What Our Series B Means for Our Customers

Today, Faro announced a $37.3 million Series B financing, co-led by Merck Global Health Innovation Fund and S32, with participation from all of our existing investors and new investors including Ankona Capital.

We are grateful for the support, but the more important story is what our customers are trying to make possible.

Across biopharma, development teams are beginning to move beyond using AI for isolated tasks. The opportunity is increasingly about how AI agents can support complex workflows across clinical development, helping teams move from study design through execution and the many downstream activities required to advance a medicine.

That is a much harder problem than generating content.

Clinical development is built on thousands of interconnected scientific, medical, regulatory and operational decisions. A change in one part of a study can affect eligibility criteria, site operations, data collection, statistical analysis, regulatory documentation and downstream systems.

For AI to participate meaningfully in those workflows, it needs more than access to documents. It needs to understand the intent behind a development program.

Faro started with the clinical protocol because it sits at the center of so many downstream development activities.

But our goal was never simply to make protocol authoring faster.

We have spent years building proprietary clinical development data models that represent the concepts, relationships, constraints and intent behind a study in a structured, machine-readable way. That allows software and AI agents to reason across clinical development processes rather than treating each document or task as an isolated interaction.

Documents remain important outputs. They are just not the underlying system of record.

That distinction becomes increasingly important as AI moves from assisting with individual tasks to taking on more complex workflows.

An agent helping draft a section of a protocol is useful. An agent that understands why an endpoint was selected, how that decision affects the schedule of activities, what data needs to be collected, what downstream systems need to be configured, and what regulatory and operational constraints need to be preserved, is something different.

That requires a shared model of the work itself.

Our customers are already pushing us in this direction.

Faro is now used by six of the ten largest pharmaceutical companies in the world. That gives us the opportunity to learn from teams working across a broad range of therapeutic areas, study designs and development workflows.

What we are hearing consistently is that the ambition for AI is getting larger. Teams are thinking across study design, protocol authoring, study build, execution, data flows, review and the downstream work required to move a development program forward.

They do not always describe the destination in the same way, and the transition will not happen all at once. But the direction is clear.

The value of AI in clinical development will increasingly come from its ability to understand context across workflows, reason over connected decisions and take action while maintaining the oversight required in a regulated environment.

That is the foundation we are building with our customers.

This Series B gives us the resources to move faster.

We will continue expanding Faro's agentic AI capabilities and accelerating our work with customers deploying agents across their development organizations. The goal is to help development teams improve both the quality and efficiency of the processes required to move medicines from first-in-human studies through approval.

Over time, we believe the same foundation can extend further.

The underlying challenge is not unique to a protocol, a document or even an individual stage of development. Biopharma is full of complex workflows where scientific intent, structured data, operational decisions and regulatory requirements need to remain connected.

If AI is going to take on more of that work, it needs a way to understand those connections.

That is the problem we are focused on solving.

The most important thing we have learned is that this cannot be built in isolation.

Our customers have taught us where the complexity really lives. They have challenged our assumptions, pushed us into harder problems and helped us understand what it will take for AI to operate reliably inside real development organizations. This financing allows us to keep doing that at a greater scale.

We are excited about the technology, but even more excited about what development teams will be able to do with it.

There is a lot more to build.