AI Engineering for Healthcare and Regulated Environments
Production AI systems for environments where accuracy alone is not enough — where outputs carry regulatory weight, require source attribution, and are reviewed by clinicians, analysts, or regulators.
Why Healthcare AI Requires a Different Architecture
Most AI systems are designed for environments where errors are recoverable. Healthcare is not one of those environments.
Healthcare AI Has Different Failure Modes
In a generic application, an AI error is a user complaint. In healthcare, it is an audit finding, a wrong medical determination, or a device safety event. The architecture requirements are not the same.
Regulatory Logic Cannot Be Probabilistic
Rules like FDA risk class, VA rating schedules, and EU MDR classifications are not ambiguous. Applying LLM probabilistic inference to determinations that are mandated by statute introduces failure modes that accuracy benchmarks will not detect.
Every Output Needs a Source
When a clinician, regulator, or analyst reviews an AI output, 'the model said so' is not sufficient. Every claim needs the specific document, filing, or evidence record that supports it.
Human Review Is a Design Requirement
In regulated environments, AI is a force multiplier for expert review — not a replacement for it. Systems built without human-in-the-loop architecture will fail at the exact edge cases that regulators examine most closely.
Where We Work
Four overlapping domains — each built on the same architectural principles, applied to different regulatory contexts.
Medical Device Intelligence
Horizon scanning, time-to-market prediction, and regulatory classification across the full FDA and EU MDR device catalog. Systems that reduce manual analyst review from thousands of signals to dozens — without false negatives on novel approvals.
- ◈Pre-market horizon scanning (72+ sources: FDA, PubMed, EUDAMED, ClinicalTrials.gov, HTA bodies)
- ◈Time-to-market prediction with 6-tier authority-weighted evidence scoring
- ◈Regulatory class detection (FDA 510(k)/PMA/De Novo · EU MDR Class I–III · IVDR A–D)
- ◈EMDN taxonomy classification and device catalog management
- ◈Conference abstract novelty mining from EMBASE and medical literature
Post-Market AI Surveillance
Production monitoring systems for AI-enabled medical devices — tracking KPI signals (sensitivity, specificity) from real-world hospital submissions, detecting performance degradation before it becomes a safety event.
- ◈Hospital PSO data ingestion (RL6 XML) and canonical observation pipeline
- ◈Welford's online algorithm for numerically stable baseline tracking
- ◈Two-layer monitoring: absolute thresholds + statistical deviation from running baseline
- ◈Facility anonymization via opaque UUID tokens (PSWP legal requirement)
- ◈Bronze/Silver/Gold medallion architecture: immutable raw → canonical → computed KPI facts
Medical Evidence AI
AI systems that analyze medical records, clinical documentation, and regulatory filings to surface evidence-based findings — with source traceability, bounded LLM reasoning, and regulatory logic as deterministic guardrails.
- ◈Medical record grounding with domain-specific embeddings (MedEmbed) for clinical terminology accuracy
- ◈Source-attributed nexus reasoning: every output linked back to specific documents
- ◈Deterministic regulatory logic encoding (38 CFR, coverage determinations, clinical criteria)
- ◈In-database vector search: sensitive data never leaves the production warehouse perimeter
- ◈LLM-as-judge evaluation harness + golden cohort regression testing
Clinical Evidence Synthesis
RAG pipelines for medical literature, regulatory filings, and clinical evidence — designed for environments where the output will be reviewed by clinicians, analysts, or regulators, not just users.
- ◈Multi-source retrieval across PubMed, ClinicalTrials.gov, openFDA, MedRxiv, and proprietary corpora
- ◈Source authority scoring: FDA/regulatory=1.0, academic/clinical=0.8, manufacturer=0.5
- ◈Per-output confidence decomposition: Evidence Support · Source Authority · Contradiction Check
- ◈SME override API with stored decision audit trail
- ◈Weekly digest pipelines with analyst review workflows
Pharmaceutical & Drug Development AI
AI systems for pharmaceutical R&D — regulatory intelligence, clinical trial support, pharmacovigilance, and real-world evidence generation — designed around the FDA/EMA drug development lifecycle and GxP validation requirements.
- ◈Drug development lifecycle intelligence (discovery → Phase I/II/III → NDA/BLA → post-market)
- ◈Clinical trial intelligence: protocol analysis, endpoint reasoning, patient/site identification from RWD
- ◈Regulatory document intelligence: IND, NDA, BLA, EMA MAA — extraction, gap analysis, Q&A
- ◈Pharmacovigilance AI: adverse event case processing, benefit-risk reasoning, safety signal detection
- ◈RWD/RWE evidence generation: EHR, claims, registries → safety, effectiveness, comparative effectiveness
Regulatory Frameworks We Work Within
We are not a regulatory affairs firm. We engineer the technical controls — audit trails, bounded inference, deterministic logic, provenance — that AI systems require in these environments. Your regulatory, quality, and clinical teams own the compliance posture; we build the architecture that supports it.
FDA 510(k) / PMA / De Novo
Pre-market clearance and approval pathways. AI systems that classify devices, estimate regulatory risk class, and track development phase from discovery through market entry.
EU MDR / IVDR
European Medical Device Regulation Class I/IIa/IIb/III and In Vitro Diagnostic Regulation Class A/B/C/D. Regulatory classification, EMDN taxonomy coding, and market status tracking across EUDAMED.
FDA Post-Market Surveillance
AI-enabled device performance monitoring from hospital Patient Safety Organization (PSO) data. Statistical baseline deviation detection, facility anonymization, and PSWP-compliant data handling.
38 CFR Parts 3 & 4
VA Schedule for Rating Disabilities and service connection standards. Deterministic regulatory logic for pyramiding (§4.14), bilateral factor (§4.68), TDIU (§4.16a), combined ratings (§4.25), and benefit of doubt (§4.7).
FDA IND / NDA / BLA
Investigational New Drug, New Drug Application, and Biologics License Application pathways. AI systems for regulatory document intelligence, gap analysis, and evidence extraction across the drug development lifecycle.
ICH E2x / Pharmacovigilance
International Council for Harmonisation safety guidelines covering pharmacovigilance system requirements, individual case safety reports, periodic benefit-risk evaluation, and signal detection and management.
21 CFR Part 11 / GxP
Electronic records and signatures for validated computerized systems in regulated pharmaceutical environments. AI systems designed for audit trails, reproducible outputs, and change control requirements.
Active Production Work
Medical Device Intelligence Platform
Five interconnected AI pipelines over a 10,000+ device catalog spanning the full regulatory lifecycle — FDA 510(k)/PMA/De Novo and EU MDR/IVDR — plus MHRA (UK), PMDA (Japan), and EUDAMED. Grounded evidence, EMDN taxonomy coding, human-in-the-loop analyst review, and an active FDA post-market surveillance contract.
Working on a Healthcare AI Problem?
Tell us about the regulatory context, what the failure mode looks like, and what human review looks like in your workflow. We'll tell you honestly whether we can help.