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How CSL Uses GitLab Duo AI Agents in Biotech R&D

Gitlab
10/04/2026
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product manager for SDLC platforms at CSL. Sometimes conversations about software quality can be pretty abstract. At CSL, it's not abstract. It's the difference between a therapy reaching a patient safely and not reaching them at all. CSL develops therapies for people living with rare and serious diseases, which means their software has to be as uncompromising as their science. That's exactly the kind of problem we built KitLab to solve. Welcome Sree. Tell us a little bit about yourself and what your team does at CSL. Thank you, Bill. Great to be here. I'm Sreekanth Ramachandran. I work for a biotech company named CSL, and I lead the SDLC platform team. We develop life-saving therapies for people living with rare and serious diseases and so as a pharma company, as you can imagine, our mission is all about our patients. Wow, what an awesome mission. I mean, you're literally saving people's lives. Exactly, and that's a huge motivation for all of us at CSL. When we have a bad day, we just have to take one step back and just think about why we're doing what we're doing. My team, by introduction about what we do, we are focused on building that foundational engineering capability that enables all of our product teams across CSL so they can deliver software efficiently, securely, and more importantly, with compliance, because we have to comply with a lot of regulations. So that's key for us. It's one of the really exciting parts of being part of the GitLab ecosystem, I think, is software is really instrumental in helping so many causes around the world, in your case, saving people's lives. So I want to actually hear a little bit more about how GitLab fits into your engineering organization day-to-day. How do we help you? What we have seen is over the last couple of years, our GitLab users have just doubled, right? Like we just have more developers just working on many things across CSL. And so over time, what has now happened is GitLab has become like a key solution as part of our organization's platform strategy. And so as we are seeing more teams kind of building products and delivering continuously, what's key is now consistency is just super important, right? And we want our developers focused on actually solving problems and not solving tooling problems and bringing tools together and focusing on processes. And so GitLab is actually bringing many of those development activities in a single experience. It has become the foundation for source code management. It has become like the standard for many of the CI, CD that we do. And also now with AI, right? Like GitLab helping us kind of embrace AI really well, right? And final point I'll make is because we are a regulated company that the trust, the visibility, like traceability aspects like that is just super important, right? So GitLab is helping us with those governance and controls that ensures that we are able to deliver at speed as well, right? And at the scale of our company. Speaking of AI, I've heard you've had some great success with Duo Agent Platform, our AI solution. Can you tell us a little bit about what you've built and kind of the results so far? Yeah, sure. Right now it's directly tying into our strategic objectives for CSL. It is part of our transformation roadmap. And so as more teams are, like what we are seeing is, you know, we have product teams now building a lot more than before. And that's because we want to use that to be a strategic differentiator for us. Because if we are able to build things faster than our competition, that differentiates us. So that's like a super critical priority for us. And so productivity is just important. And that's where AI fits in, right? And what we are seeing is teams are just embracing AI. And it has become part of our development life cycles, right? No doubt about that. And what Duo has enabled us, we've been able to use agents within Duo a lot, right? Developers are using various agents across the life cycle. And we've also seen, similar to the GitLab users, in just the last two months, we have seen Duo users double in just two months. And not only that, our credit consumption is also increasing significantly, which is a challenge though, right? Like it does add up, adds up to the cost, but I'm hoping tools like features like Orbit kind of can optimize that a bit. That's amazing to hear. You're using agents across the life cycle and I'm seeing the speed benefits of that. And thank you for doubling the number of Duo users over the last two months. That's an even faster velocity than your GitLab adoption. One of the key tenants to our investments is to make sure that the ROI is really there for your spend. And you mentioned GitLab Orbit there, that's just one of many projects where we're focused on ensuring that our customers get the right context to ensure that the cost of tokens can go down and the quality of agent outcomes can go up. And that combined with the level of budget controls and usage visibility that we give our customers is a really important priority for us. So let's keep partnering on that together to make sure the ROI is there for your spend. Can you tell us a little bit about what made you decide to choose on Duo Agent Platform right now and how it's different than competitors? Yeah, sure. I think primarily it's part of GitLab, right? So that's just step one, right? And it integrates well into our IDE and into our code base. So that just by default helps us so the developers is always connected to Duo and Duo has the knowledge behind the scenes as well. And now, you know, I spoke about GitLab Orbit, right? Like that now really helps us give us the knowledge graph that we need to not only optimize tokens, but also give us better quality output, right? So these two have been like critical factors. So what's exciting while challenging, right? You know, we have both happening at the same time is at CSL, we are seeing a convergence of multiple priorities like any other organization, right? I mentioned about the modern technology monetization we are undergoing, but at the same time, we are also becoming a more product-centric organization while we are adopting AI. So we have multiple things happening at the same time. And so the challenge is how do our product teams take advantage of all these opportunities without being slowed down by process bottlenecks, by tooling barriers, right? AI is just going to be there, but we are heavily focused on foundations right now. Sree, thank you so much for joining me today. It's been awesome talking to you. As we wrap up, you're building software to save people's lives. Tell us a little bit about your vision for how that plays out for the next few years to come. Sure. At CSL, we are good at what we do, right? So we have a lot of process domain experts who are great at R&D, great at supply chain topics within a pharma company, great with manufacturing, and so on and so forth. AI will be a key differentiator, right? Where we can, our process domain experts can use AI as that strategic differentiator for CSL to bring life-saving therapies faster to our patients, to develop drugs, finish R&D in a much faster time than before. If our platform is able to do that, and along with AI, we will be able to deliver for our patients much faster and with quality. Amazing. You know what gets me excited about what you're saying there, Sree, is first, the role that GitLab can play in helping your teams increase innovation velocity and speed while maintaining control and the quality that you need to serve your patients. But then there's this secondary effect on the world that we both get to enjoy, which is real difference in patient outcomes. You know, people who are struggling with really serious diseases having a higher quality of life. What an incredible mission to partner on. Thank you so much for choosing GitLab. For sure. I appreciate it. Thank you for the time.

