Reimagining Pharma Commercialization with AI Agents

A Q&A with Imtiaz Samjoo, EVERSANA, and Kevin Kallmes, Nested Knowledge. 

EVERSANA and Nested Knowledge have teamed up to revolutionize systematic literature reviews (SLRs) using AI. Their collaboration is streamlining workflows and unlocking faster, more precise insights. We sat down with Imtiaz Samjoo, Vice President, Evidence Synthesis, EVERSANA and Kevin Kallmes, CEO, Nested Knowledge, to explore how this partnership is reshaping evidence generation in life sciences. 

Imtiaz Samjoo, EVERSANA 

What inspired EVERSANA to partner with Nested Knowledge on AI-driven SLRs?
At EVERSANA, innovation is at the heart of everything we do. Over the past decade, health economics and outcomes research (HEOR) and evidence synthesis (ES) have undergone dramatic transformations—driven by advancements in technology, methodology and the growing need for timely, reliable insights.

SLRs are essential to our work, but traditionally they’ve been slow and labor-intensive. AI and machine learning have revolutionized this work, enabling automation in screening and data extraction. That’s when we saw an opportunity to collaborate with Nested Knowledge. 

Their platform offered a powerful way to enhance our capabilities across HEOR/ES, medical affairs and market access. It automates key steps—search, screening, data extraction and synthesis—while preserving rigor and transparency. 

How has this impacted your team and clients?
The integration has substantially reduced the time needed to complete SLRs. Our teams now deliver richer, more current insights—faster. It’s not just about speed; it’s about scalability and precision, especially in patient-focused commercialization and health outcomes analysis.

We’re proceeding thoughtfully, guided by our clients and health technology assessment (HTA) bodies, to determine where and when to scale these AI solutions more broadly. 

Any challenges along the way?
One of the biggest hurdles is ensuring transparency in how AI is used. We’ve had to ensure every AI-driven step is well-documented—from prompts to parsing logic to quality checks. Initiatives like the Cochrane Collaboration and PRISMA group have fostered global partnerships, promoting the sharing of data and methodologies as well as providing regular updates on conduct and reporting in the era of machine learning and AI. Full automation isn’t feasible yet due to the complexity of textual data and the need for expert interpretation. But even partial automation is already transforming how we work. 

Kevin Kallmes, Nested Knowledge 

What makes your platform a great fit for EVERSANA?
Our platform supports the entire SLR workflow—from literature search to synthesis—with AI-enhanced tools built for accuracy and traceability. EVERSANA’s focus on innovation and patient-centered solutions made them a perfect partner. 

From day one, we saw the potential to elevate their already world-class offering—and that’s exactly what we’ve done. Our suite includes AI-powered search, smart screening, structured data extraction, and synthesis tools—each designed to tackle different challenges in the review process. So, for every critical step EVERSANA’s experts take, we have a ‘human-in-the-loop’ automation to accelerate and scale up vital research tasks. 

How do you maintain scientific integrity while using AI?
Transparency and auditability are built into our system. Every AI-assisted step is traceable back to the source data. And we emphasize human oversight—our tools support expert judgment, not replace it. That balance is essential for scientific rigor. 

What’s next for the partnership?
We’re exploring deeper integrations like real-time data abstraction and expanding use cases across therapeutic areas. The goal is to make evidence synthesis not just faster—but smarter. We’re also excited to share our journey at upcoming events like ISPOR EU. 

Want to learn more about how EVERSANA is accelerating commercialization with AI?  

Through the EVERSANA AI Accelerator we are partnering with best-in-class companies such as Google, Amazon and now Nested Knowledge to solve real-world life sciences challenges and create a better experience for patients.  Learn more .