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Service Provider Executive Series Episode #9 - Gabor Fari (InteliNotion)
80 Plays1 month ago
In today’s episode, host Steve Gens is joined by Gabor Fari, Co-founder and Principal of InteliNotion, to discuss how structured content authoring has evolved, the value of combining it with GenAI, and what to expect from InteliNotion in the short/long term.
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Transcript
Introduction to Regulatory Executive Podcast
00:00:14
Speaker
Welcome to the Gens and Associates Regulatory Executive Podcast Series. This is Steve Gens, your host, where I have one-on-one conversations with leading executives that represent the regulatory software and services sector to learn more about their organizations, what they're up to, and more importantly, how are they innovating this space.
Evolving Content Landscape with Gabor Fari
00:00:33
Speaker
So today, I'm very happy to be speaking with Gabor Fari. from IntelliNotion and Gabor, I know we've talked on and off for probably the last decade, certainly with structured content authoring and you know The world's really changing. you know The authoring paradigm, or should we say the content generation paradigm, is rapidly evolving the last 24 months. And it was 20, 25 years where it's kind of been static enterprise content management, you know template style guides, and you know some structural content authoring. But the world's changing very quickly. So this is a very, very timely discussion.
00:01:12
Speaker
So before we get into it, could you quickly introduce yourself and also the the company for our listeners?
Origins and Focus of InteliNotion
00:01:19
Speaker
Hi, Steve. Thank you. And first of all, when you said ah ah but about a decade, I was reminded it was actually closer to two decades. So time flies by by very quickly when you're having fun, I guess.
00:01:30
Speaker
So thank you for the introduction. I'm the co-founder and principal at InteliNotion . We are a 10-year-old company. My partner, Vasaranganathan, and I founded the company about 10 years ago.
00:01:44
Speaker
um I've joined the company full-time in the last two years where I take active an active role in in helping run the company. um And InteliNotion grew out of a conviction that of both of us over the past 20 plus years, actually, that the industry is doing too much document management and not not enough content management.
00:02:05
Speaker
Each of us have deep experience in both document management and content management technologies, but we feel that content is is a going concern. It's an ongoing thing, so to say, and document documents are snapshot of content at a point in time.
00:02:23
Speaker
And that was our initial philosophy. When we formed the company, we wanted to build a new new kind of content management system based on structured content principles.
Structured Content Principles and Evolution
00:02:32
Speaker
um We felt that there was a strong need in the marketplace.
00:02:35
Speaker
um This is not a new need. ah Pharma companies in the early 2000s have attempted to implement structured content authoring systems. ah Most of them were based on XM authoring systems, and they're very complicated. And it seems that there was a strong resistance to these kind of specialized tools that were meant more for the publishing industry.
00:02:58
Speaker
So we wanted to build a new kind of platform that that was had had a front end based natively on Microsoft Word. So that's what we've been doing for about eight years until Gen.E.I. came along and we ah we actually came to the realization that these things belong together. So that's where we are today.
00:03:21
Speaker
um We have a strong structured content authoring foundation and we can talk a little bit later about Gen.E.I. and how that fits together. Yeah, and I think that's a good place to start as far as structured content and authoring, how it's evolved over you know the past decade or 15 years, like you said, maybe more, and kind of where it's going. So ah you know with your introduction, it really brought me back to the DIA show. And I know Greg and I had a very robust conversation with you at this year's RSIDM show, but if I go back
00:03:57
Speaker
about 15 years ago, because you were talking about XML, and I forget who the speaker was. So they're up there. It says, like, you really shouldn't be scared of the structured content authoring. They went into an XML, you know, editor.
00:04:10
Speaker
And if you never coded, I mean, when I started my career, I built hospital software. So, you know, coding is coding, no matter, I think I learned like nine languages before I got on the business side of, ah you know, technology.
