Use case 1 · Trial enrollment acceleration
AI cohort matching against EHR data at academic medical centers and IDNs. Lifts enrollment 3–5x. Common in oncology, rare disease, complex chronic conditions.
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AI Engineering for Pharma Clinical Operations
Clinical trial AI, AI-driven cohort matching, regulatory submission document generation, real-world evidence pipelines, and pharmacovigilance AI for pharmaceutical companies. Built by the team that’s been integrating with clinical systems since 2013.
$45K Discovery · $95K Production-Ready · $145K Pilot-Ready
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📌 Definition
Pharma Clinical AI is the engineering of AI features that accelerate pharmaceutical clinical operations — trial design, patient recruitment, protocol authoring, regulatory submission generation, real-world evidence (RWE) production, and pharmacovigilance signal detection. Modern 2026 pharma AI deployments require BAA-eligible inference paths, 21 CFR Part 11-compliant audit trails, ICH GCP-aligned documentation, FDA/EMA submission patterns, and HIPAA-compliant integration with EHR data sources for cohort matching and RWE. Productized fixed-price tiers: $45K (4-week Discovery), $95K (8-week Production-Ready), $145K (12-week Pilot-Ready).
What we build for pharma companies
- 01
AI cohort matching for clinical trials
LLM-driven patient eligibility matching from EHR data. Lifts trial enrollment 3–5x at academic medical centers and integrated delivery networks.
- 02
Trial protocol authoring AI
AI-assisted protocol drafting from prior trials in the therapeutic area. Citation-grounded, references the institution’s protocol library, reviewed by clinical operations.
- 03
Regulatory submission document generation
AI-drafted clinical study reports (CSRs), investigator brochures, IND/NDA module 2 summaries, and EMA submission documents. Reviewer-in-the-loop.
- 04
Real-world evidence (RWE) pipelines
Production data pipelines combining EHR data, claims data, and registry data into RWE-grade datasets. De-identification, cohort definition, outcome tracking.
- 05
Pharmacovigilance AI
AI-driven signal detection from adverse event reports, EHR data, and social listening. ICSR (Individual Case Safety Report) drafting assistance.
- 06
Medical writing AI
AI-assisted drafting of medical writing deliverables — manuscripts, abstracts, posters, congress presentations, and field medical materials.
- 07
eCRF and ePRO infrastructure
Electronic case report forms, electronic patient-reported outcomes, ePRO/eCOA collection. 21 CFR Part 11 compliance, audit trail, EDC integration.
- 08
Investigator portal
Web portal for clinical trial investigators — protocol access, patient enrollment tracking, query management, regulatory document distribution.
- 09
Site selection AI
AI-driven trial site selection based on historical site performance, patient demographic match, principal investigator track record, and competing trial density.
Pharma Clinical AI pricing · 2026
What’s included in every pharma engagement
- HIPAA-compliant architecture from day 1 — encryption at rest and in transit, role-based access, audit logging
- BAA-covered AI providers (OpenAI, Anthropic, AWS Bedrock, Google)
- 21 CFR Part 11 compliance for clinical trial systems
- ICH GCP-aligned audit trail patterns
- ICH E6(R3) compliant documentation
- EDC integration (Medidata Rave, Veeva Vault, Oracle Clinical One)
- EHR integration (Epic, Cerner-Oracle, Athena, Allscripts) via FHIR R4 plus HL7 v2
- De-identification pipelines for RWE generation (Safe Harbor and Expert Determination methods)
- Computer system validation (CSV) deliverables
- IQ/OQ/PQ validation documentation
- Pre-signed BAA templates with major cloud and AI providers
- SOC 2 Type II and HITRUST audit-ready documentation
Common pharma AI use cases
Use case 2 · Regulatory submission acceleration
AI-drafted CSRs, investigator brochures, IND/NDA module 2 summaries. Compresses submission timelines 20–40%. Reviewer-in-the-loop.
Use case 3 · Real-world evidence generation
Production RWE pipelines for post-market surveillance, label expansion studies, and payer value dossiers. Combines EHR, claims, and registry data.
Use case 4 · Pharmacovigilance signal detection
AI-driven adverse event signal detection from spontaneous reports, EHR data, and social listening. ICSR drafting assistance, MedDRA coding suggestions.
Use case 5 · Medical writing automation
AI-assisted drafting of manuscripts, abstracts, posters, and congress presentations. Citation-grounded, references institutional content library.
Production reality
Ship pharma AI that survives FDA, EMA, and GCP auditors
Free 30-min architecture call. We’ll scope your AI use case, regulatory pathway, and the right tier for your therapeutic area.
FAQs
FAQ
An LLM with RAG over patient chart data (with appropriate de-identification or BAA coverage) screens eligible patients against trial inclusion/exclusion criteria. Output is a ranked list of candidates with the supporting evidence cited from chart context. Clinical research coordinators review and confirm. Production deployments lift enrollment 3–5x and cut screening time by 60–80%.
Yes — when reviewer-in-the-loop. FDA and EMA permit AI-assisted drafting of submission documents as long as the responsible medical writer reviews, edits, and signs. The audit trail must show who drafted what and who approved. We build that audit trail into every regulatory submission AI engagement.
Every pharma AI engagement we ship to production includes 21 CFR Part 11-compliant audit trails, electronic signatures, access controls, and validation documentation. We’ve shipped engagements that passed FDA Bioresearch Monitoring (BIMO) audits and EMA GCP inspections.
Yes. We integrate with Medidata Rave, Veeva Vault, Oracle Clinical One, Castor EDC, and most major EDC platforms via their REST APIs or ODM-XML exports. We also integrate with eCRF, ePRO, and IRT systems as needed.
Two patterns: (1) Safe Harbor de-identification per HIPAA §164.514(b)(2) — removal of 18 specified identifiers, applied before AI processing, or (2) Expert Determination per HIPAA §164.514(b)(1) — statistical de-identification certified by a qualified statistician. We support both methods and document the chosen approach for each engagement.
CSV is the GxP validation process for computerized systems used in regulated activities. IQ (Installation Qualification), OQ (Operational Qualification), and PQ (Performance Qualification) deliverables document that the system is installed correctly, operates as intended, and performs reliably in production. Our $30K validation package add-on delivers IQ/OQ/PQ documentation aligned with GAMP 5 standards.
4-week Discovery, 8-week Production-Ready, 12-week Pilot-Ready. Full production deployment with CSV validation and regulatory documentation adds 16–32 weeks. RWE pipelines depend on data source complexity — typically 12–24 weeks for production-grade pipelines.