How Purposa Works
Every engagement begins with a confidential conversation. We meet you where your AI compliance exposure lives.
TIER 1
Know your exposure before the FDA does.
Independent audit of the AI tools and clinical datasets your organization uses in drug development — evaluating performance across all patient populations with regulatory rigor.
Ready to understand your exposure? Request a confidential consultation.
TIER 2
Turn findings into forward progress.
We partner with your R&D, regulatory, and data science teams to close representation gaps and build equitable AI practices into your development pipeline.
Ready to move from findings to action? Request a confidential consultation.
TIER 3
Make compliance a competitive advantage.
Purposa builds the governance infrastructure that makes AI compliance sustainable — positioning your organization ahead of evolving FDA requirements before they become mandatory.
Ready to build the standard? Request a confidential consultation.
NEW
Built for AI that acts, not just answers.
Autonomous AI is already executing clinical workflows across pharma and biotech. We audit the data, design, and decision logic behind these systems before they reach regulators.
Operating agentic AI in drug development? Request a confidential consultation.
Our approach
Purposa Health audits run as governed loops — not autonomous AI. Each phase has a defined input, defined logic, a documented output, and a hard stop before the next phase begins. The result is a finding your regulatory team can defend, not a recommendation your team has to trust blindly.
01
Inventory
02
Mapping
03
Analysis
04
Risk
05
Remediation
06
Monitoring
Detailed framework documentation available upon engagement.
Request a Confidential ConsultationWho we serve
Deploying AI tools fast and need independent validation before regulatory deadlines hit.
Patient advocacy partners are questioning whether your AI reflects the patient populations your drug is designed to treat.
An IND or NDA submission includes AI-assisted analysis. You need a defensible representation and performance narrative before it goes to FDA.
FDA has asked about population performance in your AI models. You need a peer-level expert answer — fast.
Your enrollment commitments are documented. Your AI tools are not in that picture yet. That is a representation gap regulators will find.
Evolving FDA and EU AI guidance is raising the bar on AI in drug development. You need documented evidence of representation and performance now.
The mandate
The FDA's draft AI credibility framework and the January 2026 FDA–EMA Guiding Principles establish the expectation that AI tools supporting a regulatory decision demonstrate credibility for their context of use — including equitable performance across patient populations. Sponsors are accountable for the tools they deploy, not just the data they generate. Final FDA guidance is anticipated and will sharpen these expectations.
The EU AI Act sets audit, transparency, and ongoing-monitoring expectations for high-risk AI systems, including clinical decision support. Under agreements now moving through formal adoption, obligations for these systems phase in through 2027 and 2028 — giving sponsors a defined window to build defensible governance before the requirements bind. Transparency obligations begin applying earlier.
Regulator-required enrollment commitments for clinical trials create explicit linkage to the AI tools driving enrollment, patient matching, and protocol design. Most sponsors have not closed this representation gap yet.