00:04:22
Speaker
And he, I think he lost 90% of the audience because he was so proud of his coding and going up and down. And I looked around and people eyes were bulging, because they're used to work, you know, so that's probably,
00:04:34
Speaker
maybe in the dinosaur period. And so maybe we start there because Greg, you know, on the team too, he's tracked this for the last 15
Generative AI's Role in Content Authoring
00:04:43
Speaker
years. Like every two or three years, we'll have something either in our large regulatory studies or the pulse, like we're structured content authoring on the medicinal side and also on the med tech side.
00:04:53
Speaker
So maybe bring us up to speed a little bit that I think we want to spend most of our time with what you already introduced about the generative AI and structure, because the big conversation in regulatory three to four years ago, we have to go from documents to data and the whole paradigm is fundamentally changing. And I think that's a value of today's conversation, but maybe a brief history lesson, Gabor, and then we'll get into that that the juicy conversation.
00:05:19
Speaker
Yeah. one of the ah So first of all, I actually think I know which presentation that was. I was there too. And so I think that that that is a well-known well-known in the analysts of DIA history.
00:05:34
Speaker
um So when I think about this whole process, so first of all, we are not, ah so ah for us, Word is a tool. It's a tool that sits on everyone's desktop.
00:05:45
Speaker
ah We decided to build a tool that is ubiquitous. It's part of ah the everyday workflow that people use. But to us, Word is, it's it's not because it's Word. it's To us, Word is a rich canvas for content. So we don we don't necessarily say that Word is the future.
00:06:03
Speaker
We built a back end for structured content. We can switch it to pretty much any other editor. In fact, we've already built an interface to an Exum editor as well.
00:06:14
Speaker
So the point is is how you structure content. and And the other point is to bring the tools to the everyday productivity tools that people use. We don't see word going away anytime soon, but the need to for data to content has been there for a very long time.
00:06:31
Speaker
One of the early quotes that I have, and that was probably about 15 years ago or so too, is when I heard a pharmaistic ah a pharma executive speak and this they said, or he said that,
00:06:43
Speaker
It takes our industry 12 years to turn structured content into unstructured content because that's what the regulatory authorities require. And that really struck home. And and the idea here was, well, why don't we keep the content structured as early as in its lifecycle as possible?
00:07:03
Speaker
Now, the FDA is not really ready yet, or the regulatory is not not ready yet for complete ah conversion and to chuck all the investments they've made out of the window because people still need to read documents. So what we see here is actually a parallel universe where documents will coexist with direct data-driven content because people will still want to want to read and understand content. For example, when i when i try to understand something, i want to interact with the content. I want to read the content.
00:07:34
Speaker
I want to i would read it as pages ah rather than just one big blob of text. So there's still ah an important part that documents will play or electronic documents will play ah in parallel to the direct data-driven submissions or direct data sponsor to the regulatory agency interaction with with data. So one of the things we're looking at, just to get into it right away, is FHIR.
FHIR Standard in Healthcare Data Exchange
00:08:06
Speaker
We see that FHIR is actually FHIR standard, which stands for Fast Healthcare Interoperator Resources,
00:08:13
Speaker
This was developed in the early 2000s as a way for electronic health records to be able to interact with each other because there was no lingua franca between these healthcare systems.
00:08:23
Speaker
So some modern web technologies were used and basically that's where they came up with this whole concept of a bundle, a bundle of information that's exchanged between different systems. It's a pretty brilliant concept, but it doesn't mean that everything will be a bundle because,
00:08:39
Speaker
ah These FHIR bundles can be sent from one system to another. They can be interpreted. But people still want say, well, what's in it? do i How do I understand it?
00:08:50
Speaker
I want to be able to do things like search on it. FHIR is not really ah a content standard. It's a data standard. And some of the things that are ah so embedded into Word, like annotations, people the ability to collaborate, to co-author, and all these other things that people do to prepare the content,
00:09:09
Speaker
I'm not there because it was meant to be a data exchange standard. So the way we see the world is that ah content and data exchange will coexist for a while and it will probably intermingle.