TL;DR

  • CSL standardized on GitLab as its single DevSecOps platform for source code management, CI/CD, and regulatory compliance governance across all engineering teams.
  • GitLab Duo users at CSL doubled in just two months, with AI agents now embedded across the full software development lifecycle to accelerate developer productivity.
  • GitLab Orbit's Knowledge Graph is being used to optimize token efficiency and improve AI agent output quality, helping CSL manage rising credit consumption costs.
  • CSL's long-term vision is to use AI as a strategic differentiator that enables domain experts in R&D, supply chain, and manufacturing to bring life-saving therapies to patients faster.

CSL's Mission-Driven Platform Strategy

This executive interview features GitLab CEO Bill Staples in conversation with Sreekanth Ramachandran, Product Manager for SDLC Platforms at CSL, a global biotherapeutics company developing therapies for rare and serious diseases. Ramachandran explains how his team's mandate is to build foundational engineering capability that enables all product teams across CSL to deliver software efficiently, securely, and in full regulatory compliance. Over the past several years, GitLab has evolved from one tool among many into the central platform underpinning CSL's source code management, CI/CD pipelines, and enterprise governance — eliminating tool sprawl and freeing developers to focus on solving business problems rather than managing fragmented toolchains.

Rapid AI Adoption and the Duo Agent Platform

Ramachandran describes how AI adoption at CSL has accelerated dramatically, with GitLab Duo users doubling in just two months — a faster adoption velocity than even GitLab's own user growth at the company. Developers are actively using AI agents embedded across the software development lifecycle, and credit consumption is rising significantly as a result. He highlights GitLab Orbit's Knowledge Graph as a key mechanism for optimizing token efficiency and improving the quality of agent outputs, which is critical for managing the cost implications of scaled AI usage. The ability to build faster than competitors is framed as a direct strategic differentiator in the highly competitive biopharmaceutical R&D landscape, where speed to therapy matters for patients.

Chapters

0:00 - Software Quality in Biotech
0:39 - CSL's SDLC Platform Mission
2:01 - GitLab as the Engineering Foundation
3:33 - AI Adoption with Duo Agents
5:08 - ROI, Tokens & GitLab Orbit
6:11 - Why CSL Chose Duo Over Competitors
7:48 - Long-Term Vision for AI in R&D
8:40 - Closing Remarks

Key Quotes

1:32 "We are focused on building that foundational engineering capability that enables all of our product teams across CSL so they can deliver software efficiently, securely, and more importantly, with compliance, because we have to comply with a lot of regulations."
4:08 "We want to use that to be a strategic differentiator for us. Because if we are able to build things faster than our competition, that differentiates us."
4:48 "In just the last two months, we have seen Duo users double in just two months. And not only that, our credit consumption is also increasing significantly."
6:37 "GitLab Orbit — that now really helps us give us the knowledge graph that we need to not only optimize tokens, but also give us better quality output."
8:13 "Our process domain experts can use AI as that strategic differentiator for CSL to bring life-saving therapies faster to our patients, to develop drugs, finish R&D in a much faster time than before."

FAQ

Why did CSL choose GitLab Duo over competing AI coding tools?

Ramachandran cites native integration as the primary factor — because Duo is built into GitLab, developers are always connected to it through their IDE and codebase without additional configuration. GitLab Orbit's Knowledge Graph further differentiates Duo by providing contextual intelligence that improves output quality and optimizes token usage, which is critical at CSL's scale of consumption.

How does CSL balance rapid AI adoption with strict regulatory compliance requirements?

CSL relies on GitLab's built-in governance, traceability, and audit controls to maintain compliance while scaling AI usage. The platform's unified approach means that compliance guardrails are embedded in the same environment where development happens, rather than bolted on as a separate process — allowing teams to move quickly without bypassing regulatory requirements.

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