aren’t required to use an RFP process, that what you’ve put down here, when I look at it, I think of it’s kind of the essential elements of an RFP, right? You might give this to an organization that then wraps a bunch of bureaucratic contractual language around it and things like that. But this is the essence of a organization-centered or user-centered approach to finding an RFP or finding a DAM that fits. So let’s talk about what are the pitfalls that people run into when they’re procuring a DAM system?

Amy Rudersdorf: 08:25

So I’ll start by saying, I think it’s really important that when you’re procuring a technology that you talk to your colleagues in the field and see what they’re using. But just, as much as that’s important, I will say that’s also a major pitfall if you do that, if that’s your only approach. Because you may have a colleague who uses a system they love, it does everything they need it to do, and they say to you, “Yeah, you should definitely buy this system.” But the reality is that that system works for them in their context and your context, your stakeholders are very different. And so that assumption is, I think, flawed. You have to go through a stakeholder engagement and discovery process where you’re talking to your users and finding out what they need, what their requirements are in order to communicate to vendors what it is that you need that system to do for you, as opposed to what it’s doing for your colleagues. I’ll say, Kara Van Malssen posted a LinkedIn post a few weeks ago, and it was really useful. It’s the eight worst ways to choose a DAM based on real world examples. And one of those is choose the system that your colleague recommends. And as she says, your organization’s use cases are totally different from theirs. I think there’s also the pitfall of, you go to a conference and you met a salesperson, they were really nice, the DAM looked great, it did everything that they said it could do. But when you’re at a conference, that salesperson is on their best behavior, and they’ve got a slick presentation to show you. So just approaching this with a multifaceted approach is going to be far more effective than just saying, my colleague likes it, or I saw it at a conference. You combine all of those things together as part of your research to find the system that works for you.

Chris Lacinak: 10:44

Yeah. And that makes me think of requirements and usage scenarios, which I want to dive into. But before we go there, I want to just ask a similar question, but with a different slant, which is, what’s the risk of not getting this right, of selecting the wrong DAM?

Amy Rudersdorf: 11:02

Yeah, so the risk is huge. I think DAMS are not cheap. I would say that’s the first thing. You do not want to purchase or sign a contract, which is typically multi-year, with a vendor for a system that doesn’t work for you. You will be miserable. And I think more importantly, your users will be miserable. And this will cause work stoppage, potentially loss of assets, and it could be a financial loss to the organization. Not doing this right will have repercussions all the way down the line for the organization, and you’ll be hurting for years to come.

Chris Lacinak: 12:00

Yeah, I think one thing we’ve seen is an organization, maybe they go out and they buy a cheap DAM, and maybe they think, “Well, you know what? It’s cheap. If it doesn’t work, we only spent, what, $15,000 or $20,000,” or whatever the case may be. Not realizing that that might be the cheapest part, right? Because you got to get organizational buy-in, you got to train people, you got to onboard them. And then it goes wrong, or it goes, you know. And we’ve seen this. We’ve come in on the heels of this. Where like, there’s a loss of trust. There’s poor morale. People don’t believe that it’s going to go right this time. So yeah, there’s a lot to lose there, and it’s more than just the cost of the DAM system, as you point out. So let’s jump back to requirements and usage scenarios. So you talk quite a bit about the importance of getting requirements and usage scenarios documented and getting them right. Could you just talk a bit about those two things, how they relate to each other, and then we’ll kind of dive in and I’ll ask you for some examples of each of those.

Amy Rudersdorf: 13:03

Okay. Well, this is where I’ll probably start to nerd out a little bit. But you’re going to, as the centerpiece of your RFP, communicate your needs. And when I say your, I mean your organizational needs for a new system. So you will be representing the needs of your stakeholders, if you’re doing it right. Their challenges or pain points, their wishlist, all of that needs to be communicated to vendors in a clear and concise manner that they can interpret appropriately and provide answers that are meaningful to you so that you can then analyze the responses in such a way that you can understand whether that system will work for your organization. So structuring your requirements and your usage scenarios, we call them usage scenarios at AVP, lots of people call them use cases, but structuring those correctly is going to be the part that gets you the responses you need in order to make a data-driven decision.

Chris Lacinak: 14:20

And I’ve heard you talk about before, I mean, to that point, I guess, we have seen RFPs in which the question that is posed is, can you do this thing to the vendor? And the vendor just simply has to check a yes or no box. To your point, I think from what I’ve seen from your work is like, you really get to how do you do this thing so that there’s much more information around it. So it sounds like structuring those, getting those right and structuring them in the right way is going to give you not a yes or no answer, which is often misleading and unhelpful and things, but like a much more nuanced answer.

Amy Rudersdorf: 14:56

I think the other part is, you want to help the vendor understand. You want to work with them to get the best outcome from this process. And so giving them as much context as you can is important too. And that’s why we structure our requirements the way we do so that the vendor sees what the need is, but also understands why we’re asking for it.

Chris Lacinak: 15:20

That’s a really good point. That is something that you hear vendors complain about with RFPs that they don’t provide enough information. And I want to ask you later about what ruffles the feathers of vendors, but let’s keep on the requirements and usage scenarios. So can I ask, you said most people call them use cases, AVP calls them usage scenarios. Why is that?

Amy Rudersdorf: 15:42

Well, a use case is just a standalone narrative of, it’s a step-by-step narrative of what the needs are for a system. So you’re telling a story about a user going through a process or a series of processes. A usage scenario offers context beyond that. So you provide some background information. Why is this usage scenario important? Well, you’re explaining that we’re asking you to respond to this because this is our problem. And so providing, again, it’s that context. So the vendor understands why you’re asking for something or why you need something. It just makes their answers better. They’re more informed. I think they feel more confident in their responses. And so it’s just a little bit more context around the use case than just a standalone use case.

Chris Lacinak: 16:38

What does a well-crafted requirement look like?

Amy Rudersdorf: 16:44

So at AVP, we use the user story structure, which comes out of the agile development process. It’s basically an informal sort of general explanation of a software feature that’s written from the perspective of an end user or a customer. And we call them personas. So as part of your user story, it’s a three-part structure. So as a persona, or the person that needs something to happen, I need to do something so that something is achieved. So a standard requirement is just a statement of a need. But here you can see there’s a real person. So this is user-driven content. There’s a real person who has a real need because something really needs to be achieved. And I think that structure is really powerful. I just said “really” a lot of times. But there are a lot of examples for how to build user stories on the web. And again, just giving that vendor as much context as possible. You have to think about you’re handing over to these vendors 20-page documents that they have to sift through to try to understand what your needs are and how to match them to their system. And so any background you can give them, any context you can give them, is just going to be a win for everyone. So it really will impact whether you’re seeing responses from the vendors that align with your needs or not. I think it provides clarity in the process that the standard requirement structure doesn’t offer.

Chris Lacinak: 18:31

Right. So I’ll go out on a limb here and venture a guess that a bad requirements list might be a list of bullet points, something like integrations, video, images, things like search, things like that. Not a lot of context, not a lot of useful information.

Amy Rudersdorf: 18:49

The other thing to keep in mind is these have to be actionable. So you can’t say “fast upload.” Every vendor is going to say “yeah, our upload is super fast.” But you could say, “As a creative, I need to upload five gigabyte video files in under 30 seconds” or something like that. You want them to be something that a vendor can respond to so that you get a useful response.

Chris Lacinak: 19:24

How might you explain what a well-crafted usage scenario looks like?

Amy Rudersdorf: 19:30

Sure. So usage scenarios are, as I said earlier, they’re these step-by-step narratives. So it’s a story about a user moving through the system. They flow in a logical order. They cover all of the relevant steps, all the decision-making points. They are user-centric. So the scenario should define who the user is. We always use real users in our usage scenarios. So we’ll have identified some of the major personas from the client’s organization. And that might be, like I said, a creative. It might be the DAM Manager. It’s real people who work in their organization. We don’t name them by name, but we name them by their title. So that this is truly representing the users. So it’s the story of the user performing tasks. And every usage scenario should have clear objectives, outlining what the user is trying to achieve. And it’s their specific tasks. They’re solving a problem. So it might be, for example, just this isn’t how you would write it, but it might be a story about a marketing creative who needs to upload assets in batch and needs to ensure that metadata is assigned to those assets automatically every time they’re uploaded. And the DAM Manager is pinged when those new uploads are in the system so that they can review them. So that might be a story that you would tell in a usage scenario. It’s realistic. It’s based on real people. And it represents real challenges that users face.

Chris Lacinak: 21:26

Yeah, that makes a lot of sense. There’s a lot of things that we see in marketing and in communication around the power of stories. I can imagine that that is a more compelling and meaningful way to communicate to vendors. It makes me wonder, in your experience in working with organizations, you craft this story and someone listening might think that’s information that’s at the ready that just simply needs to be put into story form. But I’m curious, you put a lot of emphasis on discovery and talking to different stakeholders. And I’m just curious, how useful is this process to people within an organization coming up with these stories? Are they at the ready? Or is it through the discovery process that they’re able to synthesize and really understand to be able to put it into that form?

Amy Rudersdorf: 22:21

Yeah, I would say if you take nothing away from this discussion except the fact that discovery is absolutely necessary as part of your technology procurement process, it’s that. Discovery is the process of interviewing your users and stakeholders to understand what their needs are, their current pain points are, and what they wish the system could do. That’s it in a nutshell. And I have never had a core team or the person leading the project on the client side say, “Oh, I already knew all that.” Time and time again, their eyes are opened to new challenges, new needs from these users. So, it’s a really powerful process. I think this is taking it a little off topic, but just to ensure that you have buy-in from your stakeholders, bringing them in at the beginning of the process is key. So it’s a benefit for you in that you learn what they need, you learn how they use systems today and what they need the system to do in the future, but you’ve also kind of got them engaged in the process as well. They see that they’re important and that you’re making decisions on their behalf and thinking of them as the system is being procured. And all of that together, I think, is really powerful and can only make for a better procurement process.

Chris Lacinak: 23:58

Yeah. So, wow, it really does point out the value of the process. So earlier I was saying like, what’s the pitfalls or maybe someone doesn’t want to go through the RFP process, but like the RFP, I mean, let’s say somebody did just throw together an RFP without going through the process, it would be a very different RFP than after going through the process. And the process, and also it sounds like the process solidifies things that don’t manifest in an RFP. They actualize through greater adoption and more executive buy-in and in other ways that you wouldn’t have if you didn’t go through this.

Amy Rudersdorf: 24:35

Absolutely.

Chris Lacinak: 24:36

Let me ask about that, the buy-in side. So in discovery, well, not so much the buy-in, I think more about adoption here, but like one of the challenges has to be you talk to, let’s say 10 different people, each person has many requirements they want to list. And maybe one of those is in creative ops, maybe one is in marketing, maybe one is in more administrative role. Who knows? They are different stakeholders with different focus points and they all give you lots of requirements. And on one hand, I have to think it’s important for those to be represented so someone doesn’t look at it and say, “Well, it doesn’t have any of my stuff in there. This system’s not right for me.” On the other hand, it’s got to be such a huge load. It just makes me wonder, how do you get to prioritization to both represent but also make sure that the most important stuff is represented up front?

Amy Rudersdorf: 25:31

Right. Well, it’s definitely a team process. So the first thing I’ll say is just to provide a little context, when we do these requirements, these user stories, in the past, we would write 150 requirements. And we try really hard not to do that. It’s really hard on the vendors to ask them to respond to 150 requirements. And so we really try to synthesize what the users are telling us and really hone in on the key needs. Now that doesn’t mean that we disregard different users’ needs. But in some cases, their need is something that every dam can meet. So there’s no need to include that in the requirement list. You want to be able to search. They all can search, so that should be fine. But once you have got your requirements list, which I think in a healthy RFP is probably in 50 or so requirements range, then it’s up to the organization to prioritize those requirements. So as a company, we will write those user stories on behalf of the client. But then we give them that list and say, now prioritize these. This is your part of the process. And typically, this is the core team’s job. So when we work with a client, there’s usually two to four people who are part of the client core team. And they are either sitting in on the discovery interviews or reading transcripts or just really engaged in the process. So they understand what these priorities look like. So by the time they get that list, they should be able to, as a group, sit down and identify the priorities. And we prioritize based on the list we do is mandatory, preferred, and nice to have. So if there are some requirements that someone is noisy about really wanting to have in the list, we can always just call it nice to have. And they’re there. But then it’s not mandatory that the system is able to do it.

Chris Lacinak: 27:59

So it sounds like that’s done through a workshopping or group process where folks are able to discuss and talk about those. So that seems like that innately. Being able to be heard, have the conversation, and then even if it’s not called mandatory, you still feel like you got to have the conversation and it’s represented in some way.

Amy Rudersdorf: 28:23

Yeah. And I wanted to also say that gathering these requirements from the users is really obviously important, as I’ve said. But then engaging them throughout this process is also really valuable. And so not just asking them at the beginning what they need, but actually letting them come to demos and things like that, I think, is important as well. It’s going to make implementation and buy-in much more successful.

