Pod Insights Eye On A.I. #227 Sedarius Tekara Perrotta: The Importance of Data Quality in AI Systems About this episode In this episode of the Eye on AI podcast, we dive into the critical issue of data quality for AI systems with Sedarius Perrotta, co-founder of Shelf.
Sedarius takes us on a journey through his experience in knowledge management and how Shelf was built to solve one of AI's most pressing challenges—unstructured data chaos. He shares how Shelf's innovative solutions enhance retrieval-augmented generation (RAG) and ensure tools like Microsoft Copilot can perform at their best by tackling inaccuracies, duplications, and outdated information in real-time.
Throughout the episode, we explore how unstructured data acts as the "fuel" for AI systems and why its quality determines success. Sedarius explains Shelf's approach to data observability, transparency, and proactive monitoring to help organizations fix "garbage in, garbage out" issues, ensuring scalable and trusted AI initiatives.
We also discuss the accelerating adoption of generative AI, the future of data management, and why building a strategy for clean and trusted data is vital for 2025 and beyond. Learn how Shelf enables businesses to unlock the full potential of their unstructured data for AI-driven productivity and innovation.
Don't forget to like, subscribe, and hit the notification bell to stay updated on the latest advancements in AI, data management, and next-gen automation!
Stay Updated:
Craig Smith Twitter: https://twitter.com/craigss
Eye on A. I. Twitter: https://twitter.com/EyeOn_AI
(00:00) Introduction and Shelf's Mission
(03:01) Understanding SharePoint and Data Challenges
(05:29) Tackling Data Entropy in AI Systems
(08:13) Using AI to Solve Data Quality Issues
(12:30) Fixing AI Hallucinations with Trusted Data
(21:01) Gen AI Adoption Insights and Trends
(28:44) Benefits of Curated Data for AI Training
(37:38) Future of Unstructured Data Management
Transcript Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Speaker 00 We did it out of need, out of pressure, out of pain of being able to deliver more value for our customers because out of the box that didn't exist. So we built this observability layer and a lot has happened since then. We were founded in 2017 or we went to market in 2017. This need for transparency into data, especially unstructured data or files, different types of documents. This has been needed whether or not Gen AI has come to fruition. It was relevant. It has been relevant since digitalization. And really what has happened with Gen AI is a problem that has been old, that a lot of people are very, very familiar with. Of what is in my file system? What can I trust before I hook it into Gen AI?
0:41 Speaker 00 This has come to the forefront and this is where we're in the right place at the right time, solving a really pertinent problem for scaling Gen AI in businesses globally. So this, my journey into this space all started at the USP score. I was in Transylvania of all places and I was working at a think, at a think tank. So I was responsible for knowledge management at a think tank. And I did that for several years in my service and then I went out and became a consultant doing knowledge management, um, projects around optimizing the, the value of intellectual capital and best practices, lessons learned, key strategies, all that fun stuff.
1:22 Speaker 00 And did projects for Harvard, MIT, Stanford University, Society of Organizational Learning and a whole bunch of stuff for the World Bank. And that led me to spend time at MIT, honing my craft at the Sloan School. Where I really got into, that was when I got into AI and NLP specifically because when you do knowledge management, it's very manual and tedious and was looking for ways to optimize and speed up that process of getting quality data because it's always been the problem. The problem with knowledge management or, or any, um, let's call it any file system management is the quality of what's in it.
2:04 Speaker 00 People create multiple versions, they create, uh, and they put it in different places. Things go out of date very quickly. Some things are the approved version, some things are not. Things are noncompliant in there and there's really no transparency into it. So I started a consulting company. It was called neuron global and that's what we did. We did knowledge management consulting. We we served hundreds of companies on knowledge management consulting projects and we really got specialized in SharePoint. So got to experience SharePoint firsthand. At the the ignite, the Microsoft keynote, they actually said that two million SharePoint sites are created every day.
2:49 Speaker 00 And two billion files are uploaded to these sites every day. It's just the scale is.
2:55 Craig S. Smith? Yeah. And and for listeners who don't know SharePoint, just give a little thumbnail.
