AI that an inspector can audit.

In regulated life sciences, an unauditable model is a liability regardless of accuracy. We build AI systems with validation protocols, immutable trails, and change control native to GxP — useful and inspectable.

Discuss your use case →What we build

quantpi · industry/telemetry

$ industry.constraints()
✓ frame: GxP · 21 CFR Part 11 · GAMP 5 · EU Annex 11
✓ deployment: cloud · hybrid · air-gapped
✓ proof standard: measured on your data
# domain constraints are design inputs, not blockers

IND/01What's at stake

The constraint isn't ambition. It's evidence.

Pharma and healthcare organizations aren't short of AI use cases — they're short of AI systems that survive validation. Every model touching regulated data or decisions needs documented intended use, performance qualification, and a change-control story that covers retraining. Build that in from the start and AI in GxP is not just possible — it compounds.

IND/02What we build here

Use cases we ship

Regulated document intelligence

Batch records, SOPs, validation protocols, submission documents — classified, extracted, and searchable with page-level citations and full audit trails.

validation: IQ/OQ/PQ

Clinical & lab data pipelines

Ingestion and harmonization of clinical, LIMS, and instrument data with lineage that answers ‘where did this number come from’ instantly.

lineage: end-to-end

Pharmacovigilance triage

AI-assisted adverse-event intake and prioritization with human adjudication and complete decision logging — throughput without losing the trail.

oversight: human-adjudicated

Quality & deviation analytics

Pattern detection across deviations, CAPAs, and complaints — surfacing systemic issues quarters before trend reports do.

signal: cross-system

Submission & dossier assembly

Drafting and consistency-checking support for regulatory submissions, with every generated passage traceable to source content.

traceability: per-passage

Validated RAG for SOPs

Ask-the-SOP systems for manufacturing and quality teams — grounded answers, cited sections, refusal when the SOP is silent.

grounding: cited or refused

IND/03Domain constraints we design for

Built for your constraints

Computer system validation (CSV/CSA) with IQ/OQ/PQ for AI components
21 CFR Part 11 electronic records and signatures compliance
Change control treating model updates as validated changes
Data integrity per ALCOA+ across pipelines
EU Annex 11 alignment for European operations
Audit-ready evidence packs generated by the system itself

IND/04Questions, answered straight

FAQ

Can generative AI be used in GxP environments at all?

Yes, with discipline: defined intended use, a locked validation set, performance qualification against acceptance criteria, human oversight proportional to risk, and change control for model updates. Regulators object to unvalidated systems, not to AI categorically.

How do you validate a model that can change?

By treating change as a first-class process: versions are locked artifacts, retraining is a controlled change with re-qualification against the locked test set, and continuous monitoring runs with predefined alert limits. GAMP 5 second edition explicitly accommodates this approach.

Can systems run fully inside our network?

Yes — air-gapped deployment is our default for life sciences: open-weight models, on-prem vector stores, and observability that never sends data outside your boundary.

Do you have experience with actual inspections?

Our validation engineering follows the same CSV discipline our team has applied in regulated enterprise environments for years, and every system ships with an inspection-ready evidence pack. We also run mock inspections before you face a real one.