IntuitionLabs
Castor EDC AI integration with Claude, GPT and Gemini for clinical data management

Castor EDC AI Integration & MCP Agents

Connect frontier AI models to Castor via REST API and MCP. Validated AI workflows for study build, medical coding, RBQM, query generation and eConsent — built for 21 CFR Part 11 and ICH E6(R3) GCP.

Castor AI Workflows We Deliver

High-ROI AI use cases for Castor that ship to production fast, run inside a GxP framework, and integrate cleanly with the rest of the clinical stack.

Study Build
AI Edit-Check Authoring
Extract endpoints, eligibility criteria, derivations and safety logic directly from the approved protocol and propose Castor eCRF structures, edit checks and ePRO instruments. Cuts study build time by 30-50% while preserving full UAT and change control.
Discuss study build
Data Management
Auto-Coding & Query AI
Automatic MedDRA and WHODrug coding with confidence scoring, AI-generated query text, query prioritization, and automatic protocol-deviation detection — all surfaced inside the data manager's normal workflow.
Talk to data management
Monitoring
AI-Powered RBQM
Anomaly detection across sites, subjects and forms aligned to the TransCelerate RBQM framework and FDA risk-based monitoring guidance. Central monitors get a ranked, explained flag list rather than a static dashboard.
Plan RBQM rollout

API-First Castor Meets Tool-Using AI

The Castor REST API exposes Study, Record, DataPoint, SurveyPackage and User resources behind OAuth 2.0 — the exact shape that modern tool-using AI agents need. We wrap it in either a Model Context Protocol server for Claude or a function-calling adapter for GPT and Gemini, so any frontier model can read, propose changes and (with human approval) execute them inside Castor. Every call is logged with prompt, response, model version and authenticated user — satisfying the audit-trail expectations of 21 CFR Part 11.

Castor EDC REST API wrapped as MCP tools for Claude and other frontier AI models

Validated AI Inside Your GxP Framework

AI in clinical operations is not just a productivity feature — it is a configuration item under your quality system. We treat every AI workflow as a GAMP 5 Category 5 custom application, with version-controlled prompts, model registry, evaluation harness, periodic review and full validation pack. The frontier model itself is treated as a qualified supplier subject to ongoing monitoring under FDA AI considerations for drug and biological products. The result is AI that holds up in an FDA BIMO inspection.

Castor AI workflows validated under GAMP 5 Category 5 with human-in-the-loop controls

Multi-System Orchestration, Not Just EDC

The highest-leverage AI workflows in clinical operations span systems. We orchestrate Castor with Oracle Argus for SAE reporting, Florence eHub for site documents, Suvoda for randomization, Snowflake or Databricks for cross-study analytics, and the sponsor CTMS / eTMF stack. A single AI agent can pull from all of them under one identity, one audit trail and one validation framework — eliminating the brittle copy/paste that still dominates many clinical workflows.

AI agent orchestrating Castor with safety, IRT, eTMF and analytics systems across the clinical stack

Castor AI Capabilities We Build

Validated AI workflows that ship to production fast, run inside your existing GxP framework, and survive an inspection — covering study build, data management, monitoring, safety and consent.

AI Study Build

Protocol-to-Castor build automation: extract schedule of assessments, eligibility criteria, derivations, edit checks and ePRO instruments and propose Castor study artifacts. Human approval required before UAT, full traceability from protocol to eCRF.

Plan AI build

AI MedDRA / WHODrug Coding

Automatic coding of adverse events, medical history and concomitant medications to MedDRA and WHODrug with confidence scoring, escalation to medical coder review for low-confidence cases, and full audit logging of every coding decision.

Discuss auto-coding

AI Query Generation

Natural-language query authoring that explains the discrepancy to the site, suggests the likely correction, and prioritizes the data manager backlog. Reduces query authoring time and improves site response rates.

Talk to data management

RBQM Anomaly Detection

Anomaly detection across sites, subjects and forms using query, ePRO, eConsent and audit trail signals. Aligned to TransCelerate RBQM, FDA risk-based monitoring guidance and the ICH E6(R3) risk-based quality management model.

Plan RBQM rollout

eConsent Amendment Differencing

AI-driven semantic diff across ICF versions that classifies changes as material vs cosmetic and only triggers re-consent when patient-facing meaning changes — reducing re-consent fatigue and IRB workload.

Discuss eConsent AI

Natural-Language Data Search

Castor MCP server that lets clinical operations teams query their study database in plain English (filtered by role, masked appropriately and fully logged). Answers cite the specific records and timestamps they came from.