3:01 Speaker 00 Yeah, it's Microsoft's. There's there's two major file systems in Microsoft. There's SharePoint, which is a little bit more customizable, which enables you to create portals and communities. And then there's OneDrive, which is more of a like a Google Drive Dropbox box version of that. So we're specializing in that portal version, which is really knowledge management, kind of 101. And we were doing that for a number of years and got ran up into this wall of data quality. Because everybody's very enthusiastic at the beginning of the projects. You get a lot of buy in on the executive level all the way down and then and then and then it gets launched and then months later, stuff starts going out of date.
3:44 Speaker 00 People find things. It's not what they're looking for, or it's the wrong thing. And then they stop trying to use it. And then this negative feedback loop continues until adoption becomes very difficult and hard to enforce. And that was really where we needed to found another company that sat as an observability layer on whatever file system or whatever knowledge system was being used, whether it was Dropbox, Drive, SharePoint, Salesforce Knowledge, Zendesk Knowledge, whatever it is, wherever people were putting data. We needed an observability layer so you knew you would be able to identify what was accurate, what was up to date and what was trusted and what isn't. So that was the founding of it. We we did it out of need, out of pressure, out of pain.
4:30 Speaker 00 Of of being able to deliver more value for our customers because out of the box that didn't exist. So we built this observability layer and. A lot has happened since then. We were founded in 2017, or we went to market in 2017. And then, you know, we were, this need for transparency into data, especially unstructured data or files, files and different types of documents. This has been needed whether or not Gen AI has come to fruition. It was relevant, it has been relevant since digitalization. And really, what has happened with Gen AI is a problem that has been old, that a lot of people are very, very familiar with, of what is in my file system?
5:15 Speaker 00 What can I trust before I hook it into Gen AI? This has come to the forefront, and this is where we're in the right place at the right time, solving a really pertinent problem for scaling Gen AI in businesses globally.
5:29 Craig S. Smith? We were talking about SharePoint and what SharePoint does, and the problem is when you get your data uploaded into a knowledge base, then things start going haywire because things get out of date. You find that they're do- that sort of thing. So let's go on from there.
5:51 Speaker 00 Yeah, so I think there's just a natural data entropy that happens whenever you are trying to save documents anywhere, really, whether it's in OneDrive, SharePoint, Google Drive, Box, Dropbox. It's just this natural problem of trying to manage this chaos of everybody creating documents at different times, at different versions, and things going out of date and out of compliance. Anyway, this is a real problem for Gen AI, especially RAG initiatives, Retrieval Augmented Generation, and the Microsoft Copilot, because they need that data in order to retrieve answers. And that is essentially the major difference between web-facing content
6:36 Speaker 00 and internal company content, because there's a veil across your, an organization's documents and files, and it hasn't been pierced, because there's no transparency, there's no analytics, there's no way to know what's there. So this is a major impediment to Gen AI at scale, and this is what we work on solving.
6:55 Craig S. Smith? And it's, so it's, that's interesting about RAG. I was thinking it was more preparing data for fine-tuning or even...
7:06 Craig S. Smith? You know, for a basic training of models, but it's it's it's data in a knowledge base that an LLM or some other gen AI application is accessing. Is that right?
7:22 Speaker 00 Yeah, generally speaking, and these are techniques that they that companies are using to improve the RAG responses as they're fine tuning the models, but they're trying to work around bad data. Because most organizations have kind of put their hands up and said, I don't know what to do about this. There's no transparency into this. I don't know how to fix it, but we need to we need to find a way forward. Because we see the potential. We see the the awesome benefits of gen AI in our organization to automate more, increase productivity, increase efficiency, increase competitiveness, all those things. So they kind of work around it. But that's exactly the problem that we solve is the data quality issue, the garbage in garbage out issue. And that is monitoring and keeping track of
8:06 Speaker 00 what is in those documents and files and what can you trust and what what do you need to filter out?
8:12 Craig S. Smith? And how do you do that? I mean, presumably you're using AI to do that as well.
8:18 Speaker 00 Mm hmm. We are we're using AI. We're using chains of experts, NLP, ML. We have over one hundred and fifty autonomous agents behind the scenes working for us. So i- it surfaces in twenty two algorithms that are specifically designed to in the sections of documents, so not you can't compare a document to document. That's not very helpful. Because a question is being asked and it's in a particular sentence or paragraph on a particular page, and that's the area that that you need to dial into. So we're breaking it down into those sections and then we're looking for we're comparing those sections to one another and identifying duplication, identifying conflicting,
9:01 Speaker 00 identifying areas that don't have context, identifying areas that are noncompliant or toxic or biased. And then we're we're surfacing that on an interface so that people can actually see for the first time the quality of their data and it can be empowered to take action.