00:09:22
Speaker
So that's great. um I think that's so kind of a very rapid kind of history. um But as you already mentioned, and and and Greg and I certainly are experiencing, you know the last two years, there's just been such fundamental change. And you know we recently were invited with one of our clients where they were looking at structured content authoring,
00:09:44
Speaker
maybe not generative AI, and certainly at the shows and a lot of the conversations, you're you're more into these conversations than we are. Over the last six months, there's school thought, well, oh, geez, generative AI is going to replace structured content authoring. We don't need that anymore. And then there's another like, and I think you're you know in that camp because you certainly educated Greg and I as far as, well, the better the structure of your content, the better the job that AI can do. and The one thing I think everybody agrees is it's still early days you know with this stuff. so And it's it's moving really quickly. And I think the other dimension on this that a lot of people, when they think about generative AI, structured content, authoring, or the combination, they're really focusing on...
00:10:27
Speaker
you know, the primary, you know, first time to market. So they're thinking of the CSR, the protocol, but, you know, 70 to 80% of the work is lifecycle management. So how can these concepts with the structured content? I know you guys have done a lot, you know, in the labeling and CMC and just think about all the lifecycle management, how that cost.
00:10:47
Speaker
That's not so much efficiency, you know, with efficiency or productivity, you know, drive cost out of life cycle management. And that's usually 78% of a organization's activities and cost.
00:10:59
Speaker
So I don't know where you want to go with this. I just recall you, I and Greg talked about 30 minutes. and We might not have 30 minutes right now, but structure content, authoring, generative AI in parallel and how they benefit each other.
00:11:14
Speaker
Sure.
Gen AI and Structured Content Synergy
00:11:15
Speaker
Well, So first of all, I will say that this is not me or us saying that, but when you listen to to key industry analysts and the the thought leaders, not only the life sciences industry, but also other industries, including things like technical technical manual authoring, et cetera, ah the leading thinkers will say ah that the key to Gen.AI success is structure. The need for structure will not go away.
00:11:42
Speaker
And when you look at how Gen AI works, Gen AI structures content in its own way. So there's technical terms that that we can we can use like chunking or semantic chunking and things like that.
00:11:55
Speaker
But they are these systems are set up to actually structure content so it makes sense for the large language model for the Gen AI. It does not necessarily make sense in terms of scientific ah scientific purposes or regulatory purposes.
00:12:10
Speaker
We want to actually break content up the way we interpret the content, the way they're logically follow content reuse. So one of things is when we talked about structured content, I think we need to talk about componentized content because ah the structure comes from the components and the component reuse is what enables these workflows that we we built out.
00:12:30
Speaker
um When ChatGPT came out about three years ago, there was no doubt about it that we also agreed that this would be revolutionary. It took us a little while to try to figure out how we would embed this into our software, where it would make sense. But the more we thought about it and the more we talked to our customers, we realized we are actually a very good position because there are things like when you want to generate a draft document,
00:12:57
Speaker
You don't generate a protocol or a CSR that's hundreds of pages long, just just by writing a few prompts. What you need to do is logically um componentize the content.
00:13:10
Speaker
And then there's that other, of course, the other, ah some issues that come up with Gen AI, things like hallucinations. So the bigger, the the longer text or the bigger the document you want to generate, the more there's a chance for hallucinations. So you need to have techniques to actually make the content generation more precise.
00:13:29
Speaker
And one of the benefits of componentizing content is actually keeping keeping the component you're generating short. So it's called the context length. So if the if if the content you're generating, the length of the content is actually approximately similar or less than the context,
00:13:48
Speaker
That means the prompt and dependencies and all those other things you're sending to the LRM, the higher the quality of the content will be. So we we what we say here is that what we found is it's ah almost like for us, like peanut butter and jelly. If you you what um if you want to excuse the colloquialism, they really fit together very nicely.