Chris Lacinak: 28:51

You’ve got these requirements. You’ve got these usage scenarios. You create a bunch of things to hand over to a vendor. I guess I’m wondering, how do you manage apples to apples comparisons? Because there’s going to be such a wide variety in how they respond to things. And how do you manage comparing pricing to make sure that there’s not surprises down the road? How do you manage those things?

Amy Rudersdorf: 29:16

Well, so I’m going to set the pricing question aside for a second. So the way that we do it at AVP is, I think, a methodology that is unique to the RFP process. And that is that we’ve created a qualitative methodology. So we create the requirements, and the client prioritizes them. The vendor responds to them in a certain way. And then we’re actually able to score those responses. And it’s based on priority. So if something’s mandatory, it’s going to get a higher score. The vendor may say it’s out of the box. They’re going to get a higher score. If they say it has to be customized to do that, they’re going to get a lower score. So we create this scoring structure that allows us to hand over to the client data that they can look at. So they’re actually seeing side-by-side scores for all of the respondents to the RFP. Pricing is really tricky. It is so complicated. Every vendor prices their system completely differently. And so we really have to spend a lot of time digging out the details to understand where the pricing is coming from and what the year-to-year pricing looks like. And then we do actually provide a side-by-side analysis of that as well. It’s really tricky to do it. But in the end, the client gets data that they can base their decisions on. And then you asked a question about avoiding surprises when it comes to pricing. I think this is the hardest thing to talk about when you’re buying technology. And I think this is probably the case for lots of different types of technology, not just the DAM, MAM, PIM, PAM world. But this information is not widely available on the web. You can’t go to a vendor’s website and see how much it’s going to cost for you for the year, an annual subscription or license. And the reason for that is that there are so many dependencies around their pricing, including how much storage you need and what that storage growth looks like over time, how many users and what type, some vendors base their pricing on seats, like the number of users you have and the different types of users in their different categories, SLA levels, service level agreement levels. So if you want the gold standard, it’s going to cost this. So the costs are going to be unique to your situation. Just to sort of toot our horn that we know this market really well. And so if somebody says, how much does it cost for an annual license to vendor X? I can say, but that just comes with years of experience. Otherwise, it’s a wild west out there as far as pricing goes.

Chris Lacinak: 32:48

You know that I know that you want to get your hands on Amy’s how to guide and handouts for DAM selection. Come closer and I’ll tell you where to find it. Closer. I don’t want anyone else to hear this. Okay. It’s weareavp.com/creating-a-successful-dam-rfp. That’s where the guide is. Here’s where you get the handouts. It’s weareavp.com/free-resources. Okay. Now delete those URLs once you download them. I don’t want that getting out to just anyone. All right. Talk to you later. Bye.

Two thoughts here. One is, um, I mean, you talked about the kind of like spreadsheet analysis and scoring. But I know you, you dive deeper than that. I mean, part of your comparison, comparative analysis process is also demos as well. And I imagine that that, that plays, that makes me think of a couple of things. One is like one using those as a tool in the apples to apples comparison. But two, like I imagine, you know, you have this list of requirements and uses scenarios and some solutions can probably meet that out of the box. And some probably need some custom development to do it or some sort of workflow development or something in order to meet those. So could you just talk a little bit about the role of demos and custom configurations related to pricing?

Amy Rudersdorf: 34:20

Yeah. So a demo is a general term, um, that can mean many different things in this, in this realm. Um, so vendors love to give demos, uh, and they would love to, you know, spend an hour and a half with you telling you how great their system is. That’s their job. Their system may be great. And, and so, you know, that’s, that’s okay, but that’s not how you base a decision, a purchasing decision. Um, you, you, you go, you see those, those demos, those sort of bells and whistles demos to get a sense of what the system looks like. What we do is, um, after the RFP comes back and you know, we’re sort of playing with different ways to do that now. Um, but the way that we’ve done it typically is that after the, the, um, RFP comes back, let’s say you get six responses, you choose your top three, and then you spend two hours in a demo with the, with the vendor. The vendor does not get to, um, uh, make the agenda. We do. And in that demonstration, they’re going to, um, respond to the, some of the usage scenarios that we wrote for the RFP. So for 15 minutes, talk to us about that uploading, um, usage scenario I mentioned earlier. And in order to do that, here are assets from the organization and metadata from the organization, um, that you must use in your examples. So now you’re seeing side by side, um, demonstrations of how the systems work with your data. And I think that’s really powerful, um, because now you’re going to start to see the system maybe move a little slower with that five gigabyte movie that you have. Um, and, and, and it’s not quite as slick as the, as the, the assets they use typically in their, in their demos. So you get to see a real sense of how the system works in that way. And as part of those demonstrations, we always have the clients fill out feedback forms. So again, um, we’re going to get some, some qualitative, um, responses like what, what did you like? What other questions do you have? But we’re also going to get quantitative responses, score this vendor, um, on use the, on the usage scenario that you saw, what, you know, from one to five, did they, did they do what they said the system could do? And so again, we’re, we’re trying to set up opportunities for that apples to apples, um, uh, comparison. And how about the, um, kind of custom configuration aspects? I guess this goes, really goes back to kind of, I guess it’s both pricing and timeline, right? Like how do you manage that through the RFP process? I think that’s really probably one of the toughest things. The vendors differ on how involved they want to be in customization and configuration. Some systems require lots of configuration, um, but not so much customization. And maybe we should define those terms. So configuration means, you know, pressing some buttons behind the scenes to make something happen. Um, maybe turning on a feature, turning off a feature. Customization means writing some code to make the system do what you need it to do. So configuration should be cheaper and easier than customization. And so, uh, from a configuration perspective or from, from a cost and timeline perspective, configuration is, is less of a challenge. Um, because typically the, the vendor can do that. And that’s part of the, the offering. Customization is different. Uh, if something is custom, we ask them to tell us how much time it’s going to take and how much it’s going to cost to do it. Uh, so that that’s in, that’s in their proposal as well. In order to get to that point can be challenging. You really have to be very specific and clear about what you need. Um, so an example would be integration, which is something that everyone asks for, um, in an RFP. DAMS aren’t systems that just stand alone in your organization. They integrate with collections management systems or marketing technologies. Um, and so understanding for instance, who is responsible for building the integration and maintaining the integration. Uh, knowing that upfront is super important. If a vendor says, yeah, we can do that. Make sure they explain how that happens and what it’s going to cost and what the real cost is going to be for you. Um, so I, I guess I just say that you’re your best advocate and, and if you have a question, ask it and ask them to, to, um, document it.

Chris Lacinak: 39:44

Speaking of vendors, like what, what have you heard as responses from vendors to, you know, the, the RFPs that you’re proposing people do in this process. Do they love them? Do they hate them like that? How have they been received by vendors generally speaking?

Amy Rudersdorf: 40:01

Um, well, I’ll, you know, we have actually reached out to vendors and asked them this question and I have heard on a number of occasions that they really like the RFPs that we put together for them because they’re so, they’re so clear and they understand what we’re asking and why we’re asking it. And you know, going back to this, this point I made earlier about not having 150 requirements, you know, the vendors appreciate that as well. It’s a, it’s a lot of work for them to respond to these and, and we, we don’t want this to be onerous, um, or overly complicated for them. So we’ve really tried to create RFPs that serve the client foremost, but also, um, make the process as pain free as possible for the vendors as well. And we’ve gotten feedback from them, from a number of them that they like, um, the way that we present the data.

Chris Lacinak: 41:04

Having been someone that’s been on the responding side of RFPs, I will say, you know, one of the things you worry about when you’re in that position is the, uh, customer being able to make an apples to apples comparison, making sure that the appropriate context is there, making sure that they fully understand, um, and that you have all the right information to be able to provide the right responses. So I guess everybody has a vested interest in being clear and transparent, right? That’s actually helpful to everybody. And I imagine that also helps people like opt out. Maybe a vendor says, you know what, we’re not, they look at that RFP and they say, this really is not our strong spot. We should not spend the time on this. So that’s probably helpful to them to be able to filter out what is and isn’t in their wheelhouse.

Amy Rudersdorf: 41:50

Yeah, absolutely. I, I think it’s important to recognize that we don’t work in a vacuum. Um, we, we work very closely. We are vendor neutral as a company, but we work very closely with, um, the sales teams at lots of different, um, vendor companies. And we want to be partners with them as well. We want, we want them to be successful, um, whoever they are and whatever we can do to make sure that they’re able to, um, show off their system as well at, and as appropriately as possible, you know, that’s a win for everyone. And so I do really keep in mind that perspective when I’m putting these, um, documents together.

Chris Lacinak: 42:40

So what are some of the things that you’ve heard vendors complain about with regard to RFPs? Not ours, of course, other people’s RFPs. Like, what are the things that would turn a vendor off or make them not want to respond or make them feel poorly about an RFP?

Amy Rudersdorf: 42:57

I think I’ve mentioned this now a couple of times. I think one of the common deterrents is just the overwhelming number of requirements. And when they’re not written as user stories, they can be really confusing, hard to interpret. Um, and, and just really probably pretty frustrating to, um, try to answer or respond to. The other challenge, and I talk to clients all the time about this is you can’t make every requirement mandatory because there is not going to be a system out there that can do absolutely everything you want out of the box turnkey solution. Um, and it’s, it’s unreasonable to ask for that, I think, in my opinion. Um, and so, you know, making sure that you’re really prioritizing those requirements helps vendors see that you’ve really thought about this and that you, um, understand what you’re asking for and what your needs really are. So I think that maybe isn’t a deterrent, it’s a positive, but flipping that every, every requirement being mandatory is, um, is probably really frustrating. I would say too that, I mean, there’s, there’s sort of the flip side of this. Um, there’s excessively detailed or overly complex, and then there’s not enough information to, to provide a, a useful response. Um, so finding that sweet spot where you’re giving them the context, the background, the information they need, um, but not overwhelming them is, is, is, um, important. And I, you know, we’ve all seen a poorly structured RFP, you know, something that lacks clear vision or is ambiguous or vague, or, you know, is filled with like grammatical errors and spelling mistakes. It just makes everybody look bad. And, you know, if I was responding to that, I would question, um, the organization and their sort of dedication to this process.

Chris Lacinak: 45:04

We should, we should point out that you’re a hardcore grammarian.

Amy Rudersdorf: 45:08

I am.

Chris Lacinak: 45:09

So, um, let’s talk about timeline. What, you know, you talked about who is this, who’s the guide and checklist for, and you said it’s for people who are maybe getting their first DAM. Maybe it’s for people who are getting their second or third DAM. They’ve already got one. When is the right time to start the RFP process? People are surprised at how long this process takes. At AVP, it is a 20 week process. And so that’s five months. And that is, uh, that is sort of keeping all the, the, the milestones tight, moving the process along quickly. Uh, that’s, that’s just how long it takes. Um, so, you know, thinking about things like, Oh, my contract is coming up in a year, you know, working backwards from that, you need a solid six months or more for implementation. So, you know, you should, you should be, um, working on that RFP now. Uh, but, you know, give yourself, you know, expect this process, if you do it right, to, to take a solid four or five months. Um, and, and then you also have to, you know, build in buffer for your procurement office. You’ve got your InfoSec, uh, reviews. All of these things can take even longer. So, um, yeah, as soon as you realize that you’re going to get a new system, uh, start the work on that RFP.

Chris Lacinak: 46:40

And we should say that five months that you mentioned includes, uh, a pause while you wait for vendors to respond to the RFP as well. Right. And how long is that period typically?

Amy Rudersdorf: 46:54

Um, I, I usually say a month, I think less than a month is not, is not being a, a good um, you know, I think it’s sort of, uh, inhumane to make them respond in, in less than a month. These are complicated. They want to make sure they’re getting it right. We want them to get it right. So a month is a solid amount of time. We also build in time where they can ask questions and so they can’t really start working on it until they get the questions, the answers back. So a month I think is the, is the sweet spot there.

Chris Lacinak: 47:28

You’re right. I, I feel like we run into frequently where people might hear five months and they’re a little put off by how long that sounds. Uh, and then it is extraordinarily common that, uh, contracting and security takes significantly longer than that to get through. So that is something I think people often underestimate, especially if you’re not used to working through procurement, like that’s something that people really need to consider as a, as a part of their timeline.

Amy Rudersdorf: 47:57

This is a real, this happened yesterday. I have a client who was very adamant that we, um, shorten that time, time frame by a month. So we had compacted all of our work into four months and they came back to me and said, you know, we’re going to need more time. So let’s, let’s go with your original timeline. And then he said, it’s like, you know what you’re doing.

Chris Lacinak: 48:24

Yeah, yeah, yeah. That happens sometimes. So I mean, I do what, you know, I have, there has been this concept lately though, that I’ve heard repeated consistently about a fast track selection process and it’s something that’s significantly faster. And I wonder, I don’t know, do you have thoughts about that? Is that a realistic thing? Does that sacrifice too much? Is it possible as long as you’re willing to accept X, Y, and Z risks? I mean, what’s the fastest you might be able to do a selection process if someone really pushed you?

