IntuitionLabs
Florence Healthcare AI integration services for clinical research operations

Florence AI Integration — Connect Claude, GPT & Gemini to Your eRegulatory Platform

Validated AI workflows for regulatory packet QC, TMF auto-tagging, ICF amendment differencing, redaction, and natural-language binder search — built on the Florence REST API and webhooks with full 21 CFR Part 11 audit trail.

Where AI Adds Value in Florence

Florence holds the essential regulatory documents, source records, consent events, and monitoring artifacts that describe how your clinical trials actually run. AI applied to that data — with the right guardrails — turns manual document handling into continuous regulatory readiness.

Site Activation
Regulatory Packet QC
AI agents validate site activation packets against TMF Reference Model and study-specific requirements, flagging missing or expiring CVs, GCP certificates, licenses, and Form 1572 elements before activation. Human reviewers approve via Part 11 signature.
Discuss the use case
eTMF Alignment
TMF Auto-Tagging
Every uploaded binder document is automatically classified against the TMF Reference Model so essentials reach the correct zone without manual filing. Sponsor and CRO reviewers see proposed tags before commit, with full audit trail.
Plan a pilot
Protocol Amendments
ICF Amendment Differencing
Across protocol amendments, AI compares ICF versions line-by-line and flags risk-significant language changes for IRB submission, re-consent decisions, and translation prioritization. Saves days of manual diff work per amendment.
Talk to an expert

Built on the Florence REST API

We connect external AI models — Anthropic Claude, OpenAI GPT, Google Gemini — to Florence through its REST API and webhooks. Every AI action runs against authenticated API endpoints with study-scoped role permissions, leaving a full audit trail of prompts, model versions, retrieved context, and the human reviewer who approved each output. The integration layer is built with infrastructure-as-code so that audit artifacts are continuously version-controlled.

Florence Healthcare REST API connecting clinical research binders to external AI models

Validated AI Governance for GCP Workflows

AI features that influence GCP-regulated decisions must respect 21 CFR Part 11 and ICH E6(R3). We enforce that with pinned model versions, prompt change-control, retrieval-source versioning, human-in-the-loop signature on every regulated action, and a periodic AI system review tied to the NIST AI RMF and the EU AI Act for high-risk AI systems.

AI governance framework aligned to 21 CFR Part 11 and ICH E6(R3) for Florence Healthcare

Grounded in Your Florence Data

We index your protocols, ICFs, CVs, training certificates, monitoring letters, and deviation logs into a vector store such as Pinecone, OpenSearch, or Azure AI Search, with Florence role and study metadata preserved so the model only retrieves what the calling user is authorized to see. RAG keeps every model output anchored in citable binder records — no hallucinated essentials, no fabricated regulatory citations.

Retrieval-augmented generation grounded in Florence Healthcare binder data

AI Workflows We Build on Florence

Each workflow is delivered as a validated module with prompts under change control, model versions pinned, and full audit logging back into the Florence binder.

Regulatory Packet QC Agent

Validates site activation packets against TMF Reference Model and study-specific requirements. Flags missing or expiring CVs, GCP certificates, licenses, and FDA Form 1572 elements. Drafts a remediation checklist for the study coordinator before activation.

Plan a pilot

TMF Auto-Tagging Agent

Classifies every uploaded binder document against the TMF Reference Model with high accuracy, proposes the correct zone, extracts metadata (version, effective date, signatures), and routes to a human reviewer for Part 11-signed approval before commit.

Discuss the workflow

ICF Amendment Differencing

Compares ICF versions across protocol amendments line-by-line, flags risk-significant language changes for IRB submission, identifies re-consent triggers, and prepares a translation prioritization list for multi-language trials.

Talk to an expert

Document Redaction Agent

Prepares binder documents for sponsor sharing, regulator submission, or public clinical trial transparency requirements by identifying and redacting PHI and proprietary content with structured human review before release.

Plan a pilot

BIMO Inspection Prep Agent

Assembles inspection-ready binders and narrative responses for FDA BIMO and EMA inspections on demand. Pulls all relevant essentials, monitoring records, and deviations; flags gaps; produces a working pack for the QA team to finalize.

Discuss the workflow

Natural-Language Binder Search

Lets coordinators, monitors, and study managers ask plain-English questions across the binder — "Which Phase 2 sites are missing GCP refresher training due this quarter?" — with citations back to source records and Florence audit-trail logging.

Talk to an expert

How We Run AI Workflows Inside Florence

Every AI workflow we deploy on Florence follows the same validated pattern: retrieve from authorized records, ground the model in citable context, produce a draft, route to human approval, and log everything. AI never executes a GCP-affecting action autonomously.

Pinned Model Versions

Production workflows lock to specific model versions and dates. Upgrades go through change control with a frozen eval set re-run before promotion.

Human-in-the-Loop by Default

Every AI-generated output is reviewed and electronically signed by a qualified user with Part 11 meaning of signature captured in the audit trail.

Zero-Retention Endpoints

External API calls use enterprise zero-retention endpoints with regional hosting matched to your data residency obligations (US, EU, UK, APAC).