00:14:06
Speaker
So when we think about Gen AI, we think about how can Gen AI make the creation and dissemination of the information more efficient. um We're not saying it's going to make it more precise. It's all about efficiency.
00:14:20
Speaker
And the other factor that is very important is we need to keep the human in the loop. We don't see GenAI as a panacea that will do everything. It is great. It is revolutionary. It does a lot.
00:14:33
Speaker
um Where we see GenAI is a a couple of couple really interesting applications used for applications. so We can see Gen AI to unbird medical writers from road ah road tests, so such as rewriting ah tense tenses. So let's say you take a piece of text from a protocol that goes into CSR.
00:14:54
Speaker
It's the same text, but it goes from present ah from ah future tense or present tense to past tense. Gen AI can do an excellent job with that. Gen AI can do an excellent job summarizing tables.
00:15:06
Speaker
It can do an excellent job summarizing images or um ah figures. It does an excellent job of in in translations and summarizing content so you can take content from the protocol and create an ICF, the informed consent form.
00:15:23
Speaker
So the way we look at it is our existing our customers can be a whole lot more efficient by using the Gen AI we built into the structure content ah foundation.
00:15:34
Speaker
um But the other thing that people forget, and I see this time and time again, every time there's a new market shift, there's a cottage industry of wannabes who come along.
00:15:45
Speaker
and there's and And it seems to me that ah that any company who can who has a couple of engineers who can write prompts wants to become a JNI company. What they're not understanding is the compliance requirements it takes in this industry. So, we for example, in our into our platform over the past 10 years based on industry requirements, the customer ah customer needs, we built a lot of very sophisticated features that are all needed by customers.
00:16:14
Speaker
We need complete audit trails. we even we We're just now even building legal holds into our platform because our customers need it. So we need a strong componentized component management and kind backend that actually delivers content governance because that's also another, in our opinion, in this industry, another key of a key ingredient for success to be able to make this work. A lot of companies are jumping in, including in-house initiatives.
00:16:44
Speaker
So people want to build in-house, in-house Gen AI solutions. For many cases, for one-off documents that works well, but as a generalized solution that can actually generate any kind of document that is needed for the submission, um that's a very different proposition altogether.
00:17:05
Speaker
Yeah, so that's a lot to digest.
Client Success Story: Efficiency and Compliance
00:17:07
Speaker
And the one thing I just ah you know wrote down, because you know the SCA, people kind of identify that and as older technology, but it's it's about the ah structuring, as you say. And I just wonder, maybe from a branding, it should go from SCA to CA and just call it you know componentized authoring, because at the yeah core, that's what it is.
00:17:28
Speaker
And that hasn't changed, and it won't change, right? you know, going forward. um The other thing I was just kind of curious about, because, you know, you work with different clients that, you know, you know the clients that you've had, you know, in production, say for, you know, five to seven years. So maybe it was more the classic structural content authoring. Maybe it was in clinical. Maybe it was an area.
00:17:51
Speaker
in um you know in regulatory but like what kind of benefits were they seeing like from an efficiency or productivity and do you see like that benefit curve that's going to be going up as there's more and more of the generative ai kind of built into uh the platform it's just as you were speaking i was kind of curious about this that you know kind of your key customers you know what are they saying what is their journey too Yeah, so it's measuring the the ROI is kind of difficult because it's complex, but we've we've we've had customers who came up anywhere between 20 and 40% of efficiency gains. But yeah I found it really interesting because there's a lot of talk about efficiency gains, efficiency, et cetera, because that's what what accountants like when when they pay for systems.
00:18:38
Speaker
But ah here's a real life case story. one of our One of our key customers told us the story that that they didn't even need to do the ROIs. They bought the platform for an ah ROI, but then they found out the first time they had to avoid a protocol amendment because they had structured content and content governance, it paid for the system.