Amy Rudersdorf: 49:00

That’s a tough question. I mean, there are, there are people who talk about this fast track process. I think you’re putting yourself at risk if you don’t at least spend the time you need to with your end users and stakeholders. Whatever else you do around this process to make it go faster for you, you know, whether it’s not do the RFP and just invite vendors to do their demos. I still think spending the time with your stakeholders is going to be really important and drafting their requirements in some way that communicates those to the vendors so that when you have them demo, you have them demo with your user needs in mind. You know, I think you could do that. I’m not entirely sold on it. I think our process works really well. But if someone came to us and said, “We want to do it a different way,” I think we’d be willing to discuss other methods.

Chris Lacinak: 50:08

It makes me think, you know, if you were going to have, say, I’m thinking of a shape, Amy, and I want you to draw it, and I’m going to give you a number of dots to draw the shape that I’m thinking of, right? If I give you three dots, the chances of you getting that shape right are pretty slim. If I give you 50 dots, you’re more likely to get the shape that I’m thinking of, right? You can draw the line and connect the dots. And it seems that if you fast track it, you’re going to miss some dots and you’re less likely. And as we talked about earlier, like, I guess I do want, I mean, now that we’re talking about it, it’s like weighing the risk reward here. Like, okay, let’s say that the fastest you could do this with some level of certainty that everybody was willing to accept was three months. But you increase your chance of getting it wrong by 30%. We talked earlier about what are the risks of getting it wrong. Like, that just seems on its face obvious that that’s not worth it. Like, the amount, it’s not just the cost of the DAM system. Because to get back all those stakeholders again, do discovery again, go through the process. Like, everybody’s burnt. They’re unhappy about it. The thing didn’t work. It failed. I don’t know. It just seemed, yeah. Now that we talk about it, it just seems obvious that that’s not a great idea.

Amy Rudersdorf: 51:25

You’ve broken their trust. And if you, and I’ve seen this in implementation too, where if you invite your stakeholders into the system before it’s ready, or it’s not doing what they need it to do, they’re going to hesitate to come back. And to have to go through this process all over again, I just can’t see, you’re going to lose their trust.

Chris Lacinak: 51:56

You can imagine the Slack message already. Hey, did you see that? I went in there and nothing’s in there. I couldn’t find anything. It’s like all of a sudden that starts creating a poor morale around the system.

Amy Rudersdorf: 52:07

Yeah, it gives me shivers.

Chris Lacinak: 52:10

Yeah. In your piece, you go through the whole RFP process. We haven’t gone through that here because it’s rather lengthy and I think that it’s a lot to talk about. So we’ll leave that to folks to see in the piece. But I’m curious if you could tell us, when you see people do this on their own and they don’t have the advantage of having an expert like yourself guiding them along, what’s the number one most important part of the process that you see people skip?

Amy Rudersdorf: 52:40

Well, I think it’s the discovery process. It’s getting in front of your users and stakeholders. Without that information, you don’t know what you need. And you can only guess at what you need based on your personal experience.

Chris Lacinak: 53:00

So people think, “Oh, I know what my users need. I’ve been working with these people for years. I can tell them.” Or maybe like, “I know what we need better than anybody else. I’m just going to write it down.”

Amy Rudersdorf: 53:08

I don’t know if I said this already, but I’ve never had anyone say, “Oh yeah, I knew all that,” after they went through the discovery process. Time and again, they’re like, “Wow, I had no idea.”

Chris Lacinak: 53:18

I bet.

Amy Rudersdorf: 53:20

Yeah. It’s pretty interesting to talk to the core team after the discovery process is complete. Because they often sit in on these interviews and you can just see their eyes pop when they hear certain things that they had no idea about. And that happens every time we go through this process.

Chris Lacinak: 53:48

So we’ll put a link to your piece in the show notes here. I’m curious though, if you could tell us when people download the handouts, what’s in there? What can people expect to see?

Amy Rudersdorf: 54:01

It’s six checklists that guide you through the entire RFP process, from developing your problem statement to the point where you’re selecting your finalists. Some of the checklists are things that you need to do. So they kind of step you through discovery and how you structure your RFP. But then there are checklists that you can actually include in your RFP. We always have an overview document that sort of introduces the RFP to the vendors. And there’s a very long checklist that we include that they have to answer those specific questions that are in that download. They’re in the actual RFPs that we create as well. So it’s a little bit of, we’re offering a little IP to users.

Chris Lacinak: 54:56

So it’s the things that you would use for yourself as part of the process.

Amy Rudersdorf: 55:01

Yeah.

Chris Lacinak: 55:02

That’s great. So for the question I ask everybody on the DAM Right podcast, which is, what is the last song that you added to your favorites playlist?

Amy Rudersdorf: 55:12

Oh, I’ll tell you right now. I have it right in front of me.

Chris Lacinak: 55:17

Great.

Amy Rudersdorf: 55:18

Heart of Gold by Neil Young.

Chris Lacinak: 55:19

What were the circumstances there?

Amy Rudersdorf: 55:21

He’s back on Spotify. He had left Spotify. They pulled all of his stuff off Spotify. And I realized he was back. And so I grabbed that song. Yeah. Four days ago.

Chris Lacinak: 55:32

That’s right. Well, thank you so much for joining me and sharing your expertise and your experience and all this great information. It’s been fun having you on. I really appreciate you taking the time.

Amy Rudersdorf: 55:41

Yeah. Thanks again for the opportunity to talk about a topic that some people might not find very exciting, but I do.

Chris Lacinak: 55:52

You know that I know that you want to get your hands on Amy’s how-to guide and handouts for DAM selection. Come closer and I’ll tell you where to find it. Closer. I don’t want anyone else to hear this. Okay. It’s weareavp.com/creating-a-successful-dam-rfp. That’s where the guide is. Here’s where you get the handouts. It’s weareavp.com/free-resources. Okay. Now delete those URLs once you download them. I don’t want that getting out to just anyone. All right. Talk to you later. Bye.

Exploring the Future of Object Storage with Wasabi AiR

In today’s data-driven world, object storage is revolutionizing how we manage digital assets. Wasabi AiR, an innovative platform, uses AI-driven metadata to enhance this storage method, making it more efficient and accessible. This blog explores how Wasabi AiR is reshaping data management, the benefits it offers, and what the future holds for AI in this field.

How Wasabi AiR Transforms Object Storage

Wasabi AiR integrates AI directly into storage systems, automatically generating rich, searchable metadata. This feature allows users to find, manage, and utilize their data more effectively. By enhancing storage with AI, Wasabi AiR helps organizations streamline data retrieval, boosting overall productivity and efficiency.

The Evolution of Metadata in Object Storage

While AI-generated metadata has existed for nearly a decade, its adoption in data storage has been slow. Wasabi AiR simplifies this integration, allowing organizations to leverage automation without complexity.

Aaron Edell’s Vision for AI in Storage

Aaron Edell, Senior Vice President of AI at Wasabi, leads the Wasabi AiR initiative. His vision is to make AI a seamless part of data management, enabling organizations to generate metadata effortlessly and manage digital assets more efficiently.

Advanced Technology in Wasabi AiR

Wasabi AiR uses advanced AI models, including speech recognition, object detection, and OCR, to create detailed metadata. This capability enhances the storage system by making data more searchable and accessible. One standout feature is timeline-based metadata, enabling users to locate specific moments within videos or audio files stored in their systems.

Use Cases: How Wasabi AiR Benefits Different Sectors

Wasabi AiR has numerous applications across industries, improving data handling in:

• Media and Entertainment: It helps create highlight reels quickly, as seen with Liverpool Football Club’s use of Wasabi AiR to boost fan engagement.
• Legal Firms: Law firms save time by managing extensive video and audio records efficiently.
• Education and Research: Institutions make their archived content more accessible through AI-driven metadata.

Cost Efficiency of AI-Powered Data Storage

Wasabi AiR offers a cost-effective solution, charging $6.99 per terabyte monthly. This straightforward pricing makes it easier for organizations to predict costs while benefiting from AI-enhanced solutions.

Activating Wasabi AiR

Setting up Wasabi AiR is simple. Users connect it to their existing system, and the platform begins generating metadata immediately, enhancing value and usability without requiring complex configurations.

The Future with AI

As data continues to grow, efficient management is increasingly important. Wasabi AiR is set to play a key role by enhancing searchability and usability through AI-driven solutions.

Integration and Interoperability

Wasabi AiR supports integration with other data management systems, enhancing workflows. Its APIs allow seamless metadata export to Digital Asset Management (DAM) or Media Asset Management (MAM) systems, making data handling more efficient.

Ethical AI Considerations

Ethical considerations are crucial when implementing AI in data management. Wasabi AiR ensures data security and transparency, building trust and ensuring responsible AI use.

Conclusion: Elevating Data Management with AI

Wasabi AiR is a game-changer, enhancing how we manage, search, and utilize data. By combining AI with innovative technology, organizations can significantly improve efficiency, accessibility, and data management. As digital data management continues to evolve, Wasabi AiR positions itself as a leader, offering a future where data isn’t just stored—it’s actively leveraged for success.

Transcript

Chris Lacinak: 00:00

The practice of using AI to generate metadata has been around for almost a decade now.

Even with pretty sophisticated and high-quality platforms and tools, it’s still fair to say that the hype has far outpaced the adoption and utilization.

My guest today is Aaron Edell from Wasabi.

Aaron is one of the folks that is working on making AI so easy to use that we collectively glide over the hurdle of putting effort into using AI and find ourselves happily reaping the rewards without ever having had to do much work to get there.

It’s interesting to note the commonalities and approach with both Aaron and the AMP Project

folks who I spoke with a couple of episodes ago.

Both looked at this problem and aimed to tackle it by bringing together a suite of AI tools

into a platform that orchestrates their capabilities to produce a result that is greater than the

sum of their individual parts.

Aaron is currently the SVP of AI at Wasabi.

Prior to this, he was the CEO of GreyMeta, served as the Global Head of Business and

GTM at Amazon Web Services, and was involved in multiple AI and ML businesses in founding

and leadership roles.

Aaron’s current focus is on the Wasabi AiR platform, which they announced just before

I interviewed him.

I think you’ll find his insights to be interesting and thought-provoking.

He’s clearly someone who has thought about this topic a lot, and he has a lot to share

that listeners will find valuable and fun.

Before we dive in, I would really appreciate it if you would take two seconds to follow,

rate, or subscribe on your platform of choice.

And remember, DAM Right, because it’s too important to get wrong.

Aaron Edell, welcome to the DAM Right podcast.

Great to have you here.

Aaron Edell: 01:38

It’s an honor.

Chris Lacinak: 01:40

Thank you for having me. I’m very excited to talk to you today for a number of reasons.

One, you’ve recently announced a really exciting development at Wasabi. Can’t wait to talk about that.

But also, our career paths have paralleled and intersected in kind of strange ways over

the past couple decades.

We both have a career start and an intersection around a guy by the name of Jim Lindner, who

was the founder of Vidipax, a place that I worked for a number of years before I started

AVP, and who was also the founder of Samba, where you kind of, I won’t say you started

there, you had a career before that, but that’s where our intersection started.

But I’d love for you to tell me a bit about your history and your path that brought you

to where you are today.

Aaron Edell: 02:34

Yeah, definitely.

The other funny thing about Jim is that he is a fellow tall person. So folks who are listening to this can’t tell, but I’m six foot six, and I believe Jim is

also six six or maybe six seven.

So when you get to that height, there’s a little Wi-Fi that goes on between people of

similar height that you just make a little connection.

You kind of look at each other and go, “I know your pain.

I know your back hurts.”

So my whole life growing up, ever since really I was five years old, I loved video, recording,

shooting movies, filming things.

I eventually went to college for it.

I did it a lot in high school.

And this is back in the early 90s when video editing was hard.

And the kid in high school who knew how to do it and had the Mac who could do it was

kind of the only person able to actually create content.

So I was rarefied, I guess, in that sense.

So I would go to film festivals and all sorts, and it was just great time.

And I was never very good at it.

I just really loved it.

And when you love something, especially when you’re young, you learn all of the things

you need to know to accomplish that.

So I learned a lot about digital video just because I had to figure out how to get my

stupid Mac to record and transcode.

And then I got introduced to nonlinear editing very early on and learning that.

So when I went to college, I went there for film and video, really.

That was what I thought I wanted to be when I grew up was a filmmaker.

My father was talent for KGO television and ABC News for a long time.

So I had some familial– and my mother was the executive producer of his radio show.

So I had a lot of familial, sort of, media and entertainment world around and was very

supported in that way, I suppose.

By the time I got– so I went to college, and I loved my college.

Hampshire College is a fantastic institution.

It has no tests, no grades.

It has a– you design your own education, which is not something I was prepared for,

by the way, when I went there.

