theCUBE + NYSE Wired: Data + AI: Turning Data Into Knowledge for Autonomous Systems | Iman Bhattachary, QIAGEN | Neo4J GraphSummit NYC 2026

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More from theCUBE + NYSE Wired: Data + AI: Turning Data Into Knowledge for Autonomous Systems

Iman Bhattacharya

Sr. Global Product Marketing Manager

QIAGEN

play_circle_outline QIAGEN Digital Insights bioinformatics arm and its role in life sciences data

play_circle_outline Human-curated knowledge foundation built over 25+ years with MD-PhD experts

play_circle_outline Three-tier QIAGEN Discovery Platform: knowledge foundation, MCP, Discovery Explorer agent layer

play_circle_outline Traceable, Explainable AI to Prevent Hallucinations in Drug Discovery: Governance, Provenance, and ROI

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Iman Bhattachary, QIAGEN | Neo4J GraphSummit NYC 2026

Iman Bhattacharya

Sr. Global Product Marketing Manager QIAGEN

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John Furrier

>> Palo Alto Studio Connection, Silicon Valley and Wall Street. I'm John Furrier, co-host here with Dave Vellante, my co-host. Hello, I'm John Furrier with theCUBE. We are here in New York City for GraphSummit NYC with Neo4j. Obviously graph databases, knowledge graphs, ontologies, the hottest conversation in the AI infrastructure layer of data as it brings a lot of value. Our next guest, Iman Bhattacharya, he's the senior global product marketing at QIAGEN, a biotech company who is— obviously one of the benefits of all the GPUs and the compute is the breakthroughs. Iman, thanks for coming. Appreciate you stopping by theCUBE here. Appreciate it.

Iman Bhattacharya

>> Thanks for having me.

John Furrier

>> So talk about what you guys do, because I think this is illustrative. You're a perfect candidate for the AI chat because we're seeing tons of breakthroughs in science with supercomputing AI infrastructure. But the data is only as good as the data you feed in. So explain what you guys do.

Iman Bhattacharya

>> Absolutely. Thank you for asking. I am from QIAGEN Digital Insights, which is the data part of QIAGEN, the bioinformatics arm. We cater to 3 different customers: research customer, discovery research, clinical, and also data science and the biopharma part of it. Specifically today, we are here for the data science part of it. And then what you just mentioned is just like your model is as good as your quality of data. So I think while everybody is talking about AI and the model layer, we deeply focus on the knowledge foundation and the knowledge content of it. And we believe that AI is here to stay. AI is here to elevate yourself, but it has to be deeply grounded in human curation.

John Furrier

>> The AI infrastructure with NVIDIA, we've covered them like a blanket.

Iman Bhattacharya

>> Yeah.

John Furrier

>> You're seeing hospitals, science, pharmaceuticals, biotech, all winning with the new horsepower they got. But you've always had a lot of data. Explain the data landscape that you guys are dealing with and what's changed the most in the AI era we're in now.

Iman Bhattacharya

>> See, I would say that data, as in the foundational knowledge, is the connective tissue between that AI model and this enterprise and these accounts, right? And everything is changing. Models are changing, agents are changing. They are getting more intelligent, but they're as good as your knowledge foundation, right? It has to be fully grounded in knowledge, which has to have proper provenance, it has to be traceable, and it has to be properly governed. So that's what we do. Yeah.

John Furrier

>> Yeah. And the agent conversation is, well, they're off the rails. We got to have governance. The observability, the explainability piece is huge. Reliability is huge. Safety is a big conversation now. I have to ask, it's not like you guys woke up one day and said, hey, we got to get some data to use AI. You had data. So going into this AI revolution, how have you guys looked at this data opportunity? You mentioned some of the knowledge layers you're building. Is it compounding? Is the intelligence helping? How does that— how is that working? Take us through the mindset of a day in the life of, okay, wow, we got this superpower called computing. We got all this data. How do you guys look at this and where does that knowledge graph and context fit in?

Iman Bhattacharya

>> I'm glad you asked this question. It's not just that today, QDI has been here for a long time, but our group has been manually and expertly curating this data for over 25 years. It's going to be 3 decades, right? If you go to Stanford University, if you just go to their Discovery Walk, you will see our Ingenuity Knowledge Graph is etched in the timeline, right? So we have been doing this for close to 3 decades almost. And when I say what we do, it's just like we have over 150 MD-PhD level experts. They manually curate the data. Ultimately, our goal, it has to be context-driven. Without context, it's nothing. That's what I would do.

