AI Agent Development for Pharmaceuticals & Life Sciences

We build agents that plan, call real tools, and know when to stop and ask a human. That last part is what separates a system you can put in front of customers from one that stays in a sandbox.

In pharmaceuticals & life sciences, GxP validation means every system change needs documented evidence before it reaches production. That single fact reshapes how ai agent development has to be built here, the guardrails, the approval points and the evidence trail are design inputs rather than things bolted on before go-live.

The workload we are most often asked to take on first is adverse event intake and coding, usually integrated against eTMF. Every engagement opens with a measurement: the cycle time, the cost per transaction, or the error rate we are being asked to move.

Multi-model by default, so a provider outage is a routing decision rather than an incident. Six weeks to something running in production, not six quarters to a strategy document.