I’m so thrilled I went there because all of my entrepreneurial success is because of what

I learned there.

But at the time, I had no appreciation for that.

And I just thought, well, this is strange.

I’m here for film and video, and they’re like, here’s a camera.

Here’s a recording button.

And I thought, mm, this is an expensive private college in Massachusetts and probably need

to make it a little bit harder.

So my father is a physician, so I thought pre-med.

And I did it.

I went full on pre-med.

I was going to be a doctor.

I was going to apply to medical school.

But I was also working on documentaries and producing stuff and acting in other people’s

films and things like that.

So I still– that love, that passion never went away.

I was just kind of being creative about how to do it.

And my thesis project ended up being a documentary about a medical subject, which was kind of

perfect.

Because at the end of the day, my father, he’s a physician, but he’s actually a medical reporter.

And that’s a whole separate field that fascinated me.

So when I graduated, I was like, OK.

I went and actually got a job producing and editing a show for PBS, which was super cool

in New York City.

And that was around: 2000

I was doing it for a couple of years.

And we were– it was a PBS show, so we were very reliant on donations and whatnot.

And: 2008

It dried up.

We ran out of money.

And I was looking for a job.

And I worked on a couple of movies that were being shot in the city.

And I found this job at this weird company called SAMMA Systems on 10th Avenue and 33rd

Street or something that was Jim Lindner’s company.

That came to learn later.

But they were making these robotic systems that would migrate videocassette tapes to

a digital format.

So think of a bunch of tape decks on top of each other with a gripper going up and down

and pulling videotapes out of a library, putting them in, waiting for them to be digitized,

taking them out, cleaning them– not in that order, but essentially that way.

And I was just fascinated.

I mean, it was so cool.

Building robots.

Chris Lacinak: 07:02

Yeah.

Aaron Edell: 07:03

You know, video.

It was everything I loved kind of in one. And the rest is just really history from there.

Chris Lacinak: 07:09

Yeah.

So we have another intersection that I didn’t know about, which was Hampshire College, although I was denied by Hampshire College.

So you definitely one-upped me on that.

Which I taught at NYU in the MIAP program, and Bill Brand also taught there, also taught

at Hampshire College.

And I told him that I was denied by Hampshire College.

And he said, I didn’t know they denied people from Hampshire College.

Aaron Edell: 07:30

Oh, that makes it worse.

Chris Lacinak: 07:32

Anyway, all things happen for a reason.

It was all good. But that’s very cool.

That is a great school.

And what a fascinating history there.

So it’s not– I mean, I still think there’s– let’s connect the dots between working for

a company that was doing mass digitization of audiovisual and where you are today at

Wasabi.

Like, that is not necessarily easy to fill in that gap.

So tell us a little bit about how that happened.

Aaron Edell: 08:00

Yes.

Well, as my father likes to say, you know, life is simply a river. You just jump in and kind of flow down and you end up where you end up.

I don’t think I could have engineered or controlled this.

p– you know, SAMMA, this was: 2008

If I could jump back, you know, and say to myself back then, this is where you’re going

to end up, I would just been like, how?

How do you do that?

How is that possible?

So this is what happened.

I mean, I– you know, SAMMA was very quickly acquired by a company called Front Porch Digital

in: 2000

Very close to: 2009

And Front Porch Digital, you know, created these products that were– the core product

was called DIVA Archive, which still exists today, although it’s owned by Telestream.

But essentially, it is– you know, you’ve got your LTO tape robot and you’ve got your

disk storage and you have– you’re a broadcaster.

And you need some system to keep track of where all of these files and digital assets

live and exist.

And you’ve got to build in rules.

Like, take it off spinning disk if it’s old.

Make sure that there’s always two or three LTO tape backups.

You know, transcode a copy for my man over here.

Automation wants some video clip for the news segment.

You know, pull it off tape and put it here.

All of that kind of stuff was the DIVA Archive software.

And I’m oversimplifying.

But through that process, you know, I was– I joined as the– I was kind of bottom of

the rung, like, support engineer.

And I had delivered some SAMMA systems, you know, installed some and did a little product

managing just because we were– you know, we needed it.

We were only eight people.

And I was probably the most knowledgeable of the system other than one or two people

at the time.

And so by the time I got to Front Porch Digital, you know, I was doing demos and I was– I

was architecting solutions for customers.

So I was promoted to a solutions architect.

And that’s kind of where I learned, you know, business, just like generic business stuff,

emails, quotes.

I learned about the tech industry and media and entertainment industry in particular and

how, you know, how sales works in those industries and how it doesn’t work sometimes.

And all of the products that are– that are involved.

So I was kind of, you know, getting a real good crash course of just how media and entertainment

works from a tech perspective and how to be a vendor in the space.

I did a brief stint at New Line.

For those of you who don’t know New Line, I don’t think it exists anymore, but it was

a company based in Long Island that kind of pioneered some of the like set-top box digital

video fast channel stuff.

And then– but I was more or less at Front Porch for about seven years.

And then Front Porch was acquired by Oracle.

And working at Oracle was a very different experience.

You know, they are a very, very large company and they have a lot of products.

And I don’t know, I just– it just didn’t feel like I could do my scrappy startup thing,

which I had kind of spent the last 10 years honing.

So that is– so, you know, that is kind of at the point where I– a sales guy that I

had worked with at Front Porch named Tim Stockhaus went off to California to start this company

called GrayMeta based on this idea that we were all kind of floating around, which is,

man, metadata is a real problem in the industry right now, especially as it relates to archives

and finding things.

So GrayMeta was founded on that idea.

When I joined there, I was the first or second employee.

So it was– we were building it from scratch.

And I mean building everything, not just the product or the technology, but the sales motions

that go to market.

And that’s where I learned all that stuff.

I quit GrayMeta about two years in to go start my own startup because I just wanted to do

it.

I wanted to be a founder.

I wanted to know what that was like.

And I, at that point, had learned a lot about machine learning and how it applies to the

media and entertainment industry, specifically around things like transcription and AI tags.

And a couple of my coworkers at GrayMeta had this really great idea that let’s build our

own machine learning models and make them Docker containers that have their own API

built in and their own interface and just make them run anywhere, run on-prem, run in

the cloud, wherever you want.

Because it solved a lot of the problems at the time.

So we jumped ship.

We built the company.

It exploded.

I was the CEO.

My founders were the technical leaders.

And between the three of us, man, we were doing everything– sales, marketing, building,

tech support, all of it.

And gosh, what a learning experience.

Also as a founder and CEO, you’re raising money.

You’ve got to figure out how the IRS works.

You need to figure out how to incorporate stuff.

So a whole other learning experience for me.

a company called Veritone in: 2019 

Changed our lives.

I mean, we went through an acquisition.

We walked away with a lot of money.

And it was a whole new world.

Things open up, I guess, when that happens to you in business.

And I actually got recruited to join AWS.

And the funny thing is that it had nothing to do with media and entertainment or AI at

all.

AWS said, hey, you have a lot of experience taking situations where there’s a lot of data

and simplifying it for people or building products to simplify it for people and make

it more consumable and understandable.

AWS has that problem with their cost and usage data.

Chris Lacinak: 13:47

Oh, interesting.

Aaron Edell: 13:49

Yeah. And you get a, especially if you use a lot of the cloud and you’re a big company, you

get a bill. It’s not really a bill.

You get like this data dump that’s not human readable.

It’s billions of lines long, has hundreds of columns.

You can’t even open it in Excel.

It’s like, how do I use this?

So AWS was like, go figure this out, man.

So I mean, gosh, it was such a great experience.

We built a whole business based on this idea.

We built a product.

We built a go to market function.

We changed how AWS and actually I think how the world consumes cloud spending.

I think we had that big of an impact, not to toot our own horn, but it was for me, for

my career and my learning as a human, wow.

Like seeing how you can impact the whole world.

Chris Lacinak: 14:39

Yeah.

Well, as a consumer of AWS web services, I’ll say thanks because the billing definitely improved dramatically over the past several years.

So I know exactly what you mean.

And I see the manifestation of your work there.

I didn’t realize though that that’s what you’re doing at AWS.

I did always have in my mind that it was on the AI front.

So that’s really interesting that you kind of left that.

So in some ways, your role now is kind of a combination of the two in the sense that

Wasabi is a competitor to AWS, but you are very much in the AI space.

So tell us about what you’re doing at Wasabi now.

Aaron Edell: 15:17

Yeah, absolutely.

Well, you know, it was your point is really spot on because one of the biggest problems, I think for customers of the cloud is that, and I learned this thoroughly, is that it’s

not forecastable and it’s really hard to actually figure out what you’re spending money on.

And it’s also can be expensive if you do it wrong.

There really is a right way to do cloud in a wrong way.

And it’s not always obvious how to navigate that.

So when I first came up, so, you know, the board of GrayMeta called me while I was AWS,

you know, kind of chugging along and said, “Hey, Aaron, why don’t you come and be CEO?”

And I thought, you know what, that’s scary.

But it’s also like, it’s perfect because the Graymeta story, I feel like we never got to

finish telling it.

I left, you know, before we got to finish telling it.

And so I came back and I said, “Guys, I’ve now had the experience of creating our own

machine learning models and running a machine learning company, like one that actually makes

AI and solves problems.

Let’s do that.”

So that’s when I met Wasabi, was very shortly after I came back.

And you are totally right, because when I met Wasabi, it was like a door opening with

all this, you know, heavenly light coming through in terms of cloud FinOps.

Because Wasabi is, you know, cloud object storage, just like S3 or Microsoft Blob, that

is just $7.99 per terabyte and, sorry, $6.99 per terabyte and just totally predictable.

Like, you don’t get charged for API fees, you don’t get charged for egress, which is

where the kind of complexity comes in for other hyperscalers in terms of cost optimization

and understanding your cloud use and cloud spend.

That’s all the unpredictable stuff.

That’s what makes it not forecastable.

So the fact that, you know, Wasabi has just like a flat per terabyte per month pricing

and there’s just nothing else.

It’s just elegant and simple and beautiful and very compelling for the kind of experience

I had in the, and we call it the FinOps space or cloud FinOps space, where for three and

a half years, all I heard were problems about that this solved, right?

So it just pinged in my brain immediately.

The connection with AI, you know, goes back even further in the sense that I had always

advocated for, I always believed fundamentally that the metadata for an object and the object

itself should be as closely held together as possible.

Because when you start separating them and they’re serviced by different vendors or whatever,

that’s where the problems can seep in.

And one of the best analogies for this that I can think of is, you know, our Wasabi CEO,

Dave Friend, I love how he put it because he always refers to, you know, a library needs

a card catalog, right?

You go into the library and the card catalog is in the library.

You don’t go across the street to a different building for the card catalog, right?

It’s the same concept.

I mean, it’s obviously, you know, the physical world versus the virtual computer world, but

similar concept in the sense that, you know, the metadata that describes your content,

it should be as close to the content as possible because if it’s not, you know, you are at

risk of losing data at the end of the day.

I mean, I’ve talked to so many customers that have these massive libraries, sometimes they’re

LTO libraries, sometimes there are other kinds of libraries where they’ve lost the database,

right?

And, you know, in LTO, like you need a database.

You need to know what objects are written on what tape.

It’s gone.

I mean, what do you do, right?

You’re in such a bad, it’s such a bad spot to be in.

So hopefully we’re addressing that.

Chris Lacinak: 19:18

Yeah.

So that’s, I remember reading something on your website or maybe a spec sheet or something for air, which said object storage without a catalog is like the internet without a search

engine or something.

So, and to take that, to tie that to your other analogy, it’s like a library without

a card catalog, right?

You walk in, you just have to start pulling books off the shelves and seeing what you

find.

Although there, we have a lot of text-based information.

When you pull a tape out of a box or a file off of a server, there’s a lot more research

to do than there is maybe even with a book.

So yeah.

Aaron Edell: 19:55

Yes.

Chris Lacinak: 19:56

So tell me, what does AiR stand for?

It’s a capital A lowercase i capital R. Tell us about that. What’s that mean?

Aaron Edell: 20:05

So I believe it stands for AI recognition.

Chris Lacinak: 20:08

Okay.

Aaron Edell: 20:09

And so the idea is that, so the product wasabi AiR is this new product and it’s, you know,

the kind of combination of the acquits. So I guess we skipped the important part, which is that wasabi acquired the Curio product

and some of the people, including myself came over and the Curio product really was this

platform.

We called it a data platform, if you will, that when you pointed at video files and libraries

and archives, it literally, it would do the job of opening up each file, like you just

said and watch essentially watching it, you know, logging, you know, taking it, making

a transcript of all the speech, looking at OCR information.

So, you know, recognizing text on screen, recording that down, pull, you know, pulling

down faces, object recognition, basically creating a kind of rich metadata entry for

each file.

So this is where I think the, the, the kind of marriage between that technology and Wasabi

comes in because you’re, we now have a way of essentially with wasabi AiR it’s, you know,

it’s your standard object storage bucket.