John Furrier

>> So you guys pioneered the manual side of it, and people who know what that means know how hard it is.

Iman Bhattacharya

>> Absolutely.

John Furrier

>> Um, so I have to ask you, what's it like now? Is it— wow, are you leveraging the models to set up more faster knowledge banks? How has it changed? Because you have the experience of building them, you're seeing the tooling's getting easier these days, starting to hear the word ontology kicked around. Alex Karp from Palantir talks about ontologies like it was discovered yesterday. That's also been around since the '80s. So Ontologies aren't new. Yeah, knowledge graphs aren't new, but they were kind of pioneered by the early adopters, early drivers. Now you have a new scale of AI. Talk about that transition and kind of what's happening now.

Iman Bhattacharya

>> I mean, don't get me wrong, AI may be a new word or maybe a little buzzword, but we might be known for manually curated or human curated or expert curated. Knowledge, but we are always leveraging the machine learning techniques, right? Which is the actual AI things where you talk about, we use NLP process to actually derive this data and feed it into the system, right? So that's what we do. AI is here. We are very AI pro company, but that is grounded in knowledge, which gives you context.

John Furrier

>> Andy, what's the new additions that come to the table with AI because we're hearing unstructured data, structured data, we have relationship maps in the knowledge graphs, you have a great recursive capability which plays well with AI on the science side. What's going on there? How do you see that relevance piece of it and where does it kind of plug in?

Iman Bhattacharya

>> I'll give you an example. 6 months ago, we launched this product called QIAGEN Discovery Platform, which is a 3-tier product, essentially, if you can imagine the bottom layer is grounded in knowledge foundation, which is that over 25 years of knowledge foundation. The middle layer is the MCP or the Model Context Protocol, the way you access it. And the top layer is the Discovery Explorer, which is an agentic layer, right? That's what we have built. We are very excited to go to market with that one and with our newest capability with MCP. But that's what is happening. So it's not just data or it's not just knowledge foundation. It's actually a complete system. The goal is not to make it siloed. The goal is to make it be a part of that ecosystem.

John Furrier

>> It's interesting you mentioned silo because one of the things we saw with cloud was it's horizontally scalable and the app sat on top. Okay, that's SaaS. Makes a lot of sense. You want to use Dropbox? Yeah, that app sits on the cloud, Snowflake, whatever. Now you want horizontal scale with domain expertise. With massive low-latency accuracy.

Iman Bhattacharya

>> Yeah, absolutely.

John Furrier

>> This is unique. Talk about that piece because now you have the agents that, by the way, they'll be doing business logic too. They're going to be performing, they're going to be taking actionable steps. It's not just a prompt, get a return. You got work being done because now you got this new dynamic.

Iman Bhattacharya

>> Yeah.

John Furrier

>> Okay. Talk about that piece because this is— if you ground it in the data, Talk about why the knowledge layer works so well in that area.

Iman Bhattacharya

>> we are not trying to create another data silo here. We are trying to be an ecosystem. Listen, Knowledge Graph provides reasoning, contextual knowledge, and Data Foundation provides trust. And that's what is so important. We are trying to go to this connective tissue which connects between this trust to this enterprise context. So that's what we do.

John Furrier

>> What about accuracy when you have these specialized intelligence models?

Iman Bhattacharya

>> Yeah.

John Furrier

>> Now the role of the knowledge graph, how does that relate to accuracy? No one wants hallucinations.

Iman Bhattacharya

>> Absolutely right. And that's why your agent or your model is as good as your data is, right? Your agent will start to provide output and it will hallucinate. It will never say no. We have seen that it will provide synthetic results. But ultimately what matters is you produce something, your output is something which is good and traceable. Let me touch upon one point. I think you talked about ROI at some point of time. So all these enterprise accounts, all this big pharma in the drug discovery space, ultimately what the goal is, whether they're able to go from point A to point B as fast as they can, provide accurate indications faster than everybody else. And that has to be fully grounded in traceable data. So I think if you have this human-curated knowledge, every point is traceable to its own root. It's not like synthetically designed. And that's what is important when choosing the model, what data foundation you have.