AI Use Cases Across the Trial Lifecycle

🚀

Study Start-Up

AI-assisted feasibility analysis, site identification scoring against historical performance, automated regulatory packet QC, and AI-driven IRB submission readiness checks before activation.

✅

Site Activation

Essential document classification and auto-tagging, automated reminders for expiring CVs and training, and AI-generated activation status dashboards for sponsors and CROs.

🔍

Remote Monitoring

Risk-based monitor prioritization using EDC, IRT, and binder signals; AI-summarized monitoring visit prep packs; and watermarked AI-redacted source data review.

📝

Protocol Amendments

ICF amendment differencing with risk-significance scoring, automated re-consent decision support, translation prioritization, and IRB submission packet preparation.

🛡️

Inspection Readiness

On-demand BIMO inspection prep agents, narrative response drafting, continuous self-audit against ICH E6(R3) expectations, and inspection-ready binder export.

🏁

Database Lock & Close-Out

Final TMF reconciliation between site ISF and sponsor TMF, automated essentials completeness scoring, and AI-assisted site close-out packet preparation.

Getting Started With Florence AI

Every Florence AI engagement starts with picking the right first use case — one with measurable ROI, a clear human-in-the-loop pattern, and a frozen evaluation set drawn from your actual binder history. We prefer narrow, deep first projects (regulatory packet QC or TMF auto-tagging) over broad transformation programs because they build the governance, monitoring, and trust patterns that every subsequent AI workflow inherits.

Our team has built and validated AI workflows across regulated life sciences platforms. We bring the validation expertise, AI policy and governance frameworks, and the model engineering depth that lets you deploy AI on Florence with confidence.

Typical Engagement Path

  • Use Case Workshop — 1-2 days to pick a first workflow with measurable ROI and clear human-in-the-loop pattern
  • Pilot Build — 8-12 weeks to validated production deployment of the first AI workflow
  • Governance Foundation — AI policy, governance committee, model risk management SOPs, eval framework
  • Workflow Expansion — Each subsequent AI workflow follows a 3-6 week additive cycle

