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Rajipo / Industries / Pharma

AI for the teams behind the molecule.

Research signals, documentation burden, operational load — agentic AI that respects how regulated your world is, from a firm trained in the discipline of clinical environments.

Research · documentation · operations

Pharma work is documentation-dense, evidence-bound and audited. That suits us: our agents cite every claim, block what they can’t source, and keep a trail a reviewer can reconstruct — the same rigor we learned deploying into clinical settings.

  • Citation-safe drafting
  • Reviewer-in-the-loop
  • Audit-ready trails
Compound
agent-trace · pharma · illustrative

10:02:11 lit.agent → overnight sweep · 214 abstracts triaged

10:02:13 signal.agent → 6 papers flagged · relevance ≥ 0.9

10:02:14 docdraft.agent → summary drafted · every claim cited

10:02:15 guardrail.cite → 1 uncited claim blocked · returned to draft

10:02:17 reviewer.human → scientist approved · 2 edits captured

10:02:18 trace.audit → sources and decisions stored

overnight eval.suite → 38 known-answer cases replayed · pass

Where AI pays off

The work agents can carry in pharma.

01

Literature & signal triage

Overnight sweeps across abstracts and sources — ranked, cited and ready for a scientist’s morning review.

02

Documentation drafting

Sections assembled with every claim sourced; uncited claims are blocked, not published.

03

Pharmacovigilance intake

Case intake read, structured and routed — with human review gates where judgment matters.

04

SOP & quality copilots

Answers grounded in your controlled documents, with references — not memory.

05

Trial operations support

Site queries, scheduling and status chased by background agents, not coordinators’ evenings.

06

Deviation & CAPA triage

Incidents structured, similar cases surfaced, drafts prepared for quality review.

How engagements run here

The rules of the road, in your domain.

  • Every generated claim carries its source — uncited output is blocked by guardrail
  • Human review gates are designed into the workflow, not appended to it
  • Your documents and data never train outside models without written agreement

Systems we meet

  • Document management
  • Quality systems
  • Literature sources
  • Spreadsheets & data stores

Pharma

Bring us the documentation burden or the research backlog — we’ll show you what agents can carry.

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