John Furrier

>> And how does that tie into the agentic layer? We've heard conversations here I've had on theCUBE where it's like, okay, agents belong to a human, some say, but the traceability to the function, the workflows, and they're crossing domains too. So you kind of have a sovereign vibe going on there. So, that's another big thing.

Iman Bhattacharya

>> Absolutely. But I have to say that for us, every data point is traceable back to its origin, right? And because it is ultimately human curated and everything is well documented, so we go by all this.

John Furrier

>> You guys lock down that explainability.

Iman Bhattacharya

>> Absolutely.

John Furrier

>> 100%.

Iman Bhattacharya

>> Absolutely.

John Furrier

>> You guys, you trace it down to where you have the checks and balances. How does that affect the agents? Does it make it more positive for them?

Iman Bhattacharya

>> For sure, for sure. We have— give us probably 2 weeks to come up with something. I know I don't want to make an announcement when we are not supposed to, but then you can see when we are compared against some of the other models how accurate we are. We have shown this to some of the enterprise people and they were just like, oh, how can we use that? The conversation shifted. The data accuracy and tell us more about that. How can we own that? Right. So we are seeing that the trust part is coming more and it really comes from the ground when the data is grounded is actual knowledge foundation.

John Furrier

>> Yeah, the human curated. So do you recommend that people do human curation and do a mix and match of it or some machine-based or all machine? What would be your advice?

Iman Bhattacharya

>> I don't really have advice on this part because I'll tell you this, we are moving at light speed at this point of time, right? We are discovering this drug discovery agent. It has been there for a long time, but the way things are moving, every 6 months there is something newer and newer coming. So we have to just be here and wait and watch. But having said that, the knowledge foundation would always be the primary part of it, right? Somebody has to watch what are we getting? All of these pharma companies, all of these drug discovery people, they don't have any dearth of data, but they have to get something which is more contextualized. So that's what is very important.

John Furrier

>> Yeah, they get the signal out of the noise there. Let me ask you a personal question, actually more of a targeted question around your role. How has your role and your teams changed? Because one of the things we're hearing not only at this event but just in general in this area is that things are happening faster. 4-month projects are done in 4 days. So people are, one, excited by that. It doesn't get drawn out. So projects are moving faster. Teams are working together because they have to, because you have a lot of cross-domain stuff going on, even at the C-suite. CFOs are involved, human resources involved, not just CIO or CISO, of course, security and tech. But you have now this human layer of interactions because the humans in the loop, in this case curating or just driving the AI, have to work together differently.

Iman Bhattacharya

>> Oh, absolutely.

John Furrier

>> What's your take on this? Because this is kind of like— I mean, everyone says teamwork. I know that's kind of a cliché, but this is actually true cohesiveness. You need to have teamwork.

Iman Bhattacharya

>> Yeah, ultimately I would say on top of this, all of this, what resides is trust, right? And it doesn't come naturally by showing some data, or showing some quantitative model. It comes by actually doing teamwork, right? When all the foundation pieces are there, like security, what you just mentioned, and then data foundations and grounded in truth, that's where it comes in. For our team, it's like it's a full ecosystem starting from the developer from the engineer to the cybersecurity to the product owner to the product management to marketing to when we are actually going to market with our entire workforce. It's everybody works together, but we are also learning at this point in time.

John Furrier

>> It's interesting. You might not see it, but I'll just say it. You have a very high IQ on the whole data piece because the business that you're in, a lot of enterprises that aren't in these high discovery areas that are so acute on the data and the value of graphs are like, wow, this stuff works great. And they're having breakthroughs because they were grinding database stuff, whether it's structured tables, SQL queries. So you have now an awakening in the broader tech scene of the role of graphs. So the number one question that people ask me is, okay, how do I— where does it fit in and how do I run it? What do I do? So there's a lot of discovery. What would be your take on advice for someone watching in terms of what the impact could be and how to look at knowledge layers and how they should just either dip their toe in the water or jump right in? Do you take a workload? Do you— what would be your advice?