9:19 Craig S. Smith? And when you say it looks inside the document, if I have one document that that says, I don't know.
9:30 Craig S. Smith? You know, Craig's birthday is in October and another document that says Craig was born in March. It'll find that.
9:41 Speaker 00 Correct. That's exactly what it would find. That would be in our system on conflict. So you would link it up to whatever sources you have, any source. And then it's this quality assurance layer that sits on top. And in addition to that, you are able to, in real time, view your Gen AI answers. And we're able to know when an answer was derived from a document that has our section in a document that is bad, that that we've identified with our 22 different scores as a risk. So we can tell you exactly when an answer has been generated that is not trusted, that is not accurate,
10:21 Speaker 00 that is not compliant, so that in real time, you have the power to fix things and to improve your your RAG and your your Microsoft Copilot initiatives.
10:31 Craig S. Smith? Yeah, that's interesting. The you but but can't you put this, you know, let it run on your knowledge base and reconcile all of the all of the problems before connecting it to an LLM? Or is that just too big of an ask and you you really do it live?
10:57 Speaker 00 Yeah, I mean, it's crawl, walk, run. Certainly, that's where we're going, where you can automate more and more and more, and then just automatically fix things. Right now, what we do is we identify it so humans can determine whether they're going to fix it. But humans could also set up filters, and let's just say plays, that serve as gates of content going into the LLM. So you can you can automate the filtering out of content and you can automate the creation of labels and named entities as well, so that the information, the sections have more context for that LLM. So there's things that you do that you can do in real time. And then there's things that you do that that accumulate over time and improve accuracy
11:42 Speaker 00 day by day, month over month.
11:43 Craig S. Smith? And so if if someone's building a knowledge base for a RAG system, would they first do a pass? With and it's a product shelf or what? What is the product name?
11:58 Speaker 00 Shelf.
11:59 Craig S. Smith? Shelf. Okay. They would first do a pass with shelf. They would correct things that they know how to correct. And then shelf would run kind of in the background while during inference. And and then say, you know, give the user an alert that this this may have a problem. Be careful or or look more deeply into it.
12:30 Speaker 00 Yep, that's correct. There's really several use cases. That's the very proactive one. We see more of a of a use case where they've already implemented a rag or a copilot that they're having hallucinations and they want to fix those hallucinations because they can't scale it. So that's the more common use case is people saying, um, we have an issue. Houston, there's an issue here and we need a solution. So a lot of times we come into projects where they've been trying to do this manually. And then when they see this opportunity to automate a whole bunch, to actually see what's going going on. There's a lot of resonance with that and and we have very happy customers.
13:14 Speaker 00 But yeah, you can also do it before you try to even launch a gen AI initiative and that's called data readiness, gen AI readiness, responsible AI initiatives, those are what those hook into. And a lot of organizations are doing those as well.
13:30 Craig S. Smith? Yeah, you you guys started this company in 2017. I mean, that's, you know, it's recent history, but in in the world of gen AI, that's.
13:43 Craig S. Smith? Ancient history, what what were you focused on before Gen AI came along? And when did you develop this uh this solution for for RAG?
13:55 Speaker 00 Interestingly enough, uh we've been doing the same thing since we've launched, which was data quality, which is transparency into unstructured data. Like, what can you trust? What's accurate, trusted, up to date? So we've been always doing that because of the story I told you of not having any transparency in in in other projects when we were doing it from a consulting standpoint. Uh, what has changed is that humans are no longer the consumers uh of the good data, of the of the algorithms, of the analytics that we're running on top of that data. It's now it's L L M's are the the consumption. Uh, so it's just accelerated something that has always, we've been always doing and and has always existed.
14:40 Speaker 00 There's always been bad data and people have been searching and finding the wrong answers. That's that's a known problem. Um, it's just everything accelerated in the age of Gen AI because people realized that this was kind of their goal. This was their foundation for automating more and going faster. And it has accelerated our uh, let's say, adoption and accelerated the the the market need for something more general. Like, we started in the contact center where there's ROI to be proved uh in shortening a call, in uh reducing the amount of escalations there are, in improving CSAT. So this is where we've been because there's some, there's ROI attached to it.