00:18:58
Speaker
And they do 30 to 40 protocol amendments a year. And one protocol amendment costs more in the entire system. So the system will have paid for itself 30 to 40 times over.
00:19:10
Speaker
So I find it interesting that there's a lot of talk about ah about ah ROI. There's not enough talk about compliance and and content governance and and and things like that. um Because with our system, we know exactly who the components are. So if if there is something has to be changed, we know immediately which are the downstream documents it it affects.
00:19:35
Speaker
And that's really not content, ah so much structured content authoring. but content governance that is built in the system because it's a component management platform.
00:19:46
Speaker
So, and you're right, I agree with you. a component authoring would be a a better term. I just call our platform a content platform exactly for the reason. It's a content platform that does, that actually ah manages content governance.
00:20:02
Speaker
So, yeah, and and one I love that customer kind of success story because that that's kind of impressive. And that it it reminds me of a growing conversation we're having with some of our clients just in the last six months because, you know, it's some of the classic business case or ROI and regulatory and really hasn't changed. It's, you know, it's a efficiency, productivity, you know time to market, last patient visit, database lock, you know to the filing, blah, blah, blah. so But you know what we've been introducing as we think more about it, and I think the example you've given with your customer, with it paid for itself 30 to four times, that
00:20:40
Speaker
You know, there's a triangle around efficiency, productivity, but at the bottom, and it's something that manufacturing people think about every day is throughput. Like, how can we increase the throughput of the regulatory organization or the clinical organization? So really, the success story that you're talking about is increasing throughput. It might get masked by, hey, it's efficiency or productivity, but that that's throughput.
00:21:04
Speaker
Yes, you can have that that type of, you know, kind of business benefit. And I'll take throughput, you know, because efficiency is a subset of, ah you know, you know, throughput, you need efficiency, the productivity and the efficiency to get throughput.
00:21:18
Speaker
So just kind of a reflection there on it. So, hey, the the time's going quick and the world is changing and technology is changing
Future of Automated Authoring and Data Connectivity
00:21:28
Speaker
quick. So what should we expect from a telenocean and kind of the short and longer term?
00:21:33
Speaker
So yeah, and before I answer that, let me just just share one more reflection on this because I completely agree with you. Throughput is important and we have this phase of quality by design and that's where we achieve quality and efficiency by design.
00:21:48
Speaker
But but ah the trick is to be more efficient and increase throughput by also increasing quality. That's the trick of the matter. And that's why if you actually structure the content, componentize the content, you can actually, you can do those things.
00:22:01
Speaker
um One use case, and this also cuts into the your your current question, is ah one of the best use cases these days that I can think about is automating the clinical study reports.
00:22:13
Speaker
um it It can literally cost ah take weeks to to finish a clinical study report. And our tool has been has been used for years to but to um for clinical study reports.
00:22:26
Speaker
But now with the advent of Gen it can make it a whole lot more, ah can increase the throughput a whole lot more because we can take all those tables that are in in the document and we can use Gen AI to summarize them. And Gen AI can do a very, very good job, especially if we combine it with advanced techniques like ah RAG, which is retrieval augmented generation or graph RAG, which we also use, which is a knowledge graph driven way of driving retrieval augmented generation.
00:22:56
Speaker
um So these things are actually real, and this is real the real benefit of Gen AI, people are latching onto that. We're now supplementing this with a higher level level of automation, where we can actually automatically bring in tables into mass populate or use bulk import of tables ah into the documents. So literally, you can almost create ah a CSR generator.
00:23:20
Speaker
That's what the industry wants. but One of the things we should not forget is that with all this news hype about automation, what Gen AI allows you to do, it doesn't really necessarily reduce all the work that goes, it doesn't automate it.