Iman Bhattacharya

>> Yeah, I would say wherever you are working in the space of drug discovery, whether you are in early discovery, biomarker identification, target discovery, indication expansion up to clinical trial, and you are working on one particular piece and your ultimate goal is to go from point A to point B, to make it like what does success look like, reducing timelines, improving accuracy, and come up with something novel faster than ever, right? Do take a look at our QIAGEN Discovery Platform at the knowledge layer. You can go to digitalinsights.qiagen.com and you can find that and reach out to us, give it a try, tell us about your project and we'd be happy to work with you.

John Furrier

>> How are people working with your offering? Take us through some of that. Things that you've discovered, surprises, and then kind of things you saw playing out. How are people engaging? And that's all.

Iman Bhattacharya

>> Gotta do that one more, once again.

John Furrier

>> How are your customers using the product? You got the insights, explain what your offering is.

Iman Bhattacharya

>> Yeah, so essentially our customers offer it the way they like, whatever their requirement. When it comes to this data science offering to the biopharma or biotech areas, we have a managed service team. They work with them to understand their requirement and provide them exactly what they need. Now we will be launching our MCP access very soon. So all these big pharmas and big companies, they will have a way to access that. And a few months from now they will be able to have agentic access using the Discovery Explorer. Right. Having said that, I think many people who are listening, they would also know one of our legacy products, Ingenuity Pathway Analysis or IPA. Do take a look at one of the agent capabilities that's coming out in a couple of weeks. I don't want to make an announcement over here, but do take a look at it and you should be able to—

John Furrier

>> That brings up my final question. What's next for you? What's your focus? You're here. A lot of great sessions going on. What's your focus? What are you optimizing for?

Iman Bhattacharya

>> So ultimately, I'll tell you this, right? We at our company, and I'll speak for myself, we are all driven by the ultimate success. What does it look like when we bring up a novel target and when a pharma company is able to bring a drug to market? When you see the success, that's really motivating, right? And we are here to really help that entire drug discovery continuum. What are we doing? We have been building, as I mentioned in the beginning, we've been building this knowledge foundation for almost 3 decades, right? But we are trying to come up with a way how people can access this smoothly, right? With this Lightspeed we are moving too. So watch out for our 2 major announcements. One would be our agent access. Second would be our MCP access in the next couple of weeks.

John Furrier

>> Yeah, get more agents working. Iman, thanks for coming on. I appreciate you sharing your perspective and what you're working on. And congratulations.

Iman Bhattacharya

>> Thank you.

John Furrier

>> I'm John Furrier with theCUBE. We are here at GraphTalk in New York City where the practitioners and the leaders are getting together to look at the future of where AI's gonna take their data. More importantly, if they have a lot of data, been curating it, like in this case, this is more superpower. Compounds the value and the faster time to get things done. It's a high-velocity environment. We're doing our part to keep up. Thanks for watching.

Iman Bhattachary, QIAGEN | Neo4J GraphSummit NYC 2026

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John Furrier

>> Palo Alto Studio Connection, Silicon Valley and Wall Street. I'm John Furrier, co-host here with Dave Vellante, my co-host. Hello, I'm John Furrier with theCUBE. We are here in New York City for GraphSummit NYC with Neo4j. Obviously graph databases, knowledge graphs, ontologies, the hottest conversation in the AI infrastructure layer of data as it brings a lot of value. Our next guest, Iman Bhattacharya, he's the senior global product marketing at QIAGEN, a biotech company who is— obviously one of the benefits of all the GPUs and the compute is the breakthroughs. Iman, thanks for coming. Appreciate you stopping by theCUBE here. Appreciate it.

Iman Bhattacharya

>> Thanks for having me.

John Furrier

>> So talk about what you guys do, because I think this is illustrative. You're a perfect candidate for the AI chat because we're seeing tons of breakthroughs in science with supercomputing AI infrastructure. But the data is only as good as the data you feed in. So explain what you guys do.

Iman Bhattacharya

>> Absolutely. Thank you for asking. I am from QIAGEN Digital Insights, which is the data part of QIAGEN, the bioinformatics arm. We cater to 3 different customers: research customer, discovery research, clinical, and also data science and the biopharma part of it. Specifically today, we are here for the data science part of it. And then what you just mentioned is just like your model is as good as your quality of data. So I think while everybody is talking about AI and the model layer, we deeply focus on the knowledge foundation and the knowledge content of it. And we believe that AI is here to stay. AI is here to elevate yourself, but it has to be deeply grounded in human curation.