15:24 Speaker 00 But every other department has the has been having the exact same problem. Uh, it's just harder to prove the ROI. And now what Gen AI does is it enables an ROI, a demonstrable ROI out of the gate for any of these because we can see exactly how many bad answers there are and show them, fixing them in real time, how much impact that they've had on their hallucination and answer accuracy.
15:49 Craig S. Smith? Uh, you know, I I had a a conversation a couple of weeks ago with a guy about data ontologies and how important it is to.
16:01 Craig S. Smith? You know, to build an ontology so that particularly for AIs, you know, Gen AI consumption. So there isn't confusion about what different things mean. And is that part of this? Does this help in building that ontology?
16:19 Speaker 00 Of course, an ontology on a graph, an ontology on a knowledge graph is the architecture and backbone of, I think, any real accuracy initiative, any hallucination reduction initiative. You really need to build it on that because it shows you the relationship between all the parts, all the sections of your data, and then on top of that, the metadata creates relationships that can't exist otherwise. So yeah, of course, we have a graph, we have named entities, we have ontologies, we have custom taxonomies, topic modeling, all these things that are, it was part of the Web 3.0 back in the day of Tim Berners-Lee, all these
16:59 Speaker 00 really cool technologies like the OWL framework and whatnot and RDF and all these other things that just didn't take root because there wasn't ROI connected to them, but they were always known to improve the quality and the retrievability of accurate answers. It's just the investment wasn't there, the appetite to invest because it was very hard to prove that ROI. Now with the speed of everything and running these models in real time and having all those conversations, you're able to see exactly when something is correct and accurate, something is not that helpful, maybe it's kind of accurate, but not exactly
17:42 Speaker 00 what you need, and that there's the human in the loop there, and then things that are just flat out either wrong because they're pulled from bad data or wrong because the model hallucinated an aspect of the question and the prompt wasn't clear enough.
17:57 Craig S. Smith? And how, running this, the shelf system on your knowledge base, is there some metric about how? Much you can reduce hallucinations?
18:11 Speaker 00 Yep, there's real time monitoring of answer quality and the improvements that you're making. Using the platform. So yeah, there's a real time answer quality dashboard where you can see exactly the number of inaccurate answers that have been produced and the source of those and that you could either fix, filter, or enrich a bad answer essentially, the data that's connected to that answer.
18:34 Craig S. Smith? And you fix and enrich in real time, or does it get shunted off into a queue that somebody else is working through?
18:43 Speaker 00 Yeah, it just depends on the organization and how they're set up. So someone can fix it right, right there and then. Especially if it's something as simple as there are two documents that are very similar, like two products, think of healthcare, like a healthcare plan, they're very, very similar, except some numbers that kind of mean everything. And the titles are even very similar. So you would want to put an aimed entity on that, telling the LLM that these are actually two different products and that
19:14 Speaker 00 if you don't know the answer, or you're going to try to pull from either of these, ask a clarifying question. Or essentially create a rule, when this particular plan is used, it follow these steps. And if this particular plan is used, follow these, but it's a way to have more control over the outputs of your gen AI answers.
19:36 Craig S. Smith? Yeah. How does somebody get started with Shell Fio?
19:40 Speaker 00 It's pretty easy. There's three steps. You first, you would connect any of your data sources. So connect your SharePoint, connect your OneDrive, can connect your Google Drive. We have dozens of connectors out of the box and a connector framework. And then two, it's gonna start running and identifying issues. So you have that dashboard. And then the third step is you get to fix, filter, or enrich and start impacting
20:07 Speaker 00 the answers being generated.
20:08 Craig S. Smith? And is this, how do you, how do you charge for this? Is this sort of a?
20:17 Craig S. Smith? Pay as you go, are there different tiers, I imagine, or do you sell seat licenses?
20:25 Speaker 00 It's a consumption model based on different tiers. So we, it's the number of pages processed, essentially, and our starting plan is at like 3,000 a month, and then it goes up from there.