00:23:36
Speaker
You can front load a lot of work. so So what you used to do is you used to bring the tables into into the document and then you and then then then you wrote the summaries of the tables. What you can do instead is you can actually ah prebuild ah pre-build the prompts based on synthetic data and and test the prompts and make sure the prompts ah meet your requirements. And when the actual actual clinical data after database log goes into the document, then can run those prompts. So there two things are happening ah to to create this magic. First, you but you ah you you take the work that was front-loaded and you back-load it ah because you do this work earlier.
00:24:18
Speaker
And the other thing that you're also doing doing is you're actually achieving that by writing the prompts, actually applying to to multiple document types. So where is all this going? I believe the future is automated authoring.
00:24:31
Speaker
One of the examples I cited was CSR, that's automated authoring. how How can we automate the production or or generation of documents at a much higher level so we can take it from one of manufacturing ah process to a more streamlined mass manufacturing process?
00:24:48
Speaker
So it's almost like industrial automation for content, which we've been talking about for a while. um And the other the other is is the the whole data connectivity to content. The other aspect of content automation is being able to to tie the document to live data, which is also another area we're working on.
00:25:08
Speaker
So where I see this going is higher level of Gen AI but We are actually looking at introducing agentic frameworks to not only generate the content, but but to do quality check, test the content that's generated. So different agents talking basically interacting with each other. One agent would generate the content. The other one would almost be the checking agent.
00:25:32
Speaker
um There's a lot of potential in that. And in general, just... achieving a much higher level of automation for generated content. And this level of automation is not only for what I described for the CSRs, where we would bulk in, rewrite the prompts and bulk load the tables into the document. It's still a document, but how about creating documents that are connected to live data?
00:25:56
Speaker
So let's say there's this backend system. So CMC is a very good example of that. But I also do think that clinical data for CSRs could benefit from this automation because When you look at how CSRs are written today, all these tables are typically generated in Word, b ah RTF, or.docx format that are brought into the documents.
00:26:17
Speaker
um It would be probably much easier to keep that in table format and have the document be connected directly to the table. So this is something that is a real life ah use case today for CMC.
00:26:31
Speaker
But I believe other documents like like clinicals will benefit from that too. And at the end of the day, we go back to what I said in the beginning, Microsoft Word is just a placeholder for content. It's a rich canvas for content.
00:26:44
Speaker
We're using with it because that's what people use. And you have all that ecosystem of tools such as styling tools and reference manager tools that are tied to it. But the at the end of the day, it is about the auto, is the ubiquitous automation of content.
00:26:59
Speaker
And then again, if you if it's Word, you just basically freeze content to Word format. But the output, ultimate output may be may very well well be FHIR.
00:27:10
Speaker
And we've actually built that too. We can actually take a Word document and for downstream systems, we can actually create a FHIR document out of that. So Word is just a placeholder. It's a way station for content at that ah point in time, which goes back to what we said in ah at the very beginning of our company.
00:27:29
Speaker
the The document is is just a snapshot of content at a point in time. And what we're doing now is we're using Gen.ai to help create that content, to quality check that content.
00:27:42
Speaker
And the content is not ah now more tied to to real data. But when people people still will want a snapshot of that document to read it, understand it, because that's typically how the human mind works.
00:27:55
Speaker
We don't think like machines. Machines can process data in in a real-time continuous format or continuous flow format. Human beings really, ah really prefer usually to read pages of documents, understand that, be able to mark it up, make comments, et cetera.
00:28:13
Speaker
And Word is still excellent format for that. Yeah, so there was a lot there. And I just have a few summary notes for our listeners. And maybe maybe another podcast we might do, because I didn't introduce, so you know, that something we're tracking in the implications, even potentially, I think, longer term, you know, on authoring nothing in the near term, meaning two to three years or So much investment going on, ah same dollars amount as AI, and we call them data aggregation platforms, the data lake, data fabric, data mesh. My favorite client calls it a data swamp because the data's not good.
00:28:47
Speaker
But you know starting to author on that because you talked about like you know the tables, listings, and all those things that might be in a in a piece of content in the content management system where Some of this data might eventually be in like a data aggregation platform, but I think that's a topic for another day.