John Furrier

>> The AI infrastructure with NVIDIA, we've covered them like a blanket.

Iman Bhattacharya

>> Yeah.

John Furrier

>> You're seeing hospitals, science, pharmaceuticals, biotech, all winning with the new horsepower they got. But you've always had a lot of data. Explain the data landscape that you guys are dealing with and what's changed the most in the AI era we're in now.

Iman Bhattacharya

>> See, I would say that data, as in the foundational knowledge, is the connective tissue between that AI model and this enterprise and these accounts, right? And everything is changing. Models are changing, agents are changing. They are getting more intelligent, but they're as good as your knowledge foundation, right? It has to be fully grounded in knowledge, which has to have proper provenance, it has to be traceable, and it has to be properly governed. So that's what we do. Yeah.

John Furrier

>> Yeah. And the agent conversation is, well, they're off the rails. We got to have governance. The observability, the explainability piece is huge. Reliability is huge. Safety is a big conversation now. I have to ask, it's not like you guys woke up one day and said, hey, we got to get some data to use AI. You had data. So going into this AI revolution, how have you guys looked at this data opportunity? You mentioned some of the knowledge layers you're building. Is it compounding? Is the intelligence helping? How does that— how is that working? Take us through the mindset of a day in the life of, okay, wow, we got this superpower called computing. We got all this data. How do you guys look at this and where does that knowledge graph and context fit in?

Iman Bhattacharya

>> I'm glad you asked this question. It's not just that today, QDI has been here for a long time, but our group has been manually and expertly curating this data for over 25 years. It's going to be 3 decades, right? If you go to Stanford University, if you just go to their Discovery Walk, you will see our Ingenuity Knowledge Graph is etched in the timeline, right? So we have been doing this for close to 3 decades almost. And when I say what we do, it's just like we have over 150 MD-PhD level experts. They manually curate the data. Ultimately, our goal, it has to be context-driven. Without context, it's nothing. That's what I would do.

John Furrier

>> So you guys pioneered the manual side of it, and people who know what that means know how hard it is.

Iman Bhattacharya

>> Absolutely.

John Furrier

>> Um, so I have to ask you, what's it like now? Is it— wow, are you leveraging the models to set up more faster knowledge banks? How has it changed? Because you have the experience of building them, you're seeing the tooling's getting easier these days, starting to hear the word ontology kicked around. Alex Karp from Palantir talks about ontologies like it was discovered yesterday. That's also been around since the '80s. So Ontologies aren't new. Yeah, knowledge graphs aren't new, but they were kind of pioneered by the early adopters, early drivers. Now you have a new scale of AI. Talk about that transition and kind of what's happening now.

Iman Bhattacharya

>> I mean, don't get me wrong, AI may be a new word or maybe a little buzzword, but we might be known for manually curated or human curated or expert curated. Knowledge, but we are always leveraging the machine learning techniques, right? Which is the actual AI things where you talk about, we use NLP process to actually derive this data and feed it into the system, right? So that's what we do. AI is here. We are very AI pro company, but that is grounded in knowledge, which gives you context.

John Furrier

>> Andy, what's the new additions that come to the table with AI because we're hearing unstructured data, structured data, we have relationship maps in the knowledge graphs, you have a great recursive capability which plays well with AI on the science side. What's going on there? How do you see that relevance piece of it and where does it kind of plug in?

Iman Bhattacharya

>> I'll give you an example. 6 months ago, we launched this product called QIAGEN Discovery Platform, which is a 3-tier product, essentially, if you can imagine the bottom layer is grounded in knowledge foundation, which is that over 25 years of knowledge foundation. The middle layer is the MCP or the Model Context Protocol, the way you access it. And the top layer is the Discovery Explorer, which is an agentic layer, right? That's what we have built. We are very excited to go to market with that one and with our newest capability with MCP. But that's what is happening. So it's not just data or it's not just knowledge foundation. It's actually a complete system. The goal is not to make it siloed. The goal is to make it be a part of that ecosystem.

John Furrier

>> It's interesting you mentioned silo because one of the things we saw with cloud was it's horizontally scalable and the app sat on top. Okay, that's SaaS. Makes a lot of sense. You want to use Dropbox? Yeah, that app sits on the cloud, Snowflake, whatever. Now you want horizontal scale with domain expertise. With massive low-latency accuracy.