Try MCP search

AI vs Traditional Castor Workflows

Traditional Castor operations rely on heavy manual lift across data management, monitoring and study build. AI-enhanced workflows preserve every regulatory control while collapsing cycle times — and they degrade gracefully because every action still requires human approval.

Study Build

Manual: 8-14 weeks of eCRF and edit-check authoring. AI-assisted: 4-7 weeks with the same UAT and change control.

Medical Coding

Manual: per-term review by coder. AI-assisted: confidence-scored auto-coding with coder review on low-confidence cases.

Monitoring

Manual: static dashboards and routine on-site visits. AI-assisted: ranked anomaly list with natural-language explanation.

Compliance Guardrails on Every Castor AI Workflow

✋

Human-in-the-Loop on Data Mutations

Any AI action that creates, updates or deletes clinical data requires an authenticated user signature with printed name, date/time and meaning of signature aligned to 21 CFR Part 11 §11.50 / §11.70.

🛡️

Zero-Retention Model Endpoints

All Castor data flows through enterprise endpoints (Anthropic, OpenAI, Google Vertex) with zero-data-retention contractual terms. PHI is de-identified where Safe Harbor or Expert Determination applies under HIPAA.

🗂️

Version-Controlled Prompts & Models

Every prompt, system message and model version is checked into Git and treated as a configuration item under change control. Model swaps are validated workflow-by-workflow, never silently rolled out to production.

📜

Full Audit Trail

Every model call logs prompt, output, model ID, model version, authenticated user, timestamp and any Castor record touched — joinable against the Castor audit trail for FDA BIMO, EMA and MHRA inspections.

📋

GAMP 5 Category 5 Validation

AI workflows are validated as GAMP 5 Category 5 custom applications with URS, risk assessment, IQ/OQ/PQ, traceability matrix, periodic review and supplier monitoring of the underlying frontier model vendor.

🇪🇺

EU AI Act & DPIA Coverage

Every workflow is mapped to its EU AI Act risk classification and to a GDPR Article 35 Data Protection Impact Assessment where personal data is in scope — refreshed on a defined cadence.