20:37 Craig S. Smith? Yeah, you, you recently did a survey of, of companies on, on where they are in their Gen AI journey, specifically around the issue of data. Can you give us some of the insights from that?
21:01 Speaker 00 Yeah, sure. I, I mean, one of the things that is very interesting, and I, we heard this from a major implementer at the Microsoft Ignite event, and the quote is, "Six months ago, I wouldn't know I had a problem." Today, this is very relevant. So I, I think one of the things that the survey indicated was how fast the market is, is moving. The adoption curve is just, it's moving a standard deviation to the right in six months, which is crazy. See how, how fast things are going. And that, another thing that was really interesting was the number of active
21:41 Speaker 00 Gen AI, Gen AI projects. So over 70% of the organizations have active Gen AI projects, not in planning, but active projects. And then of those, the vast majority are still in the, the P O C stage. And I believe Satya Nadal said at the, at the Microsoft keynote that 10% of the market right now is ready to go into production in 2024, but in 2025, that number is being forecast to be 80%. So it's the speed of this market is blazing.
22:21 Speaker 00 And the other key takeaway, I was surprised was that a lot of organizations do realize that they have unstructured data quality issues. They understand that the the fine tuning and trying to chain different technologies together isn't the long term solution. It's a solution to to trying to get something work right out of the gate and very quickly, but it doesn't scale. So I thought that was another really interesting takeaway from the market. Another thing I can say was the pure scale of most organizations.
23:00 Speaker 00 So over eighty percent of the organizations polled had more than a million documents and files.
23:07 Craig S. Smith? Yeah, I remember that.
23:09 Speaker 00 Over fifty one percent of them had over ten million. So just the breadth of that problem of how to how to know what you need to address first and and how do you even try to manage such a huge number? You you can't do that manually.
23:28 Craig S. Smith? Yeah. And and what was happening before solutions like shelf?
23:36 Craig S. Smith? I mean, was it, you know, you have a million documents, but maybe, you know, it's kind of like my my office, you know, there are stacks and stacks of documents. It's really only the top inch that that is active. The rest are, you know, virtual archives. Uh, yeah, yeah, how, yeah. How does, uh?
24:00 Craig S. Smith? How does that relate to what you're talking about?
24:03 Speaker 00 Well, I I think it's there's it's a complex problem, right? Because there are some cases where, let's say, take customer service. Customer service has a manageable amount of documents that are serving inbound questions coming from the customer. So the agents are only using thousands of documents, not millions of documents to answer questions. And when it's thousands, you can actually curate and fix the problem. You can't get a hundred percent accuracy, but you can get darn well close. So in that particular use case, which we which that's where we were before Gen AI, you can actually
24:44 Speaker 00 Quickly, relatively, get to a trusted knowledge base. You can get to a trusted, a secure body of knowledge, corpus of knowledge to support a use case. Now, then there's technologies that are like federated search or semantic search, which go across an entire organization. And I think the users of these technologies have a sophistication because they know that they're using it. And when it pulls something, the document that they're not looking for, like whenever you're doing a, let's say, a Google Drive or Dropbox search, you're like, no, that's not it.
25:25 Speaker 00 You're mostly using your human memory to identify whether it's the right version or not. But it's, they're not particularly accurate because they don't know what the right version is. So the larger the corpus of knowledge, the larger the document base that you're trying to work with, the greater the problem is and the harder it is to solve. And that actually goes into what we advise the market to do, and that's crawl, walk, run. You shouldn't try to consume the entire beast out of the gate. We have companies that are like, oh, we're going to give you 1.5 petabytes of data to quality assure. And it's like, we can do that, but what are you going to do? What are you going to do with the output? We're going to identify tens of millions of issues.
26:08 Speaker 00 Do you have people that are going to be able to fix tens of millions of issues? A better move is to identify what use case are you trying to address? So for example, it's an onboarding use case. Okay, great. We have an onboarding use case. We're going to make it easier and faster for people to onboard, to self-serve, to learn all the core company cultural knowledge and processes and procedures. And, you know, it's very similar. Then, then you identify which, which documents, which folders, which files that you actually need to support that. You port it over, you put, you know, you put the correct permissioning around that. And then you manage that because that also changes over time. It's not like. You you you make this effort to get clean and accurate data and then you're done.