Iman Bhattacharya

>> Yeah, absolutely.

John Furrier

>> This is unique. Talk about that piece because now you have the agents that, by the way, they'll be doing business logic too. They're going to be performing, they're going to be taking actionable steps. It's not just a prompt, get a return. You got work being done because now you got this new dynamic.

Iman Bhattacharya

>> Yeah.

John Furrier

>> Okay. Talk about that piece because this is— if you ground it in the data, Talk about why the knowledge layer works so well in that area.

Iman Bhattacharya

>> we are not trying to create another data silo here. We are trying to be an ecosystem. Listen, Knowledge Graph provides reasoning, contextual knowledge, and Data Foundation provides trust. And that's what is so important. We are trying to go to this connective tissue which connects between this trust to this enterprise context. So that's what we do.

John Furrier

>> What about accuracy when you have these specialized intelligence models?

Iman Bhattacharya

>> Yeah.

John Furrier

>> Now the role of the knowledge graph, how does that relate to accuracy? No one wants hallucinations.

Iman Bhattacharya

>> Absolutely right. And that's why your agent or your model is as good as your data is, right? Your agent will start to provide output and it will hallucinate. It will never say no. We have seen that it will provide synthetic results. But ultimately what matters is you produce something, your output is something which is good and traceable. Let me touch upon one point. I think you talked about ROI at some point of time. So all these enterprise accounts, all this big pharma in the drug discovery space, ultimately what the goal is, whether they're able to go from point A to point B as fast as they can, provide accurate indications faster than everybody else. And that has to be fully grounded in traceable data. So I think if you have this human-curated knowledge, every point is traceable to its own root. It's not like synthetically designed. And that's what is important when choosing the model, what data foundation you have.

John Furrier

>> And how does that tie into the agentic layer? We've heard conversations here I've had on theCUBE where it's like, okay, agents belong to a human, some say, but the traceability to the function, the workflows, and they're crossing domains too. So you kind of have a sovereign vibe going on there. So, that's another big thing.

Iman Bhattacharya

>> Absolutely. But I have to say that for us, every data point is traceable back to its origin, right? And because it is ultimately human curated and everything is well documented, so we go by all this.

John Furrier

>> You guys lock down that explainability.

Iman Bhattacharya

>> Absolutely.

John Furrier

>> 100%.

Iman Bhattacharya

>> Absolutely.

John Furrier

>> You guys, you trace it down to where you have the checks and balances. How does that affect the agents? Does it make it more positive for them?

Iman Bhattacharya

>> For sure, for sure. We have— give us probably 2 weeks to come up with something. I know I don't want to make an announcement when we are not supposed to, but then you can see when we are compared against some of the other models how accurate we are. We have shown this to some of the enterprise people and they were just like, oh, how can we use that? The conversation shifted. The data accuracy and tell us more about that. How can we own that? Right. So we are seeing that the trust part is coming more and it really comes from the ground when the data is grounded is actual knowledge foundation.

John Furrier

>> Yeah, the human curated. So do you recommend that people do human curation and do a mix and match of it or some machine-based or all machine? What would be your advice?

Iman Bhattacharya

>> I don't really have advice on this part because I'll tell you this, we are moving at light speed at this point of time, right? We are discovering this drug discovery agent. It has been there for a long time, but the way things are moving, every 6 months there is something newer and newer coming. So we have to just be here and wait and watch. But having said that, the knowledge foundation would always be the primary part of it, right? Somebody has to watch what are we getting? All of these pharma companies, all of these drug discovery people, they don't have any dearth of data, but they have to get something which is more contextualized. So that's what is very important.

John Furrier

>> Yeah, they get the signal out of the noise there. Let me ask you a personal question, actually more of a targeted question around your role. How has your role and your teams changed? Because one of the things we're hearing not only at this event but just in general in this area is that things are happening faster. 4-month projects are done in 4 days. So people are, one, excited by that. It doesn't get drawn out. So projects are moving faster. Teams are working together because they have to, because you have a lot of cross-domain stuff going on, even at the C-suite. CFOs are involved, human resources involved, not just CIO or CISO, of course, security and tech. But you have now this human layer of interactions because the humans in the loop, in this case curating or just driving the AI, have to work together differently.

Iman Bhattacharya

>> Oh, absolutely.

