Certara (CERT) investor relations material

Strategic realignment and growth engines

• Company reorganized into two main segments: ACE (Accelerating Clinical Evidence) and MID3 (Model-Informed Drug Development), aiming to simplify operations and drive growth opportunities.

• ACE focuses on improving data management and efficiency from protocol to submission, leveraging assets like Phoenix, Pinnacle, CoAuthor, and GlobalSubmit.

• MID3 emphasizes computational biology and biosimulation to transform clinical development, with significant investments in discovery and new growth areas.

• Realignment aims to unify operations, enhance specialty-led client engagement, and foster direct scientist-to-client interactions.

• Operational and sales teams are now aligned to support these segments, with a focus on clarity, accountability, and mid-term growth.

Sales strategy and market opportunities

• Sales incentives and structures have shifted to prioritize new software sales and ARR as key metrics, with strong Q1 results and ongoing momentum.

• Scientific teams are now directly involved in opportunity generation and client engagement, aiming to drive innovation and long-term growth.

• The addressable clinical trial market is estimated at $230 billion, with regulatory tailwinds supporting expansion into new applications like pediatrics and organ impairment.

• Peer-to-peer scientific engagement is seen as the main driver for client adoption and expansion within accounts.

• Execution challenges in services were addressed by breaking down silos and incentivizing collaboration between commercial and scientific teams.

AI integration and product innovation

• AI is being embedded across all products, with initiatives like Certara IQ, CODEx, D360 re-platforming, and cloud-based solutions.

• The Vyasa acquisition accelerated an AI-first mindset, with Dr. Krishnan Raman appointed Chief AI Officer to unify data layers and optimize product integration.

• AI is used to enhance workflow, reporting, and decision-making, with a focus on leveraging regulatory knowhow and data assets.

• Native AI products and modules are being developed, making it increasingly difficult to distinguish between AI and non-AI offerings.

• AI tools are also being used internally to drive productivity and offset R&D investments.