26:52 Speaker 00 No, because time passes and new products are launched and new people join and new directors put in new policies and procedures. Things are constantly changing. So for it to be accurate over over time, you need to be monitoring it in real time as well. But to start with something small to make it work, to get that confidence, to get people trusting the system and then to secure it and then to scale into to to more and more use cases. This is what we really see working and this is what we see a lot of the consulting partners that we work with. This is what they're advising to.
27:24 Craig S. Smith? Yeah. And beyond RAG, would this work for for creating a clean training data set so that you can, because that's part of the problem with hallucinations is there's so much bad data data in the in the training data. That and, you know, the the models are really looking at probabilities based on what's in the training data. But if you got rid of all of the the anomalies in the training data, then presumably, and
28:02 Craig S. Smith? I've thought about this with the with the coding assistants. If they were trained, because the first ones were, it was almost an accident, right? They they train up this massive LLM, I think it was GPT-3, and they realize, hey, this thing can actually read code. But, yeah, can you talk about that, about, you know, sort of training new models from scratch with better curated training data? And and how that would have would apply to shelf?
28:44 Speaker 00 Yeah, there's there's two other use cases that I think are really interesting for your audience. I mean, there there's the actually curating and
28:54 Speaker 00 Pruning and caretaking the data that you want to use. So if it was 1.5 petabytes, then that use case of getting of identifying the worst offenders in that obviously will have an impact and will be valuable. There's no doubt about that. So that's certainly an option. Anything that you're doing as far as data quality management before you're trying to do the the actual AI task is going to have downstream benefits. And then additionally, the autonomous agents. So if you're if you're creating experts on certain tasks and chaining to chaining them together, the data that they're consuming also
29:33 Speaker 00 benefits greatly from being pruned and caretaken before it goes into being automated and expanded very quickly. So they're really fixing the data and investing in the data. It just has downstream benefits for so many different use cases. It's why the structured data management space is so robust. And there are so many hundreds of companies there and huge businesses like Snowflake and their ecosystem and Databricks and their ecosystem, because people understand if you invest in that, you're going to be able to use it in whatever use case that you want downstream. So you're kind of fixing things at the very source. And then where you pipe that is wherever you pipe it to, you're going to have more value. You're going to have more accuracy. You're going to have more trustworthiness.
30:18 Speaker 00 So it's the same model for unstructured data. The more you invest in fixing it upfront before you you use it for whatever different use cases, the more valuable those use cases are going to be out of the gate and the less work you're going to need to do later. It's kind of like in the software world, having a really good specification versus not, and then designing something and building something and then finding bugs later and then having to fix the bugs. It's like 100 times more expensive than just having designed it in the front. In the data management space, it's the same thing. If you just fix things upfront, you save a lot of downstream headaches.
31:00 Speaker 00 And it's easy for me to say, because people have urgency to get something. Out of the gate and it takes time to do that. And that's why we kind of want to address that at both places at the like the responsible AI and the and the data cleansing. But also, hey, we have a Gen AI project right now. We're having hallucinations. We need to improve the the answer accuracy. You need to be able to do both. You need to be able to straddle both worlds because different companies are at different stages and even even different Gen AI initiatives within a company can be at many different stages and you need to be able to walk with the market and enable them at whatever states they're in.
31:36 Craig S. Smith? Yeah, it, you know, we were talking, you were saying, you know, a million plus documents. And to to focus on a particular problem or use case first, is there? And a lot of that, those documents would be stale at this point of the million or 10 million documents. Is there still value, value to be had in that?
32:06 Craig S. Smith? So called dead data, data that hasn't been accessed for a long time?
32:11 Speaker 00 That's a great question because we've heard some people say, just throw away all your data. As a knowledge manager, as my background of where I came from, that that like sends my, you know, shivers down my spine because there are best practices, lessons learned and key strategies. There are there are nuggets of gold in all of that. So just throw it away means you're just dumping out all of the learning, all the organizational learning that you've accumulated over many, many years and different, different people with different skill sets and specializations. You're saying, oh, we're just going to flush it down the toilet and we're going to get no value out of that. And I think that's a big mistake. There's so much there that can be harnessed and used. Now,
32:50 Speaker 00 do you want to use the duplicates of those, of a document that was created five years ago when there's 10 versions of it? And you want to use all 10 versions? No, you want to use the one that's correct. Do you want to get rid of all the information on policies and procedures around product literature? No, you can use that for R and D. You can use that for system design in the future. There's some really interesting use cases. I I believe it's from P and G. And Ikea where they're using old information to as inputs to design new products. Old information about products in different markets that were very successful at the time.