John Furrier

>> What's your take on this? Because this is kind of like— I mean, everyone says teamwork. I know that's kind of a cliché, but this is actually true cohesiveness. You need to have teamwork.

Iman Bhattacharya

>> Yeah, ultimately I would say on top of this, all of this, what resides is trust, right? And it doesn't come naturally by showing some data, or showing some quantitative model. It comes by actually doing teamwork, right? When all the foundation pieces are there, like security, what you just mentioned, and then data foundations and grounded in truth, that's where it comes in. For our team, it's like it's a full ecosystem starting from the developer from the engineer to the cybersecurity to the product owner to the product management to marketing to when we are actually going to market with our entire workforce. It's everybody works together, but we are also learning at this point in time.

John Furrier

>> It's interesting. You might not see it, but I'll just say it. You have a very high IQ on the whole data piece because the business that you're in, a lot of enterprises that aren't in these high discovery areas that are so acute on the data and the value of graphs are like, wow, this stuff works great. And they're having breakthroughs because they were grinding database stuff, whether it's structured tables, SQL queries. So you have now an awakening in the broader tech scene of the role of graphs. So the number one question that people ask me is, okay, how do I— where does it fit in and how do I run it? What do I do? So there's a lot of discovery. What would be your take on advice for someone watching in terms of what the impact could be and how to look at knowledge layers and how they should just either dip their toe in the water or jump right in? Do you take a workload? Do you— what would be your advice?

Iman Bhattacharya

>> Yeah, I would say wherever you are working in the space of drug discovery, whether you are in early discovery, biomarker identification, target discovery, indication expansion up to clinical trial, and you are working on one particular piece and your ultimate goal is to go from point A to point B, to make it like what does success look like, reducing timelines, improving accuracy, and come up with something novel faster than ever, right? Do take a look at our QIAGEN Discovery Platform at the knowledge layer. You can go to digitalinsights.qiagen.com and you can find that and reach out to us, give it a try, tell us about your project and we'd be happy to work with you.

John Furrier

>> How are people working with your offering? Take us through some of that. Things that you've discovered, surprises, and then kind of things you saw playing out. How are people engaging? And that's all.

Iman Bhattacharya

>> Gotta do that one more, once again.

John Furrier

>> How are your customers using the product? You got the insights, explain what your offering is.

Iman Bhattacharya

>> Yeah, so essentially our customers offer it the way they like, whatever their requirement. When it comes to this data science offering to the biopharma or biotech areas, we have a managed service team. They work with them to understand their requirement and provide them exactly what they need. Now we will be launching our MCP access very soon. So all these big pharmas and big companies, they will have a way to access that. And a few months from now they will be able to have agentic access using the Discovery Explorer. Right. Having said that, I think many people who are listening, they would also know one of our legacy products, Ingenuity Pathway Analysis or IPA. Do take a look at one of the agent capabilities that's coming out in a couple of weeks. I don't want to make an announcement over here, but do take a look at it and you should be able to—

John Furrier

>> That brings up my final question. What's next for you? What's your focus? You're here. A lot of great sessions going on. What's your focus? What are you optimizing for?

Iman Bhattacharya

>> So ultimately, I'll tell you this, right? We at our company, and I'll speak for myself, we are all driven by the ultimate success. What does it look like when we bring up a novel target and when a pharma company is able to bring a drug to market? When you see the success, that's really motivating, right? And we are here to really help that entire drug discovery continuum. What are we doing? We have been building, as I mentioned in the beginning, we've been building this knowledge foundation for almost 3 decades, right? But we are trying to come up with a way how people can access this smoothly, right? With this Lightspeed we are moving too. So watch out for our 2 major announcements. One would be our agent access. Second would be our MCP access in the next couple of weeks.

John Furrier

>> Yeah, get more agents working. Iman, thanks for coming on. I appreciate you sharing your perspective and what you're working on. And congratulations.

Iman Bhattacharya

>> Thank you.

John Furrier

>> I'm John Furrier with theCUBE. We are here at GraphTalk in New York City where the practitioners and the leaders are getting together to look at the future of where AI's gonna take their data. More importantly, if they have a lot of data, been curating it, like in this case, this is more superpower. Compounds the value and the faster time to get things done. It's a high-velocity environment. We're doing our part to keep up. Thanks for watching.