Now you can just say anything that’s in that bucket.

I want it, I want a metadata index for that.

We’ll just do automatically with machine learning and you have access to that and you can search

and you can see the metadata along a timeline, which is really kind of turning out to be

quite unique.

I’m surprised that I don’t see that at a lot of other places in specifically seeing the

metadata along the timeline.

And that’s important because the whole point, it’s not just search, it’s not just, I want

to find assets where there’s a guy wearing a green shirt with the Wasabi logo.

I want to know where in that asset those things appear because I’m an editor and I need to

jump to those moments quickly.

Chris Lacinak: 21:54

Right, right, right.

Aaron Edell: 21:56

So that, that’s, that’s what we’re doing at, at, at wasabi with wasabi AIR.

And that’s, that’s why AiR stands for recognition, AI recognition, because you know, we’re essentially running AI against and recognizing objects, logos, faces, people, sounds for all your

assets.

So I want to dive into that, but before we do that, on the acquisition front, did Wasabi acquire

a product from GrayMeta or did wasabi acquire GrayMeta?

Wasabi acquired the product, the Curio product.

So GrayMeta still exists.

In fact, it’s really quite, is thriving with the Iris product and the SAMMA product, which

we talked about SAMMA.

That was the other piece I skipped over that too.

When I, when they called me and said, come be CEO of GrayMeta, it really made sense because

SAMMA was part of that story.

And that, that was like a connection to my first job in tech, which was wonderful because

I love, I love the SAMMA product.

I mean, we were, we were preserving the world’s history, you know, the, the National Archives

and Library, the Library, US Library of Congress, the Shoah foundation, the, you know, criminal

tribunal in the Rwandan genocide from the UN, like just history.

So anyway, I digress.

Chris Lacinak: 23:07

Well, no, I mean, actually the last step, as we sit here today and talk, the last episode

that aired was with the video of Fortunoff, the Fortunoff Video Archive for Holocaust Testimonies, which was, I think one of the first, if not the first SAMMA users.

So that, that definitely ties in.

s that around, I think it was: 2015 

maybe: 2016 

I remember wandering around the NAB floor and, and for the past several months had been

having conversations with Indiana university about this concept of a project around, you

know, this, this, they, they had just digitized or actually were in the process of digitizing

hundreds of thousands of hours of content, video, film, audio.

And they had the problem that they had to figure out metadata for it.

You know, they had some metadata in some cases, in other cases, they didn’t have any, in other

cases it wasn’t dependable.

So we, we were working on a project that was how does Indiana university and others tackle

the challenge of the generation of massive amounts of metadata that is meaningful.

And so we, that, that was the spawning of this project, which became known as AMP.

And by the time this episode airs, we will have aired an episode about AMP, but I was

wandering around the NAB floor.

I come across GrayMeta.

As I remember, it was in like the backup against the wall.

And and I’m like, Oh my God, this is the thing we’ve been talking about.

Like it was kind of like this amazing realization that you know, other folks were doing great

work on that front as well.

I think at the same time there was maybe Perfect Memory.

I mean, they’re, they’re one of the ones who I see doing metadata on the timeline and in

a kind of a similar way that you’re talking about, but but yeah, there weren’t, there

weren’t a lot of folks that were tackling that issue.

So it’s really cool one to have seen the evolution.

Do I have that timeline right?

Was it about like: 2015

Was that you had a product at that point?

I remember seeing it.

So like you had been working.

Aaron Edell: 25:08

Yeah, so I, so we, I joined, I was, like I said, the second employee at GrayMeta, which

would have been August of: 2015

Right.

It must have been.

Chris Lacinak: 25:23

Yep.

Aaron Edell: 25:24

Yes.

So we, we did have a big booth and we had a product, but it’s possible. I can’t remember exactly when it is we introduced machine learning for the tagging is possible.

It was by then.

Yeah.

But it wasn’t right away that originally we were just scraping exif and header data from

files and, and sort of putting a, putting that in its own database, which yeah, it’s

cool.

It’s useful.

But when, when machine learning came out, holy cow, I mean just speech to text alone.

Yeah.

Think of the searchability.

Yeah.

s was definitely a problem in: 2016 

so for many years was that your only option was to use the machine learning as a service

capabilities from the hyperscalers and they were great, but they were very expensive.

Chris Lacinak: 26:13

Yeah.

Aaron Edell: 26:14

And talk about like cost optimization.

You know, we would even as testers, we would get bills from, from these cloud providers that, that shocked us after running it, running the machine learning.

So we, it’s why we started Machine Box was because it just, we just didn’t think it had

to be that, that, that that was the only way to do it.

And, and it was a problem.

Like we were having trouble getting customers because it was just too expensive.

That’s all been solved now.

But, but that’s why I think this is why it’s interesting because the, the, it’s really

good validation that you guys, that other people had come up with the same idea.

That to me is a great sign.

Whenever I see that when independently different organizations and different people kind of

come to the same conclusion that, yeah, this is a problem.

We can solve it this way.

But I think it’s taken this long to do it in a way that’s affordable, honestly, and

secure.

And also the accuracy has really improved since those early days.

Chris Lacinak: 27:15

Yeah.

Aaron Edell: 27:16

It’s gotten to the point where it’s like, actually this, I can use this.

This is a pretty, the transcripts in particular are sometimes 90 to 99 to a hundred percent accurate even with weird accents and in different languages and all sorts.

Chris Lacinak: 27:29

Yeah.

I agree. It’s, it’s, it’s, it’s come a long way to where it’s, it’s production ready in many

ways.

Let me ask you though, from a different angle, from the, from the customer angle, do you,

what are your thoughts on whether consumers are ready to put this level of sophistication

to use?

What do you see out there?

Do you see wide adoption?

Are you struggling with that?

What’s that look like?

Aaron Edell: 27:54

So do you mean, you mean from the perspective of like, Hey, I’ve got a Dropbox account or something and I want to, I want to process it with AI? 

Chris Lacinak: 28:01

Well, I think there’s, I think about it in a few ways.

One is, are people prepared? And here let’s think about logistics and technology.

They have their files in a given place.

They know what they know, what they want to do.

They can provide access, they can do all those things.

But the other is kind of policy wise, leveraging the outputs of, of, of something like Wasabi

AiR to be able to really put it to use in service of their mission and providing access,

preservation, whatever those goals are.

Do you, I guess I’m wanting readiness on both those fronts.

Do you, do you see that as a challenge or do you find people are diving in whole hog

here?

What do you think?

Aaron Edell: 28:40

I think, I think people are diving in.

I think we’ve really reached the point now where I do think it’s kind of, it’s a combination of the accuracy and the sort of cost to do it.

Because if it’s not very accurate and very expensive, that’s a problem.

If it’s very accurate and very expensive, it’s still a problem.

But but we’re at a point now where we can do it inexpensively and accurately.

And so I’ll mention that even just today, which, which, you know, by the time folks

listen to this, it’ll probably be a few weeks in the past now or so.

But Fortune magazine published a post about Wasabi AiR and the Liverpool Football Club.

And they, what I, what I love is that they make it very clear, right?

Their use case, which is we want our, the fans of the football club to be able to go

onto an app and just watch highlights of, you know, Mohamed Salah crushing Man U, right?

Manchester United.

And just get it like a quick 30 second compilation of like all the goals or whatever, you know,

just just fan engagement.

And in order to accomplish that, you know, Liverpool has unbelievable amounts of video

content from every game from multiple cameras.

They’re, you know, they’re, I think people imagine that there’s there’s like a whole

bank of editors sitting around with nothing better to do.

It’s not really true.

They don’t, they don’t have that many editors.

And these editors have to, you know, create content from all of this library and archive

constantly and based basically Wasabi AiR makes them do it so much faster that they

can actually have an abundance of content ready for their app, which helps with increases

fan engagement.

And it’s that simple for them.

And they like the quote in the article from Drew Crisp, who is their senior vice president

of their digital world, says that that’s how they think about applying AI.

You know, we want to solve this use case.

We want to be able to create this 30 second compilation of all these goals.

Maybe it’s against a specific team or whatever the context is.

But we can’t sit around for hours and hours and hours watching every single second and

maybe manually logging things or tagging things or, you know, it’s always like, it’s always

a, it always happens after the fact, right?

You’ve recorded it all.

Okay, now it’s on a, it’s safe on, it’s on a disk.

I’ve got all my footage.

And then maybe you, you know, you in the file name, you put the team you played, but that’s

not enough metadata.

So, yeah, so I think they are ready.

I think, you know, it’s, it’s, um, people have to think about it the right way.

You know, this is a productivity boost.

This is a time-saving boost.

This is a, what hidden gems do I have in my archive boost?

You know, that latter, that latter use case, by the way, is, is really spectacular, but

very hard to put a number to and hard to measure.

You know, how much money do I make from the hidden gems?

The things that I didn’t even know I had in the first place.

Chris Lacinak: 31:57

And I, and sports organizations are interesting.

They’ve always kind of been at the leading edge, I think when it comes to, um, creation and utilization of metadata in service of analytics, statistics, fan experience.

I mean, we think about Major League Baseball was always doing great stuff.

NBA has done some great stuff.

I mean, it’s, and, and they have something going for them, which is a certain amount

of consistency, right?

There’s a structure to the game that allows there’s, there’s known names and entities

and things.

So, um, so that does make a lot of sense.

And it seems like it’s just ripe, uh, for, for really making the most of something like

Wasabi AiR.

I can just see that being a huge benefit to, to organizations like that.

Um, are you seeing, can you give us some examples?

Are there other, um, maybe non-sports organizations that are, that use cases that are using Wasabi

AiR?

Aaron Edell: 32:53

Yeah, definitely.

Um, I’ll give you one more sports one first though, because there there’s, you know, the, the use case I gave you is, is about creating content and marketing content for channels

and for consumption of consumers.

But they also are, you know, especially teams, individual teams are very brand heavy in the

sense that they, you know, they seek sponsorship for logo placement in the field or the stadium

or whatever.

And AiR is used for, by sports teams to look at that data and basically roll up, hey, the

Wasabi logo appeared in 7% of this game and the Nike logo appeared in 4% of this game.

And then you can go to Nike and say, Hey, do you want to be 7%?

You should buy this logo stanchion or whatever.

So really interesting use cases there, but non-sports use cases.

So one of my all time favorites is a, uh, a company called, uh, Video Fashion and Video

Fashion has a very large library.

I think it’s on the, to the tune of 30,000 hours of video footage of the fashion industry

going back as long as video can go back.

And they, um, and, and a lot of this was on videotape and needed to be digitized.

And I think they still have a lot that still needs to be digitized, but they used Wasabi

AiR back when it was called Curio, um, basically to kind of, you know, auto tag and catalog

these things so that when they get a request for, and they licensed this footage, right?

So this is how they make money.

This is how they monetize it.

This is why I like this use case because it’s a very clear cut monetization use case where

they sell the, you know, they licensed this footage per, I want to say per second probably.

And they, and so Apple TV Plus came to them one day as just an example and said, Hey,

we’re making a documentary.

It’s called Supermodels.

Do you have any footage of Naomi Campbell in the nineties?

It took them like five seconds, right?

To bust out every single piece of content they have where not only does Naomi Campbell

appear, but her name is written across the street.

Somebody talks about her, right?

So it, it’s literally like a couple seconds.

Yeah.

And then they just, they license it, right?

So they, they get all this revenue and have very little cost associated with servicing

that revenue.

And that’s exactly the kind of thing we want Wasabi AiR to empower.

You know, it’s time is money, my friend.

Yeah.

We’re saving time.

Chris Lacinak: 35:21

I love, one of the things I really like about Wasabi AiR is that it allows you to do sophisticated

search where you can say, I want to see Naomi Campbell. I want it in this geographic location.

I want it at this facility and wearing this color of clothing or something, right?

Like you can put together these really sophisticated searches and come up with the results that

match that, which I think is just fantastic.

I think that is, that is the realization of what the ideal vision is for being able to

search through audio visual content in the same way that we search through Word documents

and PDFs today.

I mean, that’s, that’s, that’s fantastic.

I’d love to dig into like, let’s dig, let’s make this a little bit more concrete for people.

We haven’t really talked about exactly what it is.

We’ve got this high level description.

But let’s jump in a little bit more.

So, so folks that are going to use Wasabi AiR would be clients that store their assets

in Wasabi, in Wasabi storage.

Is that a true statement?

Aaron Edell: 36:15

Yes, they, they can be existing customers or, you know, new customers. But yes, you need to, you need to put your stuff in Wasabi storage. 

Chris Lacinak: 36:23

You’ve got your assets in Wasabi storage.

How do you turn Wasabi AirRon? Is it something that’s in the admin panel?

How does that work?

Aaron Edell: 36:31

Not yet.

I mean, that is, that’s where we’re working towards. Right now, you reach out to us, you know, reach out to your sales representative or,

you know, honestly, on our website, I think we’ve got a submission form, you say, I’m

interested, this is how much content I have.