00:29:04
Speaker
um Just summarize a cut a few things because, yeah, we're really getting into like kind of business case discussion that the other thing about that you mentioned about quality, you know, for quality, you know, improving quality for me.
00:29:17
Speaker
that brings higher confidence. And when there's higher confidence, there might be less review cycles ah that you'll make a decision quicker to move on that regulatory activity or maybe, you know, the the submission of a document.
00:29:30
Speaker
um And the other thing too, we've been tracking the sentiment of AI because there, you know, I think if you went a year, year and a half, maybe two years ago, if you take the Gartner hype curve, it was like at the top of the hype.
00:29:42
Speaker
you know, everybody's going to lose their jobs, we'll be on the beach because the machines are going to be doing everything, you know, and there will be no more work. And obviously I'm exaggerating quite a bit, but kind of what we were thinking about two years ago is really where the sentiment is. And maybe in the authoring construct, it's the authoring assistant.
00:29:59
Speaker
Maybe it's a research assistant, but it's an assistant, you know, different really ah tools. We call them cool tools or innovative tools to help us along and do things, you know, smarter, you know, quicker,
00:30:11
Speaker
um without sacrificing or hopefully increasing you know quality on that. So certainly as we track that in our pulse surveys and our bigger studies, we've been tracking the sentiment of AI very, very closely because it's changing quite a bit.
AI as a Writer's Assistant
00:30:25
Speaker
I completely agree with you because AI is a very broad term, but what we see is not just to have ah nice deliver a delivery tool that automates automatically generates documents, but We've built, we philosophically, we've built a tool that actually allows people to have a rich interaction with content.
00:30:47
Speaker
So think of co-pilot, but for medical writers. We have an AI assistant in there. You can go out. People can actually use it. They can ask questions. They can actually ask for references.
00:30:57
Speaker
So it's not only about generating the content, but ah giving the medical writer information. the ability to interact with the rich content on a documented basis, on a knowledge base, knowledge base basis, on a knowledge source basis, and also on an individual component basis.
00:31:14
Speaker
All of those things are needed because human beings need to understand the aggregator of content that's being generated. And those are different requirements that all have to be built into a tool so people can use those things.
00:31:26
Speaker
Yeah, and I think your your points just goingnna go to say how rapidly this this you know this area is changing. It was kind of frozen in time for 20 years. And now, yeah it's it's true you know for our industry, it's rapid change you know and yeah actually exciting. So um with our listeners, some of them might want to get a hold of you. So what's the best way? Is it through your website, LinkedIn? How can they get a hold Either way.
00:31:50
Speaker
ah LinkedIn, um I have a profile there. They can look up the company, Intel in Ocean with one L. um They can go to our website, which is www.intelinotion.com.
00:32:02
Speaker
um They can also send me an email, gfari at intelinotion.com or info at intelinotion.com. If somebody wants to get a hold of hold of us, it's it's pretty easy.
Closing Thoughts and Engagement Invitation
00:32:14
Speaker
So thank you for the opportunity, and I share your excitement about this.
00:32:19
Speaker
There's a lot more to come. Every day, we're finding new new applications of Gen.AI that can make users more more productive and make their jobs easier.
00:32:29
Speaker
But the human in the loop will not go away. I can end with that. Completely agree. 100% here. No, no argument or debate at all. And for our listeners, um I hope you really enjoyed it was an insightful podcast. You can see the other ones in our regulatory executive podcast series. And certainly if you want to get a hold of us, we love LinkedIn too. And again, this a unique name. So it's easy, like Gabor, you know, probably easy to, well,
00:32:58
Speaker
you know search on or certainly off our website and our contact page. So um so thanks again, Gabor. And maybe in six or 12 months, we'll follow up and see where you're at.
00:33:08
Speaker
We'll give you an update. Happy to do that. Thank you, Steve.