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Talk to us about agents for Life Sciences

30 minutes with a founder. No prep, no access to your systems, no pitch. Bring a process that eats your team’s time, or just curiosity.

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In production

Agents running inside life sciences operations today.

Commercial operations

A Fortune 500 US pharmaceutical company

Commercial campaigns moved through a five-stage process where requirements validation and technical design were manual and generated rework across the rest. Both are now agent-operated: requirements validated against compliance rules before entering the pipeline, technical flows generated from validated inputs. Intake, audience build and execution remain with the team, with approval at every handoff.

Campaign cycle time reduced 75%, from 141 days to 35.

Before
141
With agents
35

days per campaign

Requirements intake
Requirements validationAgent
Audience and data build
Technical flow generationAgent
Execution
Commercial analytics

A Fortune 100 life sciences company

Business users ask in natural language, typed or spoken, and receive governed answers with the underlying query exposed. Every request is resolved against the enterprise metadata graph, validated for safety and executed only against approved sources under the user's own permissions. One agent replaces the per-dataset assistants and the reporting queue behind them.

Self-serve analytics across HCP and campaign data, with the query exposed on every answer.

  1. 01Business question, typed or spoken
  2. 02Intent resolved against the metadata graph
  3. 03Query generated
  4. 04Executed safely against governed sources
  5. 05Result rendered in natural language
  6. 06Answer returned with the underlying query exposed
Production layer
  • Role-based access
  • Query safety validation
  • Monitoring and alerting
  • Automated regression testing
Manufacturing and supply

A US precision manufacturer serving pharmaceutical packaging and diagnostics

The context layer was deployed over the customer's ERP and warehouse management systems and the agents built against their operational data model, not a CRM abstraction of it. A pattern that transfers to any SAP S/4HANA estate: agents that read and act on the system of record.

Warehouse operations agents on the ERP layer, built against the customer's own operational data model.

Agents
Warehouse operations
Context layer
Entity resolutionMetadata graphSafe executionRetrieval
Source systems
ERPWarehouse management

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AI agents for life sciencesWhat the agents do, which systems they run on, and how they stay inside GxP.
How we deploy inside your environmentManaged SaaS, your own cloud account, or on-prem. The engineers who make it work.