And you don’t have to be a Wasabi customer when you reach out, right?

Like, we’ll help you sort that, sort that out.

But essentially, when we will, we’ll just, we’ll create an instance for you of Wasabi

AiR.

And when we do that, we’ll attach your buckets from your Wasabi account, and it’ll start

processing and basically, you’ll get an email or, you know, probably an email with a URL

and credentials to log in.

And when you click on that URL and log in, you’ll have a user interface that looks a

lot like Google, right?

It’s, it’s, there’s, you know, some buttons and things on the side, but essentially, right

in the center is just a search bar.

And we want it to be intuitive, of course, obviously happy to answer questions from folks,

but you should be able to just start searching, you know, we’ll be processing the background

and maybe you want to wait for it to complete processing, it’s up to you, but you can just

start searching, and you’ll get results.

And those results will sort of tell you, you know, some some basic metadata about each

one, there’ll be a little thumbnail.

And then let’s say you search for the word Wasabi.

And maybe you specified just logos.

I just want where the logo is a Wasabi, not the word or somebody saying Wasabi.

When you get the search results, let’s say you click on the first one, you’ll have a

little preview window and you can play the asset if it’s a video or audio file, right?

We have a nice little, you know, proxy in the browser.

And then you’re going to see all this metadata that’s all time line, timecode accurate along

the side.

And you can kind of toggle between looking at the speech to text or looking at the object

tags, and then on the bottom will be a timeline kind of like a nonlinear editor, be this long

timeline and your search term Wasabi for the logo, you’ll see all these little like kind

of tick marks where it found that logo.

So you can just click a button and jump right to that moment.

And what I like about that is so let’s say, let’s say in the use case, you’re trying to

you’re trying to quickly scan through some titles for bad words, or for nudity or violence

or something like that.

Those, you know, those things will show up and you can just in five seconds, you can

just, you know, make go through them and make sure they’re either okay or not, right?

Like sometimes, for example, it’ll, you know, it’ll give you a false positive.

That’s just what happens with machine learning.

But it doesn’t take you very long.

In fact, it takes you almost no time at all to just clear it and just, you know, go through

and then if you want, you can even edit it and just remove it or add a tag or something.

So let’s so hopefully that gives a good picture.

Aaron Edell: 39:19

Yeah, so well, and I’ll ask this question, because wasabi is so transparent about pricing.

You’ve mentioned $6.99 per terabyte. Is there is there transparency on that level yet with AiR?

Or is this still something that’s in motion?

Or?

Aaron Edell: 39:34

Yeah, we’re still we’re still working on it.

But we do have a kind of a we, we came out with a pricing for NAB, we’re calling it the NAB show special.

So you know, get it while it’s hot, I guess, because we probably will have to change it.

But it’s just $12.99 a terabyte per month.

So think of it almost like a different tier of storage, although, you know, it’s, it’s

the same storage, it’s just that you have now all this indexed metadata.

Chris Lacinak: 39:58

And is that $12.99 per month on top of the $6.99 per month? Or is that inclusive of so $12.99 total? 

Aaron Edell: 40:05

Exactly.

Yeah, which is still cheaper than I think the 20 or 30 bucks per terabyte per month for just the storage for some of the hyperscalers.

So you know, even even if you didn’t use air, and you were just paying for the storage,

it’s still a lot, a lot less expensive.

And there’s no egress and no API fees and all that.

Chris Lacinak: 40:23

Yeah.

So in the I mentioned the project that I was working on before called AMP, we, we call we came up with the term MGM, which stands for metadata generation mechanisms.

And this is to say speech to text or object recognition or facial recognition, as would

all be things we called MGMs, right?

Do you have a term for those?

What do you call those

so I can refer to them the way you do?

So this is so funny you ask, because when we we when we started gray meta, we had so

much fun trying to come up with that term.

And the original product was called haystack.

Because we thought you’re going to find the needles and I like that.

Right?

Chris Lacinak: 41:01

I like that.

Aaron Edell: 41:02

Yes.

So so how do you find a needle in a haystack? Bring a big old magnet.

So we called those things magnets at first.

You’d have a magnet for speech to text or whatever.

I think I think they were still called magnets by the time I left.

When I came back, we were calling them harvesters, which are maybe gosh, extractors, maybe extractors.

Okay, so but but since we joined Wasabi, I think we’ve just been referring to them as

models honestly, models, not all of them are machine learning models, but you know, okay,

Chris Lacinak: 41:36

well, I just just so we have a term for this discussion.

And I’ll use the term models then to talk about that. So so can you tell us what models you have built into air right now?

Aaron Edell: 41:46

Yes.

So right now, we have speech to text, which is outstanding and understands I think, 50 languages and will translate it even to English, as well as do a transcription in the native

language.

We have an audio classification engine, which, you know, basically tries to tell you what

sounds it hears, you know, coughing, screaming, gunshot, blah, blah, blah.

We have a logo and brand detection system, which we just trained ourselves from scratch

and is very good, actually, I’m really surprised because that that’s that was when we were

doing this before, it was a really hard problem to solve.

It still is, but we actually got it working.

Then we have an object recognition model, which will essentially try to tag things that it

sees lamp post shirt, beard, that kind of thing.

And then we’ve got OCR optical character recognition.

So words that appear on the screen get turned into metadata.

And then we’ve we’ve got we call it we call it technical cues.

So this is very specific to the M&E industry, but bars and tone, slate, titles, that sort

of thing.

And then faces and people.

So, you know, we will we will detect faces and then kind of kind of like how in iphoto

on your phone, like it’ll it’ll say, who’s this?

Right.

Here’s a bunch of photos of this person.

Who is this?

Same thing.

Right.

We group unknown faces together.

You can type in who they are.

And then going forward, you basically have names associated with with faces.

Right.

So it’s a very, very simple system.

Chris Lacinak: 43:31

And if I remember right from the demo, you can also upload images of individuals that

you know are going to be in your collection and and identify proactively. Right.

Like if for myself, if I could I could upload three photos of myself, say this is Chris

Lacinak and then it’ll use that.

You can do it ahead of time.

Aaron Edell: 43:52

Exactly.

Yeah. Yes.

So if you know the people ahead of time, you can do it, too, which is which is really useful.

Chris Lacinak: 43:57

I like that feature.

I mean, that’s another thing that is similar with AMP is just the concept of using non audiovisual materials in order to train models on to describe audiovisual objects.

So the OK, that’s great.

And do you do you are those are all of those models, things that Wasabi has built that

are owned by Wasabi?

Or are you connecting to other providers of AI services?

Aaron Edell: 44:24

We built we built all our own models, all homegrown.

This was this was my this was my big change when I came back to GrayMeta because I had experience doing it.

I knew it was possible and I didn’t think that relying on third party models was a good

idea.

I mean, obviously, for intellectual property reasons, but also it’s just really expensive

to do it that way.

We wanted to make it just basically I don’t want to I don’t want to say cheap, but we

wanted to make it economical for people.

Right.

Because that was a major barrier.

If you are, you know, a large library, you could have millions of hours of footage.

And if you’re paying the hyperscalers, which charge like 50 bucks an hour in some case.

I mean, what are you going to spend 120 million dollars on money on

AII tagging? Probably not.

So so we built all our own.

And the reason we were able to do that, and by the way, like, don’t think you can just

go on to Hugging Face and pull down a model off the shelf and just pop it into production.

I have seen that you can’t do that.

And the reason is because, you know, a lot of those models are trained on not media and

entertainment.

They’re trained on other world things and they don’t work.

They don’t their accuracy drops when you’re talking to people like L of C or you’re talking

to to, you know, you know, pick pick your pick your broadcaster, pick your network,

pick your pick your post house.

When you’re talking about media and entertainment content, they need to be trained for that.

And then you got to build in pipelines and we had to do all kinds of stuff to make it

efficient, because there’s a lot of really cool machine learning out there that’s very

advanced but very expensive and compute intensive to run.

And that’s also not going to work for customers.

They can’t spend 50 bucks an hour on their machine learning tagging.

It’s not it’s a no go.

So we’ve put we’ve put years of experience into our models and and also understanding

like what to expect on the other end.

I there’s a there’s a guy who works for me, Jesse Graham.

He’s been doing this for so long that you can give him any machine learning model now.

And he can just he he knows he knows the pieces of content that’s going to throw it for a

loop and he can see the results and he knows customers are going to either be OK with this

or not.

Chris Lacinak: 46:43

Yeah.

Aaron Edell: 46:44

And that that experience is so valuable to us because it gives it lets us quickly iterate.

It lets us go to market with with production models that actually work for customers. They’re not just cool demos.

You know, they’re not just kind of fluffy fun things.

They’re real.

They have real value.

And that’s why we spend so much time building our own models.

Chris Lacinak: 47:04

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by following me on LinkedIn at linkedin.com/in/clacinak.

And let me ask related to that, talking about training it based on media and entertainment

broadcast content.

How how have you found it to work or have you done testing on archival content stuff

that’s not production necessarily like production broadcast quality, always highly variable,

maybe lower quality audio and video stuff like how how how is it performing on that

sort of content?

Aaron Edell: 48:00

Surprisingly well, actually, I’ll give you an example.

So Steamboat Willie, which is now in the public domain, you know, practically an ancient piece of animated content featuring, I think, the original appearance of Mickey Mouse, although

I don’t think he was called Mickey Mouse back then.

Anyway, there there’s it correctly identifies the boat is a boat.

You know, it the object recognition, surprisingly, is able to tag things that are animated and

in black and white.

I have I have also seen it pick up logos that are on almost undetectable by human eyes.

So we had so much fun showing this off at NAB because I we Wasabi sponsored the Fenway

Bowl recently.

And so we had we had the Fenway Bowl.

We ran we ran it against wasabi air.

And there’s obviously a ton of logos everywhere.

And so there was this one logo golf, I think it was Gulf Oil or something like that.

And I would show it.

So I’d pull it up on the screen and I would click and jump to that moment.

And I would say, OK, everybody who’s watching me do this demo right now, tell me when you

see the Gulf Oil logo in the video.

And they’re like squinting and, you know, most people don’t see it.

But if you kind of expand it and zoom in, it’s just there, teeny tiny little thing in

the background.

So, yeah, I’ve I’ve been I’ve been really pleased with a lot of of where machine learning

has has how far it’s come in terms of the research that’s gone on behind it.

The you know, the the embeddings and weights that you that people are open sourcing and

making available.

It’s just extraordinary.

Chris Lacinak: 49:38

Yeah.

Let me dive into the weeds a little bit here about kind of the the the models and things. I’m curious, I mean, one of the things that we developed in AMP and I’m wondering, I know

that you had to have thought about this and I’m curious where you’ve arrived and what

you’re thinking about for future.

But is the concept of workflows.

It sounds it sounds like and correct me if I’m wrong once I’m done saying this, like

I have my I have my videos and my audio and things stored in in Wasabi.

I turn on Wasabi AiR and it runs these models.

It sounded like seven or eight-ish models, I think, in parallel.

But let’s say that I wanted to create a something that does speech to text and then runs it

through named entity recognition, sentiment analysis.

Right.

I take and I want to take outputs of one model, plug it into another model and create workflows

instead of just getting the output of a single model.

Where are you at?

Does that exist today?

Is that on the horizon?

What’s that like?

Aaron Edell: 50:44

Yeah.

So I’ve experimented with that in some way or another at actually several different companies. In fact, I think at Veritone, we even had like a workflow builder that you could do

where you could sort of drag nodes in and go output from this to there.

The state, I think the way that we’re thinking about it today is we just we don’t want you

to even have to do that.

So let’s pick apart why you’re doing that.

So named entity recognition based on speech to text.

It’s a really good example.

Like I want maybe I want to search by places.

So speech to text is particularly the one that we’ve developed is surprisingly good,

is shockingly good at proper nouns and proper names for things.

This is where speech to text in the past has always fell down.

But it’s just text.

So the way we think about it is instead of you having to come up with that use case for

that workflow, we’re just going to build that in.

So when you’re running product and you’re thinking about, “Okay, how do I solve these

problems?”

I like to I like and this is a great thing I learned from from working at Amazon is just

put yourself in the customer’s shoes, be customer obsessed.

Think about, okay, the editor is sitting down, they got to do their job.

They want to get shots of the Eiffel Tower or something or maybe just I don’t know, I’m

trying to think of a better example of that.

Because if you search for Eiffel Tower, you just show up.

But named entity recognitions like companies or something like that.

Maybe I’m looking for people.

Okay, I got it.

When people are talking about Wasabi the company and not wasabi the sushi sauce, right?

I want to differentiate.

So normally, if I search for the word Wasabi, obviously, all references will show up.

We are going to give you an experience where that is just seamless, right?

It’s a new option.

Just search for Wasabi the company or I’m doing named entity recognition on the speech

to text.

That’s how we might solve it in the back end.

We may solve it some other way.

There is a lot of the whole machine learning pipeline thing is what’s really evolving.