Frequently Asked Questions

Florence exposes REST APIs and webhook subscriptions covering documents, binders, signatures, users, studies, and audit trails. We connect AI models in two patterns: event-driven (a Florence webhook fires when a new essential document is uploaded, triggering a serverless function that calls an AI model with the document, classifies it against the TMF Reference Model, extracts metadata, and writes the proposed tags back to the binder for human approval), and on-demand (a study coordinator clicks a button in a thin AI sidecar UI that pulls the relevant Florence record, builds a grounded prompt, and renders the AI response for human approval). Both patterns log every prompt, model version, and human decision back into the binder audit trail so the entire AI-mediated workflow is inspection-ready under 21 CFR Part 11.
We connect Anthropic Claude, OpenAI GPT and o-series, Google Gemini, Azure OpenAI, and open-weights models served via AWS Bedrock. Model choice depends on the task: Claude excels at long-form reasoning over multi-page protocol amendments and ICF differences; GPT-4o is strong at structured extraction from CVs, GCP training certificates, and FDA Form 1572s; Gemini handles multimodal tasks well (scanned PDFs, signed source documents); locally hosted open-weights models are reserved for environments where data residency rules prohibit external API calls. We benchmark model performance against a frozen evaluation set built from your historical Florence binder data before choosing a default.
AI features that influence GCP-regulated decisions must respect 21 CFR Part 11, EU Annex 11, and ICH E6(R3). Our integration pattern enforces this with several controls: every AI-generated output is logged with the source prompt, model version, retrieval context, timestamp, and the human reviewer who accepted or rejected it; AI never executes a GCP-affecting action autonomously — it produces a recommendation, and a qualified user signs off with a Part 11-compliant electronic signature that captures meaning of signature in Florence; model versions are pinned and changes are managed through formal change control. The FDA draft guidance on AI in drug and biological products (Jan 2025) and the FDA guidance on electronic records in clinical investigations shape our control framework.
The use cases with the strongest ROI tend to be: automated regulatory packet QC that flags missing or expiring CVs, GCP training certificates, medical licenses, and FDA Form 1572 elements before activation; TMF Reference Model auto-tagging on every uploaded document so essentials reach the right binder zone without manual filing; ICF amendment differencing across protocol versions with risk-flagged language changes for IRB submission; redaction agents that prepare documents for sponsor or regulator submission; AI-assisted essential document classification using both content and metadata; and natural-language search across binders. We sequence pilots so each use case builds the audit, monitoring, and governance pattern the next one inherits — turning AI from a one-off project into a repeatable capability.
RAG over Florence uses three layers. First, an indexing layer extracts binder documents — protocols, ICFs, CVs, training certificates, monitoring follow-up letters, deviation logs — from Florence via API and pushes them into a vector store such as Pinecone, OpenSearch, or Azure AI Search, with Florence role and study metadata preserved so the model only retrieves what the calling user is authorized to see. Second, a retrieval layer filters by binder zone, study, language, and effective date so retired protocol versions are never surfaced. Third, a generation layer grounds the model in retrieved passages with explicit citation back to the binder document and Florence record ID. Indexing runs on a schedule with delta updates triggered by Florence webhooks, so the AI is never working from stale data.
A typical first AI workflow on Florence can be delivered in 8-12 weeks: 2 weeks of use case definition and prompt engineering, 2 weeks of integration build (Florence API client, webhook handlers, secrets management, governance configuration), 2 weeks of validation including model evaluation against a frozen test set drawn from historical Florence data, 2 weeks of UAT in a Florence sandbox or staging tenant, and 2 weeks of validated production deployment with hypercare. Subsequent AI workflows are typically additive and follow a 3-6 week cycle once the governance framework is in place. We build the integration layer with infrastructure-as-code so that audit artifacts (prompts, model versions, retrieval indexes, evaluation sets) are continuously version-controlled alongside source code.
We define a frozen evaluation set of historical binder documents, ICF amendments, regulatory packets, and monitoring letters drawn from your Florence tenant, then measure model outputs against expert-labeled ground truth using structured metrics — accuracy, precision, recall, F1, calibration — and qualitative reviews scored by clinical operations SMEs. For long-form reasoning tasks like ICF amendment differencing, we use rubric-based scoring with LLM-as-judge cross-checked by humans on a 10% sample. Production telemetry tracks human acceptance rate, edit distance between AI draft and final approved output, and any cases where the human disagreed with the model — feeding back into model evaluation and prompt refinement. Drift detection re-runs the eval set on every model version change and triggers revalidation if performance degrades beyond a defined threshold. The NIST AI Risk Management Framework guides our monitoring program.
Yes — we build bounded agents using frameworks like the Anthropic Agent SDK and OpenAI Assistants, plus custom orchestration for multi-step workflows. Clinical-focused agents we have built include site activation packet QC agents, TMF auto-tagging agents, ICF amendment differencing agents, redaction agents, and audit preparation agents. Critically, every agent runs with a tightly scoped tool set against the Florence API, logs every tool call and reasoning step for audit, and never executes a GCP-affecting write autonomously — instead it assembles a proposed change (e.g. a draft regulatory packet, a proposed TMF tag) and routes it to a human for review and electronic-signature approval before commit. The Model Context Protocol is increasingly the standard we use to expose Florence capabilities to agents in a controlled way.
Clinical trial documents frequently contain PHI — subject identifiers, signed consents, source documents with names and dates of birth. For external models, we use zero-retention enterprise endpoints from each provider — Anthropic enterprise zero-retention, Azure OpenAI with abuse-monitoring opt-out where eligible, AWS Bedrock with the AWS data residency commitment. Regional model hosting is selected to match data residency requirements (US, EU, UK, APAC). PHI is masked before leaving the platform when not needed for the task, and free-text fields are scanned for inadvertent PHI before being sent to any external model. HIPAA, GDPR, and CCPA obligations are flowed through to model providers via DPAs and BAAs reviewed during supplier qualification.
We help clients establish an AI governance committee with clinical operations, IT, regulatory, legal, and security representation, modeled after the structure recommended by the NIST AI RMF and the EU AI Act for high-risk AI systems. Governance artifacts include an AI use case registry with risk classification, a model risk management policy, prompt and model change-control SOPs, an AI incident response procedure, and a periodic AI system review cadence. We also help draft an AI policy that names the systems where AI is permitted (Florence being one of them), the human-in-the-loop requirements per use case, and the escalation path when AI behavior deviates from expectations. See our AI policy and governance services.
Yes — inspection preparation is one of the most pragmatic AI use cases on Florence. We build agents that assemble inspection-ready document packets on demand: pulling all binder essentials, training records, monitoring visit reports, deviation logs, and source documents relevant to a given FDA BIMO inspection scope; generating a draft narrative response to anticipated inspector questions; and flagging gaps that should be addressed before the inspection. The agent never modifies regulated records — it produces a working pack that the QA and clinical operations team reviews and finalizes. This pattern has compressed inspection prep from weeks to days for several sponsors, and the same agent powers continuous self-audits against the FDA risk-based monitoring guidance between formal inspections.
Costs break into three components: implementation (one-time fixed-scope project), AI inference costs (consumed via the model providers, typically a few hundred to a few thousand dollars per month per workflow at sponsor or CRO scale), and ongoing managed services (monthly retainer covering model evaluation, prompt iteration, and integration health). Inference costs scale with the volume of regulated documents and the chosen model — a regulatory packet QC workflow on a mid-size sponsor with 200 site activations per year will cost meaningfully less than a TMF auto-tagging workflow on a large CRO with thousands of monthly document uploads. We model expected costs against historical Florence document volumes during the discovery phase so there are no surprises at production scale.
Ready to Layer AI Onto Your Florence Tenant?
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Ready to Layer AI Onto Your Florence Tenant?

Book a discovery session to pick a first AI workflow, design the governance framework, and chart the path from manual document handling to continuous regulatory readiness.

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