INDUSTRIES / PHARMA & LIFE SCIENCES
AI for Pharma & Life Sciences
We build production AI for pharma and life-sciences teams where failure is not optional — GxP workflows shipped with electronic signatures, immutable audit trails, and a human reviewer on the critical path. Not a demo. A validated system.
Use cases: Regulated-lab audit intelligence; CAPA & deviation triage; Batch record review; LIMS / ELN / MES data completion; Regulatory document drafting; Pharmacovigilance intake.
Compliance and constraints handled: 21 CFR Part 11, GxP validation, HIPAA & PHI, Data residency, Human-in-the-loop, Explainability.
Can AI be used in FDA-regulated GxP environments? Yes — when it is built for validation from day one. Our pharma work ships with electronic signatures, immutable audit trails, validated change control, and IQ/OQ/PQ artifacts. The model is constrained to narrate evidence the system already flagged, with a human reviewer on the critical path.
How do you handle 21 CFR Part 11 compliance? Every write action is Part 11-signed and every model call is logged with its prompt, response, and model version so an audit can be reconstructed. Change control, electronic signatures, and immutable logs are designed in before implementation, not added later.
Where does our data live, and do you train on it? In your environment. We deploy into your tenant and cloud region with client-owned infrastructure, least-data design, and PHI/PII redaction at the edge. Your regulated data does not leave your boundary, and we do not train on it.
How long until a pharma workflow is in production? A typical engagement reaches first production batch in about eight weeks: weeks 1–2 map the real GxP workflow, weeks 3–4 ship a validated vertical slice, weeks 5–6 harden for audit, and weeks 7–8 deploy and transfer the runbook.
Does this replace our scientists? No. The system amplifies expert judgment — it catches anomalies earlier and reclaims review hours, but scientists accept, reject, or annotate every recommendation. In the field, explainability earned adoption: reviewers trusted a 92%-precision model they could inspect over a 99% black box they could not.
Which pharma workflows are the best fit for AI? Workflows with a measurable bottleneck, existing subject-matter experts, and a clear production definition: regulated-lab audit intelligence, CAPA and deviation triage, batch record review, LIMS/ELN/MES data completion, regulatory drafting, and pharmacovigilance intake.
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