33:31 Speaker 00 That they couldn't, a a human brain couldn't possibly compare all these different markets and what was good and what was bad. And what was good about the design or bad about the design. But when you, when you input it into a model and you turn the model around that particular output of designing new furniture or new product based upon what has worked in the past, you can have remarkable results. And I I think it's the same for organizational knowledge, whatever it may be. You can learn from old sales literature. You can learn from old marketing position. You could learn from old CS information. Not all of it, not all of it is equal, but to say to throw it all away is really a big mistake and a lost, a huge lost opportunity. And I, and I would imagine the organizations that just throw it away.
34:14 Speaker 00 They're going to lose a competitive advantage to the ones that actually put in the time to get value out.
34:18 Craig S. Smith? Yeah, yeah. It's you guys started before Gen AI, but there are a lot of companies coming into the space. I heard, well, I won't name them, but they're, they're the spaces. I I I don't know if it's crowded, but I see companies talking about, you know,
34:43 Craig S. Smith? mining your unstructured data or cleaning your unstructured data. How do you guys see the market? Is it such a massive market as your survey suggests? That there's plenty of room for plenty of players? Or or do you think it's getting crowded or, you know?
35:06 Speaker 00 Well, at like, I'll, I'll answer that on several levels. First of all, this is. A needed and known problem for the entire market. So every organization has unstructured data. And as the survey indicated, um, what was it? Uh, over, over 80% identified that they have at least, uh, 50% of all of their documents have inaccuracies in them. So there's no organization that doesn't have inaccurate unstructured data. That is not possible because we are humans and we create things and we are flawed by nature. So, so first of all, the, the unstructured data as a, as a repository, it's a known issue there.
35:51 Speaker 00 It is not a hundred percent accurate for those 10 million documents. So that's the first thing I would say. Second thing I would say is it is a big dynamic market and there are needed, there are a lot of different technologies needed to solve the problem. There isn't a one stop shop to fix everything. What you're going to want to do is you're going to want to construct a tech stack around the very specific problems that you're trying to solve. The use cases that you're trying to bring to market. And I see we are very well positioned because of our, we've been, we haven't built this technology in the last six months. We built this technology over the last seven years. Um, and we've invested in this and this, we are practitioners in this space.
36:36 Speaker 00 So, um, we have a point of view and a perspective on the unstructured data and we have pattern match around it, which enables us to bring more value than, than you would imagine straight out of the gate, straight out of the box. And that's really where, that's where our position, that's where we think we're, we're, we're, we really well positioned in the market overall.
36:57 Craig S. Smith? Uh, the, the technology is continuing to evolve. I mean, Gen AI, uh, in, for production at least, um, has, or L L M's, I should say, have really only, um, been in, uh, the economy for what, three years now, four years?
37:20 Craig S. Smith? And the tools that you're bringing to bear, you were talking about increasing automation. Where do you see this this technology going? I mean, specifically, technology to
37:36 Craig S. Smith? to manage unstructured data?
37:38 Speaker 00 Well, I think to answer that, you first have to see where where is the technology going in general? And in the where is it going in businesses? Where is the most value to be had? And I mean, Jensen Huang and all the the CEOs of the hyperscalers are are essentially saying the same thing on this is that you're automating the the easier tasks first. You're taking on work that was repetitive. That is manual and you're bringing in slight automations using the autonomous agent technology or the expert technology. And you're taking more and more workload and and and offloading it to to AI, to different sorts and and streams of AI.
38:22 Speaker 00 And whatever the organization's strategy is on that automation, which I think every organization has it on their map, because it's I'm not saying anything new here. It's what enables that? What what enables our organization? What enables us to succeed the fastest? What enables us? First of all, what are the first things that we should start automating? Do we want to start in the customer service function? Do we want to start in marketing? Do we want to start in HR? Do we want to start in a in a in a horizontal function?