Castor AI Integration FAQ

AI integration with Castor is built on the documented Castor REST API, secured via OAuth 2.0 and rate-limited by the published X-RateLimit thresholds. We wrap the API in either a Model Context Protocol (MCP) server so frontier models like Anthropic Claude can call Castor tools directly, or a function-calling adapter for OpenAI GPT and Google Gemini. Every model call is logged, prompts are version-controlled, and any AI action that mutates clinical data triggers a human-in-the-loop confirmation aligned to 21 CFR Part 11 signature and audit requirements.
The Model Context Protocol is an open standard, introduced by Anthropic in late 2024 and rapidly adopted across the industry, that lets AI assistants discover and call tools, resources and prompts from external systems through a single, standardized interface. For Castor, an MCP server turns the REST API into a set of typed tools (list_studies, get_record, run_query, code_term_meddra, generate_edit_check) that any MCP-compatible AI client can use safely. The benefit is portability: the same MCP server works with Claude Desktop, Claude in pharma workflows, internal AI agents and future models, without per-vendor adapter code. IntuitionLabs builds and validates Castor MCP servers with audit logging, role mapping and human-in-the-loop guardrails baked in.
The highest-ROI AI workflows on Castor today are: (1) AI-assisted edit-check and derivation authoring from the protocol, which cuts study build time by 30-50%; (2) automatic medical coding to MedDRA and WHODrug with confidence scoring; (3) risk-based monitoring anomaly detection against the TransCelerate RBQM framework; (4) AI-generated query text and prioritization to reduce data manager workload; and (5) eConsent amendment differencing so re-consent only triggers when material changes occur. Each of these is implemented as a validated AI workflow on top of the Castor REST API, with prompts version-controlled and outputs logged for inspection.
AI workflows in GxP contexts have to satisfy the same controls as any other computerized system. We design every Castor AI workflow to meet 21 CFR Part 11 and EU Annex 11: prompts and model versions are version-controlled in Git and treated as configuration items under change control; every model call is logged with input, output, model ID, model version and user; data-modifying actions require an authenticated user signature; and the entire stack is validated under ISPE GAMP 5 as a Category 5 custom application (with the underlying frontier model treated as a qualified supplier subject to ongoing monitoring under FDA AI/ML guidance for drug and biologic submissions).
Yes — AI-assisted study build is one of the strongest Castor AI use cases. We feed the approved protocol PDF (and SAP, where available) into a workflow that extracts the schedule of assessments, eligibility criteria, endpoints, derivations and safety reporting requirements, and proposes Castor eCRF structures, fields, edit checks and ePRO instruments. A clinical data manager reviews and approves each generated artifact inside the Castor UI before it goes into UAT. This is a clear productivity win, but it does not bypass validation — every proposed configuration goes through the same change control and UAT process you would run for a manually built study. FDA draft AI guidance for drug and biological products informs how we document AI involvement in the build.
AI RBQM workflows on Castor pull structured signals from the REST API — query rates, screen-failure rates, ePRO completion timing, eConsent timing, protocol deviation density, audit trail patterns — and surface anomalous sites, subjects and forms that warrant central monitor attention. AI adds two things over a static dashboard: anomaly detection (sites whose patterns deviate from the cohort norm without an obvious cause) and natural-language explanation of why each flag was raised. Central monitors triage the flagged list inside their normal workflow, and on-site visits become targeted rather than routine — aligned to the FDA risk-based approach to monitoring guidance and the risk-based quality management principles in ICH E6(R3).
AI workflows on clinical data have to respect HIPAA in the US, GDPR in the EU/EEA, the UK GDPR/Data Protection Act and equivalent regulations worldwide. We route Castor data only through enterprise frontier-model endpoints with zero-data-retention policies — Anthropic Enterprise, OpenAI API zero-retention, or Google Vertex AI with no-training contractual terms. Where required, we de-identify direct identifiers before prompts leave the regulated environment, using the HHS Safe Harbor method or Expert Determination depending on the use case. All AI traffic is logged for inspection and PHI flows are documented as part of the Data Protection Impact Assessment per GDPR Article 35.
We are model-agnostic and routinely deploy Anthropic Claude, OpenAI GPT and Google Gemini depending on the workflow. In our experience, Claude tends to outperform on long-context regulatory document understanding (protocols, SAPs, regulatory letters), GPT remains the most flexible for general orchestration, and Gemini brings strong cost-efficiency and multimodal capability when imaging or video are involved. For pharma deployments we always route through the enterprise endpoint with zero retention and never the public consumer surface. Model selection is documented as a configuration decision in the validation pack, and we run periodic A/B evaluations on the workflow level so models can be swapped without re-validating the surrounding workflow.
Hallucinations are a real risk in any LLM-driven workflow on clinical data, and our design assumes they will occur. We mitigate at three levels: (1) retrieval-augmented generation grounded in actual Castor data via the REST API, so the model is summarizing real records rather than inventing them; (2) human-in-the-loop confirmation for every action that creates, updates or deletes data — the AI proposes, a human approves; (3) automated evaluation harnesses that score model outputs against gold-standard reference cases on every prompt or model change, with regression thresholds enforced in CI/CD. This is the same evaluation discipline recommended in NIST's AI Risk Management Framework and applied to a regulated clinical environment.
A focused Castor AI workflow — for example, AI-assisted query generation or MedDRA coding for a single study — typically reaches a validated production state in 6-10 weeks. Broader programs that include an MCP server, multi-workflow orchestration and integration with other clinical systems generally run 12-20 weeks including validation. We deliberately scope projects so the first AI workflow ships to production fast and earns trust, then expand into adjacent workflows under a stable governance, validation and observability foundation rather than as one big AI rollout. This is the same incremental approach we apply across our AI enablement practice.
Castor itself has begun shipping AI capabilities (notably AI-assisted form build and protocol-driven study acceleration) and we recommend turning those on where they fit. Our work is complementary: we cover the broader set of AI use cases that depend on multi-system orchestration, frontier models, your own protocol corpus, your own dictionaries and your own validation framework — for example MedDRA/WHODrug coding tied to your safety database, RBQM tied to your sponsor-level risk register, or eConsent differencing tied to your IRB SOPs. We treat Castor's native AI and our custom AI as one validated capability stack rather than competing systems.
Data residency is handled at the cloud level — Castor offers EU and US hosting tiers, and we route AI traffic through region-matched endpoints (Anthropic, OpenAI and Google all offer EU-resident inference). For European deployments we also map every AI workflow against the EU AI Act risk classification — most clinical operations workflows fall outside the high-risk Annex III categories but transparency, logging and human oversight obligations still apply. We document the EU AI Act assessment as part of the validation pack and refresh it on a defined cadence, similar to how we handle GDPR DPIAs.
Ready to Add Validated AI to Castor?
Ready to Add Validated AI to Castor? image

Ready to Add Validated AI to Castor?

Talk to our team about a first AI workflow on top of Castor — scoped to ship fast, validated for GxP, and easy to extend into a broader AI program.

Book a Meeting

© 2026 IntuitionLabs. All rights reserved.

Privacy PolicyTerms of ServiceBook Meeting