Like for example, our audio classification and speech to text are one multimodal model,

for example.

So there’s this kind of newer world of these end to end neural networks that are really

good at doing different things.

Instead of in the old way, which is what you described where we would kind of have the

output of one and go and make it be input in another, that kind of ends up being like

a Xerox of a Xerox of a Xerox sometimes.

So we’re building kind of more capabilities around combining these things into one neural

network so that A, it’s way more efficient and B, it’s more accurate.

So that you’re going to see from us in the coming months, a lot of innovations around

that and with the express goal of doing what you described, which is just better search,

better, more contextual, more accurate search.

Chris Lacinak: 53:56

Well, I have to hand it to you.

I mean, I think what you gain with sophistication, you kind of add a burden of complexity. And right now I’ve seen the demo of Wasabi AiR and it is elegant in its simplicity.

I can totally understand aiming for simplicity.

That’s going to be a better user experience.

So yeah, that’s interesting.

It’d be good to maybe, I don’t want to bore our listeners with that, maybe a sidebar sometime

offline we can talk about that.

And another question in the weeds here, I mean, one of the things that I’ve grappled

with or we grappled with in the AMP project that I’d love to know what you’re thinking

about or how you’re managing this, if you’re able to share is on the efficiency front of

processing efficiency, right?

The concept of running, for instance, speech to text where there’s nobody talking, it’s

music or BPM analysis on music where there’s somebody talking, right?

Facial recognition where there aren’t people.

You got the idea here, but trying to really only feed segments of relative things to models,

using your term in order to create more efficient and cost-effective processing.

Is that so negligible?

Is that so processor intensive that it doesn’t really pay off or is that an actual model

that I’m now using model in a different way that works?

Aaron Edell: 55:26

I know what you mean.

Yeah, I think it does add up. So in the true FinOps cost optimization fashion, once you take out the big things, you go after

the little things because they just add up, right?

If I can reduce some fee that’s one cent or something to half a cent, that in theory would

add up.

So it’s worth it to think about it.

We do have some of that.

So for example, you mentioned a really good example of that, which is don’t run speech

to text if there’s nobody talking.

So we actually have a separate model that we call, I think it’s called the voice activity

detector or something like that.

So this is what I mean.

It’s such a good example of what I was trying to convey, which is that you have to think

about these things when you’re doing this in production.

And these are the things that drive efficiency to make it actually viable for customers to

pay for and use.

So when we first started building our own speech to text, we just plopped it in, we

ran it and my goodness, it was so slow.

And the accuracy was great, but it just was not going to work.

Over time, we built the pipeline better.

We introduced VAD that greatly improved the accuracy of the timecode markers for the speech

to text, as well as improved the overall efficiency.

I mean, I don’t want to get in trouble for this, but I think we improved the efficiency

by a hundred times.

Think about that.

Chris Lacinak: 57:00

Yeah.

Aaron Edell: 57:01

That’s a huge, huge difference.

And that’s just basically trial by fire in some ways. I mean, I believe in iterative product design.

I don’t like to sit around for six months and try to build the perfect product.

I like to build little things and iterate quickly and learn.

And that was one of the first things that we learned when we first started doing speech

to text.

And we just iterated it and made it faster, faster, faster until we got to this super

efficient state.

So yeah, for an in the weeds question, that was a really poignant one because it is where

I think the value of AiR comes from and perhaps other systems that are trying to accomplish

the same thing is when you build your own machine learning, there’s a lot of things

you got to think about and it’s hard to know what they’re going to be up front.

And it’s taken us years to get it right.

Now, it doesn’t necessarily mean it’ll take everybody years.

You can always learn, but it’s a trial by fire.

Chris Lacinak: 57:56

It makes me think of Formula One racing with hundreds of little tweaks to these vehicles

to make things just get a 10th of a second faster or something. Right.

Let me jump over to the questions around ethics and AI.

And I’m going to break that into a couple categories to kind of go off of here.

I guess, you know, when it’s come up, typically there’s one around bias, how do the AI models

in this way perform across a variety of contexts?

Another is around intellectual property.

Like here we think of in Chat GPT now, I can buy the business license in which my content

that I’m feeding it is not going to train the model, right?

As opposed to the free or the cheap one where my data that I feed it goes to train the larger

model.

Can you talk about how you are thinking about and acting on those sorts of ethical questions

today?

Aaron Edell: 58:57

Absolutely.

You know, for me, machine learning is not a means to an end. So I kind of like to use the analogy that, you know, I don’t go around talking about

how Wasabi AiR is built on electricity, right?

Like that doesn’t make sense.

Electricity is a technology that we kind of take for granted.

Machine learning solves the real problem that I’m trying to solve, which is I don’t want

people to have to lose content in their archives.

I want people to be able to find stuff quickly and be able to get it out the door.

And I want editors to just have a wonderful life, not be miserable.

And so I think about machine learning in that sense.

I don’t think about it as a, hey, we’re going to try and scrape as much data and make the

best overall models and make money by selling machine learning, if that makes sense.

So I think your motivations for your ethical use of AI start there.

The bias thing is really interesting, and I have to hand it to, I mentioned my Machine

Box co-founders before, Mat Ryer and David Hernandez.

David Hernandez, brilliant computer scientist, he really taught me a lot.

And one of the things that he pointed out to me was, and this was back in, we were doing

Machine Box in, I don’t know,: 2017 

turn words into vectors.

And this is important because for the listeners who don’t know what that means, basically

take the word frog and take the word toad.

Now instinctually as humans, we know that those are a lot closer together in concept

than the word frog and the word curiosity.

So vectors attempt to kind of do the same thing.

We take basically every word, and this is, you have to picture a thousand dimensions,

right?

It’s not a three-dimensional thing, it’s like thousands of dimensions.

But basically in these thousands of dimensions, we can do math to figure out that the word

frog and the word toad are very close together.

And this helps us in search.

So if I search for toad, I get pictures of frogs because they’re very relevant.

Now those systems, a lot of these embedding and vectorization systems were trained, at

least back in the day, and I’m pretty sure this has been addressed, but they were trained

on news articles and written material from humanity ranging all the way back.

So what happened was that if you actually look at the distance between, for example,

the word doctor and the word man, much closer than doctor and woman, and the inverse was

true for nurse and man and nurse and woman.

Now that’s a bias.

That bias came from the training data, which is again, I think was a lot of news articles

written over the last 70 years or something like that.

So what you end up with is a machine learning system that’s just as biased as humans are

or have been in the past.

And they don’t necessarily reflect our inclusive nature and how we want our society to exist

where we don’t want that bias.

That’s not something we want in our machine learning because we’re using our machine learning

to solve problems in the real world and it doesn’t reflect the real world.

So I think about that a lot and I think about how can we improve our machine learning.

Now it’s the training data.

It’s not the machine learning models.

It’s the training data.

So we as humans have to go back and fix that in the training data and do our best to think

of those things ahead of time.

And there’s ways, there’s tools to process your training data in certain ways and look

at patterns and things like that.

And you can detect that kind of thing.

So I’m always thinking about that and I always want to make it better.

And it’s probably an ongoing challenge that’s never going to really end, but something that

we have to pay attention to.

Ethically, like any technology, any new technology, what I’m about to say could be applied to

nuclear physics.

It could be applied to electricity.

It could be applied to taking metal and making it sharper.

Don’t use it for bad things.

Your intentions, like I mentioned before, my intention is to make people’s lives at

their jobs, in particular media and entertainment editors and marketing people and these professionals,

I don’t want them to have to sit around trying to find stuff.

I want to make them immediately find the thing they’re looking for and deliver the content

and the value that they want.

That’s my purpose.

If your purpose is to go around electrocuting people or dropping nuclear bombs or stabbing

people, you’re going to use these technologies in the wrong way.

So I don’t mean to say that we all have to just be responsible for our own actions.

I think we do, but the rules that we come up with, scientists have rules around bioengineering,

for example.

There’s laws against you can’t patent certain molecules, you can’t patent DNA.

Those things are being challenged all the time.

But I do think that we can collectively as a society agree that we’re not going to use

AI for these purposes, even though some people will.

You can’t legislate bad guys out of existence.

They will be there and they will test it.

But I think the more educated we are about it, the more we can tackle it.

But I don’t think that means we have to stop using AI or ML or we can’t innovate and we

shouldn’t innovate and we shouldn’t see where this can go.

I think that’s equally as dangerous.

Chris Lacinak: 64:43

I’ve got a question that’s a little bit out there, but if you don’t have a response to

this, I don’t know anybody who does. So you’re the best person I can think of to ask this question.

And that is about the prospect of a future in which the machine learning models, and

here I’m not talking about models as in things that generate metadata, but the machine learning

model as in the thing that you train over time, are interoperable.

Is there a future in which I go to Wasabi as an organization, my data is there, I

spend years training it and cultivating that data, not just the data, not the output of

just the metadata, but let’s say the machine learning that we do over time and training

the models and giving it feedback and maybe triangulation of that data, that God forbid

Wasabi goes out of business in 20 years, that I could take that and transfer it to another

entity that has machine learning.

Is there a future in which such a thing exists or is that not even on the horizon?

Aaron Edell: 66:04

Well, today, I’m a very customer obsessed person.

I mentioned that already. And I think if I’m the customer, when I spend effort and time training a machine learning

model, let’s say in Wasabi AiR, which you can do, you can train it on people and soon

you’ll be able to train it on other things.

I’m putting my effort and my data into that.

I should own that.

And I believe in that.

So we segment that all off.

We don’t aggregate people’s data.

We don’t look at the training data and make our own models better.

You own it.

It’s your data.

If you trained it, it’s yours.

But I think that it would be hard, just the nature of the technology itself, it’s hard

to take all that training and shuffle it off somewhere else.

I mean, I guess in theory, there’s like embeddings and vectors and stuff like that and you could.

I think more likely over time, you won’t have to train it.

I think our models will get better at context.

They will be larger.

They’ll have more parameters.

But I also think that they’ll get more specific and I kind of like this agent approach that’s

kind of emerging where, let me put it this way.

I do not think that artificial general intelligence is anywhere near happening.

I mean, I think people will change their definition of what that means to kind of fit their predictions.

But I don’t think that we’re in danger of one very large AI model that just does everything

and takes over humanity and kills us all.

Or I don’t know, who knows, maybe they’ll do something wonderful, like help us explore

other planets, whatever.

I think what’s more likely is that we will get better at segmenting off specific tasks

and making machine learning models that are just very, very, very good at that and then

orchestrating that, which is kind of what Wasabi AiR does today.

But I don’t think the need for training it is interesting because if you asked me this

question back in: 2017 

machine learning, which is that your machine learning model should be trained on the data

that it’s expected to run against.

You should not be able to tell the difference.

And this was kind of at the time when synthetic training data was emerging and you can’t beat

a human curated, really, really clean, really good data set.

You can’t beat it.

And today I think that that might be changing a little bit and that the need to train models

to be more specific or to train it on your own data is not heading up.

I think it’s probably going down.

In fact, we already see some of it a little bit.

Like, you know, take, okay, great example, the Steamboat Willie example.

It used to be that you would have to train your object recognition system to recognize

animated objects as kind of custom objects.

We have been experimenting with some machine learning that we haven’t put into air yet,

but we might at some point where you don’t have to do that anymore.

In fact, it actually interprets your search in a different way.

So if I searched for, let me put it this way, like it would process a picture.

Let’s say it takes a picture of the two of us talking and I have a beard and you don’t

have a beard.

And I sent it to this system and processed it.

Instead of coming back with brown hair, beard, blue shirt, microphone, right, this whole

list of things, it just sits there.

Then you ask it, is there a microphone in this picture?

Yes, there is.

Here it is.

Is there, and this is what I like about it because the words that we use can be very

different.

So is there a mustache?

Yes, there’s a mustache.

And it draws a line just around this part of my beard.

Instead of saying the whole thing is a beard, right?

Or it’s using an LLM to interpret the question rather than trying to seek custom training.

And it has a fundamental deep understanding of the picture in a way that we don’t understand

as humans, right?

It’s broken it down into vectors and things that are just basically math.

And when you ask it, is there a green shirt here?

It interprets your question and goes, okay, this vector over here kind of looks like a

green shirt.

I’m going to say there’s a 60% chance that that’s what it is and draw a bounding box

around it and there you go.

I think that’s the future.

I think that’s where we’re going.

Machine learning models that are specific, but way more contextual and understand images

and video and data in ways that we can’t, but can be mapped to concepts that we as humans

think about.

Chris Lacinak: 71:22

And somewhat related, kind of pulling several of these strings together, like the question

around humans in the loop, like we’ve done a lot of work with the Library of Congress and Indiana University, that AMP Project kind of had at its core that humans in the loop

as far as these workflows go.

And some of that was quantitative.

It was about, for instance, taking the output in a given workflow, taking the output of

speech to text, reviewing it by a human, editing, correcting, and then feeding it back sort

of thing.

Some of it’s qualitative.