38:56 Speaker 00 Do we want to build experts that improve the efficiency of our of our SEO optimization? I don't know. The the use cases are almost infinite in imagination. So You're identifying what you're trying to fix, knowing that in the future, more and more things are going to be automated and there's going to be orchestration layers of these agents, agent swarms, agent frameworks, whether we go to general AI or not, I think it doesn't matter at this point. What is very clear is that there will be an increased amount of capability
39:38 Speaker 00 year after year towards more and more automation with more and more confidence. So 2025 is going to have more confident, more secure, more proficient, effective Gen AI initiatives in 2024. And likewise, 2026 will follow that same trend. And until it gets to a point where you can't, you can't forecast anymore. You don't know what is happening because so many different technologies are converging. That's another really interesting thing. You have the biotech, you have Internet of Things, you have fabrication and manufacturing, you have robotics. I mean,
40:20 Speaker 00 it is an interesting time to be alive. And I say all that because all of it's built on unstructured data, because LLMs consume unstructured data, they're fine tuned on unstructured data, and they output unstructured data. So it's the oil, it's the fuel source of the machinery of the future. So it is a critical component. It isn't the only component, like, like if you're building a jet, there's a lot of other parts and they all got to be right. But if you try to launch that jet without fuel, it's not going anywhere.
40:52 Craig S. Smith? Yeah. You mentioned at the beginning of Microsoft a few times, what's the relationship with Microsoft and with Copilot? And, and, yeah, tell us what you're doing there.
41:09 Speaker 00 Because of where we came from, because we were doing a consulting practice for SharePoint instances, essentially advising on how to build them and how to optimize them and how to make things more findable. We've had a long relationship with Microsoft and we've seen them grow and be extremely visionary in this space. I think they're one of the most visionary companies at the moment as far as where they're going. There's a couple others, but they're certainly one of the top ones. And their strategy, as shared last
41:49 Speaker 00 week at their major event, Ignite, at the keynote, their strategy is to invest in these co-pilots, to invest in faster, more automated workflows. Whether you're creating a document, whether you're editing an image, whether you're creating a presentation, or you're doing Excel sheet, or you're trying to query SharePoint or OneDrive, whether you're trying to use meetings or Teams, Microsoft Teams, they want to integrate and they want to be part of everywhere where people are working. And as I shared before, they have great ambitions, they have incredible products, but they all need unstructured data to be accurate, up
42:34 Speaker 00 to date and trusted in order to function properly and for people to use them and to adopt them and to scale them. And that's where we come in. We're an enabler of that. We want to work with Microsoft and with these co-pilots and with companies implementing the Copilot Studio and AI Foundry of Microsoft to build these technologies. We want to be part of that conversation to enable them to deploy more accurate co-pilots at scale.
43:05 Craig S. Smith? Yeah. And are you then integrated into some of those products? Or are there extensions or plugins or something that are available?
43:16 Speaker 00 Yeah, we are integrated into Copilot Studio, into AI Foundry, into SharePoint, into OneDrive. We can be a connective fabric between all of these different technologies and the pipework of unstructured data that connects them. The bigger point that I'd like to make in considering
43:36 Speaker 00 AI for whatever organizations roadmap for 2025. The greater point is that this space is moving very, very fast. That the LLMs are evolving almost by the week. And there's a, there's like a mini war of the LLMs of who's going to have the best one. And there's many models and there's custom models and all of these things. There's frameworks being used to patch them all together. There's different use cases of the agents and and the RAG retrieval augmented generation and building one's own models and so forth and so on. And I guess just one of the things that I would want people to leave with is the understanding
44:20 Speaker 00 that all of these are powered by the same fuel. And if that fuel is crude, if it is unstructured, if it's crude unstructured data, the engines aren't going to run properly. So no matter what the use case is, no matter what you're looking at implementing in 2025, I would just implore you to just think about what is your unstructured data strategy? What are you going to do to make sure that the information being piped into these initiatives is accurate, up to date and trusted? And, and I think that, that if people have that thought and going into the planning and going into the new year, I think they'll be in a good position to succeed in 2025 and beyond.
Previous #226 Nathan Michael: The Future of AI-Powered Drones and Autonomous Warfare (with Shield AI) Next #228 Rodrigo Liang: How SambaNova Systems Is Disrupting AI Inference Page updated November 5, 2026 · transcript and summary are machine-generated
