Four Capabilities for Healthcare and Regulated AI
Each capability is designed around the specific architecture requirements of regulated environments — not adapted from generic AI consulting.
Evidence-Grounded AI Systems
“Every output traces back to a specific source.”
RAG pipelines designed for regulated domains — where outputs carry regulatory, clinical, or legal weight and will be reviewed by analysts, clinicians, or regulators. Source authority weighting, per-output confidence decomposition, and evidence provenance links are not optional features; they are architectural requirements.
Deliverables
- ◈Evidence retrieval pipeline with source authority weighting (FDA/regulatory, academic/clinical, manufacturer)
- ◈Per-output confidence decomposition: Evidence Support · Source Authority · Contradiction Check
- ◈Provenance links: every output traceable to specific documents, filings, or records
- ◈SME override API with stored decision audit trail
- ◈Analyst review workflow integration
Business Value
Outputs that can be reviewed, challenged, and defended. Analyst throughput multiplied without replacing expert judgment.
Who Buys This
Head of AI/ML, CTO, or VP Regulatory at a medical device company, health data company, or healthcare technology organization deploying AI in clinical or regulatory workflows.
The Problem We Solve
Off-the-shelf RAG provides no source attribution, no confidence scoring, and no evidence provenance. When a clinician or regulator asks 'how do you know that?' the answer cannot be 'the model said so.'
Evidence from Production
ECRI Horizon Scanning: 5,000 weekly signals → ~100 for SME review, zero false negatives on novel device approvals. Trajector Direct Nexus: 99.4% of nexus opinions link to specific medical records.
Regulatory Logic Engineering
“When the rule is clear, no LLM should touch it.”
AI systems that encode regulatory frameworks — FDA classifications, VASRD rating schedules, EU MDR risk classes, coverage determinations — as deterministic algorithmic guardrails. LLMs handle the genuinely ambiguous tasks: interpreting narrative medical records, reasoning across biomedical knowledge, synthesizing multi-source evidence. Unambiguous regulatory determinations are implemented as executable logic, not model outputs.
Deliverables
- ◈Deterministic regulatory logic layer implementing specific statutes or regulatory frameworks
- ◈Hybrid architecture: deterministic rules (zero LLM) + bounded LLM for genuinely ambiguous tasks
- ◈Output constraint enforcement: schema validation, confidence caps, SME override protection
- ◈Idempotent run keying (UUID5): same input always produces same output
- ◈Regulatory logic unit tests covering all classification paths and edge cases
Business Value
Outputs that can be defended to regulators and auditors. Zero hallucination risk on rule-bound determinations. Confidence 1.0 where statute mandates the outcome.
Who Buys This
VP Regulatory, Head of Product, or CTO at a medical device company, health insurer, legal-medical technology company, or any organization deploying AI in workflows governed by specific regulatory or statutory rules.
The Problem We Solve
Applying probabilistic LLM inference to rule-bound determinations introduces failure modes that accuracy benchmarks cannot detect — confident wrong answers that look identical to correct ones, with no audit trail.
Evidence from Production
Presumptive Nexus: 20 rules across 7 legislative categories, 0 false positives, confidence 1.0. VA Rating: full 38 CFR §4 implementation (pyramiding, bilateral, TDIU, combined ratings) — 80–100% exact match on golden cohort, 157 unit tests.
AI Evaluation & Quality Engineering
“If you can't measure it, you can't defend it.”
Structured evaluation frameworks for AI deployed in high-stakes workflows — because 'we tested it and it seemed accurate' is not sufficient when regulators, clinical leaders, or auditors ask how you know the system is working. Golden cohort design, LLM-as-judge evaluation harnesses, confidence calibration analysis, and production observability that answers the question before it's asked.
Deliverables
- ◈Golden cohort design: representative known-outcome cases that gate every production deployment
- ◈LLM-as-judge evaluation harness measuring output quality against expert-authored ground truth
- ◈Automated QA checks per production run: evidence quality, grounding completeness, traceability, cost baselines
- ◈Confidence calibration analysis: does the model's expressed confidence match its actual accuracy?
- ◈Production observability dashboard: quality, cost, latency, and coverage tracked per run
Business Value
Answers 'how do we know it's working?' before a regulator or clinical leader asks. Catches regressions before they reach analysts or patients. Converts 'we think it works' into 'we measured it and here is how we know.'
Who Buys This
Head of AI/ML, VP Engineering, or regulatory/quality team at any organization deploying AI in healthcare or regulated workflows who needs to measure, prove, and maintain system quality over time.
The Problem We Solve
Most AI quality programs consist of accuracy benchmarks on held-out test sets. In regulated environments, this is insufficient: benchmarks don't detect distribution shift, calibration failure, or regressions on the specific cases regulators care about.
Evidence from Production
Trajector OL Use Cases: 11 automated QA checks per run, 9-veteran golden cohort regression gating, DBSCAN anomaly detection, joins against 1.37M+ VA evidence documents, 78 tests. ECRI EMBASE: model selection made on calibration quality, not raw accuracy — Opus over Sonnet for appropriate uncertainty expression.
Production AI Systems for Healthcare
“Embedded engineering, weekly cadence, full lifecycle ownership.”
End-to-end AI engineering for healthcare and life sciences organizations — from architecture design through production deployment and ongoing measurement. Embedded engagement model: working alongside your analysts, clinicians, and technical team on a weekly cycle, with multiple pipelines in production simultaneously and the discipline to prove behavior in the lab before shipping to production.
Deliverables
- ◈Architecture design: multi-pipeline system with deterministic-first principles and human review integration
- ◈R&D phase: iterative development in a dedicated lab environment before production promotion
- ◈Production deployment: FastAPI/Celery infrastructure, job queuing, SME override APIs, monitoring setup
- ◈Evaluation harness: golden cohort, automated QA checks, cost instrumentation, MLflow experiment tracking
- ◈Ongoing cadence: weekly shipping of new capabilities, measurement of every change
Business Value
AI capability delivered faster than building an internal team with the regulatory domain depth to do it correctly. Production systems that can be reviewed by clinical and regulatory leadership from day one.
Who Buys This
CTO, VP Engineering, or Head of AI at a healthcare organization, medical device company, or health data company that has a genuine AI opportunity — horizon scanning, evidence synthesis, clinical workflow automation, regulatory classification, post-market surveillance — but needs the combination of production engineering rigor, healthcare domain depth, and regulatory context to execute it correctly.
The Problem We Solve
Building AI in regulated healthcare domains requires a combination of skills that is rarely available on a single team: production engineering discipline, LLM architecture expertise, regulatory domain knowledge, and the judgment to design human oversight into the system from the start.
Evidence from Production
ECRI Institute: embedded engagement, five pipelines in production, weekly shipping cadence, active FDA BAA contract. Lab-to-production discipline: R&D phase → production promotion → automated sync-checks policing drift.
Not sure which capability fits your problem?
Most engagements span more than one. Tell us about your environment, your regulatory context, and what you're trying to build — we'll be direct about whether and how we can help.