It’s about ethics.

There are some sensitive collections that need to be reviewed and make sure that they’re

described properly and accordingly and things.

And I guess I wonder, do you think about that in the work that you’re doing?

One, it sounds like some of what you just said makes it sound like the quantitative

aspect of that is becoming less and less important as things improve dramatically.

But I wonder, do you think about humans in the loop with regard to what Wasabi offers,

or do you think about that as something that’s up to the user post-Wasabi processing to manage

themselves?

Aaron Edell: 72:31

No, I think about it all the time.

In fact, one of the bigger initiatives that we have, and we are still working on it very much, is a frictionless human in the loop process with your data.

So in spite of what I just said, I still think that you need to be able to teach it things

based on your data and correct it, and it should learn.

We do that with faces today, for example.

That’s a really good example of this, but it’s solved.

Where we want to take it is some of the other things you mentioned.

So improving proper noun and proper name detection, improving the way it detects certain objects

and things in your data, because maybe you’re NASCAR or something, and you just have a very

specific content with objects that are, in the broader perspective, kind of strange,

but in your perspective are very set and usually the same or something like that.

You should be able to use your own data and say, “Yeah, that’s what this is.

This is a tire.

This is this car.”

And we actually do have it in the system.

We’ve just disabled it for now because I want to make it so seamless that you don’t even

really know what you’re … You don’t even really think about it as training machine

learning.

Just like … I really love the Apple Photos example.

They just do such a good job with faces.

I don’t know if you have an iPhone.

I’m sure Android does the same thing.

Just go in your photos and it’s like, “Hey, who is this guy?

Who is that?”

Brilliant.

It should be very similar.

“What is this?

I don’t know what this is.

Tell me what this is.”

So I think about that a lot.

I definitely see … There is just no better arbiter for accuracy in machine learning and

data sets than humans, ironically.

You have to, as a human, make some decisions.

For example, back in: 2016 

I bet I could train a classification engine to tell if a news article was fake news or

not fake news.”

ake news was a big problem in: 2016

I went about to try and train it.

Basically that meant creating a data set of fake news and not fake news.

I wrote a lengthy blog post about the details, so I won’t reiterate it here.

What I ended up figuring out was that, as a human, I have to decide what is fake news.

How do I … Is it satire?

Is it factually incorrect information?

There’s all these subcategories.

I just had to figure out where do I draw the line.

The machine learning ended up working best was when I drew the line in the data set had

bias.

What I was really doing was training a bias detection system.

So it was able to tell if this article was written in a biased way or an unbiased way

and rank it.

That journey for me was really telling about how data sets get made to train these machine

learning systems in the first place.

You really cannot mess up.

This is where the human in the loop problem or question can become a problem and you have

to think about.

If I am surfacing, “Hey, what is this logo?” and you get it wrong and the next guy gets

it right five times, you’ve caused a problem in your machine learning because you now have

a dirty data set.

So you need to think about that.

How do I keep it clean?

How do I check that this work that’s been done is actually accurate?

That’s part of the reason why we’re spending so much time thinking about it is we want

to get that experience right.

Chris Lacinak: 76:26

So that’s on the horizon, it sounds like.

That’s great. Look forward to seeing that.

And users of Wasabi AiR, you have, as we mentioned, a user interface within Wasabi’s GUI, but

is there APIs that can push this out to other systems?

If people generate the metadata in Wasabi AiR, can they push it to their DAM?

Aaron Edell: 76:49

Absolutely.

In fact, we’re in the talks with several MAM systems right now. I think that IBC, which is in September, will be able to announce some of them, but we want

people to do that.

The vision for Wasabi AiR and for Curio prior to the acquisition was always that this is

a sort of data platform with APIs.

In fact, our whole UI consumes our own APIs.

That was really important for us and that was a wise decision that was made before I

came back to GrayMeta because at the end of the day, you know this, in the DAM world,

in the MAM world in particular, man, you can go in a lot of directions with a MAM.

You can get bogged down in the tiny features and all of the requests that customers want.

And I think that’s why so many MAMs today are kind of like rubber band balls.

They have a lot of features and they’re all different and they all have different buttons

and they can be very confusing.

It’s really hard to keep something simple when you’re sort of serving all of those use

cases and trying to build a thousand features, one for each customer.

I don’t want to be in that business.

So I think we’ve got a great tool that gets you what you need off the ground right away.

Some customers have described it as a great C-level tool as well.

We just need some insight into this archive for our managers or for these certain groups

of people.

But the people who use MAMs and DAMs and really use them, they should have access to the metadata

too.

And so they will.

Chris Lacinak: 78:27

Yeah.

Well, let’s talk, I think what I see when I look at Wasabi AiR is a blurring of the lines between what has been storage and DAM and MAM, but also between storage and other

storage providers that offer AI and ML tools.

Right?

So I’d like to, let’s touch on each of those for a minute.

Wasabi AiR brings to the table something that is in many ways, not new, right?

Google Cloud and AWS, they have a suite of tools that you can use to process your materials,

but it is new that you turn on the switch and it does it automatically.

You don’t have to go deploy this tool and that tool and put together workflows and things

like that.

Is that the main difference between, is that how you would describe the difference between

what Wasabi is doing today and what AWS is doing today?

Aaron Edell: 79:17

Absolutely.

Yes. I mean, I feel like I don’t even need to continue talking, but I will, because I think you described

it pretty perfectly.

We want it to be very simple and elegant and we kind of want to redefine what object storage

is.

What is, especially cloud object storage, like what criteria defines cloud object storage?

And having a metadata and an index that’s searchable, I think is, we’re hoping is going

to be the new definition because it is really hard to solve this other ways.

I mean, there are other similar tools, but yeah, if you use the hyperscalers, first of

all, it’s an API call.

You still have to process your video, transcode it, and in some cases, chop it up, post each

of those pieces, or in other cases, you can send the whole file, but I think it depends,

to an API endpoint, get back that metadata and then what, right?

Like it’s a JSON file.

And then, so if you want to view this metadata on a timeline and make it searchable, there’s

a whole stack you need to build with open search or some kind of search index incorporated.

You need to build a UI.

You have to process and collate all that metadata.

You have to keep track of where it came from, especially if you’re chopping stuff up into

segments.

And yeah, you end up building a MAM.

Chris Lacinak: 80:40

It’s complicated.

Aaron Edell: 80:41

Yeah, it’s complicated.

Exactly. I do think that the value of just being able to just turn it on, like here’s my storage,

press a button, and now I’ve got this insight.

And if I want, I can hit the API, get the metadata into my existing MAM, but I also

have an interface, a search bar, a Google search bar into my archive just without having

to do anything.

I like that.

I like that solution.

Chris Lacinak: 81:07

Yeah.

It makes a lot of sense. And I suspect that there will be others that follow suit, I imagine.

Aaron Edell: 81:16

Probably.

Chris Lacinak: 81:17

So tell me about the blurring of the lines between the dams of the world and Wasabi,

because you’re now, there is, this creates an overlap of sorts. How are you thinking about that?

What do you think it means to the evolving landscape of digital asset management?

Aaron Edell: 81:33

Yes.

It’s definitely a heady topic. And I think that the MAM world has always been a world that both fascinates me and terrifies

me at the same time.

When we were at Front Porch Digital, for example, we integrated with all the MAMs that existed

at the time.

And I remember going to various customer sites and they would show me their MAMs and I was

just like, “Oh my God, this is so complicated.

I don’t know how do you use this?

There must be all kinds of training and everything.”

And they were very expensive.

Very, very, very, very, very, very expensive to implement.

We had our own, we built our own MAM light.

We always called it a MAM light called DIVA Director.

And this is, Diva Director is kind of where I think I get my idea of what a MAM should

be from, but it’s not.

MAMs have a purpose.

There’s a whole world of moving files around, keeping track of high res and low res and

edits and all that, that I am willfully ignoring at this point because that is important.

And it is complicated and there are wonderful MAM tools out there to solve all that.

But when I think about these customers that I spent so much time with, the Library and

Archives of Canada, the Library of the United States Congress, the Fortunoff Archive, the

USC Shoah Foundation, all of these archives have a kind of somewhat finite archive.

Now there’s stuff that’s new, that’s born digital, and maybe they have parts of what

they do that, if you think about like, I don’t know, NBC Universal are always making new

stuff, but they also have an archive.

And the people who are thinking about and maintain the archive have kind of different

use cases from other people.

So when I think about blurring the lines, I really think about the customer.

Like what do they need?

When they wake up and they go to work, what do they have to do with their fingers and

their hands and their brains on their computer?

And if it’s, you know, manage an archive, be the person who can fulfill requests for

content, help other business units find things.

I think an application like Wasabi AiR is probably sufficient.

Now there’s always new, there’s always features and things that can be added and improvements,

but I don’t want to take it beyond that.

Like I don’t want to go further into the MAM and DAM world because I think that those existing

systems are way better than anything we could build for those purposes.

Chris Lacinak: 84:16

So it sounds, yeah, I mean, you look at a lot of dams, you know, there’s complex permission

structures and a lot of implementation of governance and things like that, that Wasabi AiR doesn’t do.

So in those cases, it sounds like Wasabi AiR could serve the purposes of some folks who

don’t need a dam or mam otherwise.

And in other cases, Wasabi Air is populating those dams or mams to help them, give them

the handholds, the metadata for improving search and discovery within their own systems.

Aaron Edell: 84:46

Exactly.

It’s exactly, it’s a source of more metadata and it’s sort of a window into your objects that maybe your other MAMS don’t have.

The other important thing too, is if you flip it, if you think about like S3, right?

If I have, and we’ve had customers who have had S3 buckets with hundreds of millions of

objects in them.

If you go into the AWS console, into the S3 console, there’s no search bar, right?

That’s not part of object storage, you know, because it’s a separate concept.

I mean, it’s, you know, and you have to solve it with technology.

You can’t just search your object storage with no indices or anything like that, that

otherwise it’d take a million years.

So I feel like that’s where we sit.

We are saying Wasabi AiR, Wasabi object storage now has a search bar.

That’s it.

Chris Lacinak: 85:43

We focused heavily on audio and video today. Does Wasabi AiR also work with PDFs, Word documents, images, just the same?

Aaron Edell: 85:52

It does. Okay. It does.

And it’s a good point because those open up, being able to process that opens up whole

other worlds, you know, that we don’t spend a lot of time thinking about, but we will,

we’re going to start.

Because, you know, and video and audio too is not just limited to media and entertainment

as well.

I like to think of, for example, law firms and, you know, maybe there’s a case and there’s

discovery and they get a huge dump of data.

And that data might include security camera footage of a pool gate or, you know, video

or interviews and depositions and not just all the PDF.

And I think, you know, if you were opposing console and you wanted to, you know, give

these, this law firm a really hard time, send them boxes of documents and, you know, you

can’t search boxes and boxes of documents, right?

There’s no insight into that.

Or say, oh yeah, I’ll scan it for you.

You scan it and you send them PDFs, but they’re not, they’re just pictures, still not searchable.

So I think making PDF searchable, making Word docs searchable, pulling out, you know, images

that might be embedded in these things, processing those with object detection and logo recognition

and all sorts is a very valuable space that Wasabi Air does today.

You just got to put it in the bucket.

Chris Lacinak: 87:14

Well, Aaron, it has been so fun talking to you today, geeking out.

Just it’s really exciting. And your career path and your recent accomplishments have been just, you know, game changing, I

think.

Thank you for sharing your insights and being so generous with your time today.

I do have one final question for you that I ask all the guests on the DAM Right podcast,

which is what is the last song you added to your favorites playlist?

Aaron Edell: 87:43

Oh boy.

You know, I have to admit something that’s going to be, that’s going to divide your audience in an extraordinary way, which is that I actually own a Cybertruck.

I’m also a child of the eighties.

So the whole Cybertruck aesthetic really pleases me.

In fact, if you were to just crack open my brain and dive inside, it’s like basically

would be the interior of the Cybertruck.

And the music that would be playing is the kind of a whole genre that I’ve only recently

discovered because of the truck is sort of eighties synth wave.

So I’ve recently added to my favorites, some very obscure eighties synth wave music that

I could look up.

Chris Lacinak: 88:25

Yeah, please.

Please do. We have a soundtrack where I add all of these songs to a playlist that we share.

Aaron Edell: 88:33

So recently I added a song called Haunted by a group called Power Glove.

Chris Lacinak: 88:40

Okay. Awesome.

Aaron Edell: 88:41

And the Power Glove has a space in it. It’s Power Glove. 

Chris Lacinak: 88:45

Good to know.

Aaron Edell: 88:46

Because there’s also a band called Power Glove that doesn’t have a space.

Chris Lacinak: 88:51

Good to know.

We learned yet another thing right at the tail end of the podcast. Awesome.

Well, Aaron, thank you so much.

I’m very grateful for your time and your insights today.

I really appreciate it.

Aaron Edell: 89:01

It’s my pleasure.

Chris Lacinak: 89:02

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