AI for environments where every output must be controlled, explainable, source-linked, reviewable, and auditable. Not a product feature — an engineering philosophy and governance framework for regulated industries.
Generic AI is designed for general-purpose use. Regulated industries require a fundamentally different AI design philosophy.
AI retrieves from your organization's approved documents and data — not from open internet or opaque model training weights alone.
Outputs are generated within predefined templates and structures — reducing variability and enabling consistent review and validation.
AI operates within governed workflow states — not as a free-form assistant. What AI can generate, when, and in what context is controlled.
AI-generated outputs are reviewed, revised, and approved by qualified human experts before use in regulated contexts.
All AI outputs, drafts, and evidence are version-controlled — supporting traceability, review cycles, and controlled document management.
Role-based permissions determine who can create, review, approve, and access AI outputs — with audit trails recording every action.
Complete timestamped records of what was generated, who reviewed it, what was changed, and who approved — for every output.
Every recommendation, claim, and output is linked to specific source documents, records, or data points — not generated from nothing.
Platform design supports enterprise AI governance, customer validation documentation, and controlled deployment practices.
AI models built with Life Sciences and regulatory domain context — not generic language models applied without domain knowledge.
AI interactions are governed by controlled prompts, workflow state, user role, and approved content sources — not open-ended queries.
AI retrieves from your organization's approved document base — SOPs, protocols, data, quality records — not from uncontrolled external sources.
AI outputs are generated within predefined templates approved by your organization — ensuring structure, format, and content consistency.
Structured review stages ensure qualified experts assess AI-generated content before it is used in any regulated context or decision.
Every AI interaction, output, review, and approval is logged — creating the evidence trail needed to defend AI use in a regulated environment.
Every WHPL product applies the same Deterministic Regulated AI framework — but for a distinct regulated workflow challenge.
| Product | How Deterministic AI Is Applied | Key AI Governance Mechanism | Primary Regulated Domain |
|---|---|---|---|
| CRONOS | Controlled clinical data capture, AI-assisted quality checks, workflow-governed study operations | Structured EDC workflows, AI flags requiring human data review, complete audit trail | Clinical trial execution, BA/BE, eClinical operations |
| CronoLex | Template-governed CSR section generation from approved clinical inputs with source traceability | Template constraints, source-linked outputs, mandatory medical writing expert review | CSR generation, clinical reporting, medical writing |
| ProtocolGen | Template-governed AI drafting from study summaries using ICH M11-aligned structure, curated clause libraries, and TransCelerate CPT-style content organization | Organizational template governance, curated clause library constraints, structured collaboration and human-guided review and validation | Protocol creation from study summaries, clause consistency, institutional knowledge preservation, multi-stakeholder review workflows |
| ProofOS | Retrieval-grounded evidence mapping, continuous gap detection, structured proof pack generation | Source-only retrieval, AI-detected gaps for human QA review, full evidence audit trail | Inspection readiness, TMF QC, SOP evidence, CAPA closure — Life Sciences QA |
Request a regulated AI assessment for your specific workflow — clinical, quality, compliance, documentation, or evidence.