HIPAA-compliant medical AI for independent medical examinations. Clinical document intelligence for record-heavy specialties. Edge AI on consumer wearables for continuous monitoring. AI-driven dispatch for mobile clinical services. Every system designed with PHI inside the BAA boundary, audit-grade explainability, and clinician-in-the-loop control.
Book discovery callBook a call→Every AI component — OCR, medical NLP, embeddings, generation — runs inside one signed AWS BAA boundary. VPC endpoints for every service. KMS-encrypted, append-only audit logs. No PHI ever traverses the public internet, no PHI ever reaches a public model.
We answer them with the architecture, with the numbers, and with the case study where we shipped it. Click any worry to see how we think about it.
Agreed. Our default architecture runs every AI component — OCR, medical NLP, embeddings, LLM — inside a single signed AWS BAA boundary. PHI never traverses the public internet. VPC endpoints for every service. KMS-encrypted, append-only audit logs. Document custody transfer is an explicit, logged event.
PHI bytes that ever traverse the public internet in our default healthcare architecture
PHI redaction before any LLM call — regex + field scrub + Comprehend Medical + Bedrock Guardrails
Each one anchored in a working system you can trace: the architecture, the trade-offs, the operating cost, the failure modes. No vapor.
Capacity-constrained K-Means clustering, 11-constraint hard filtering, 11-factor weighted scoring, OR-Tools VRPTW route sequencing, 2-opt cross-fleet improvement. Sub-30-second re-optimization for STAT visits. Every assignment audit-grade.
Hybrid retrieval (vector + full-text + Reciprocal Rank Fusion) over case files. Every extracted fact carries {document, page, bounding-box, quote} so the clinician can click and verify in seconds. Mandatory case-level tenant scoping enforced at the repository layer.
Trained models sync back to the watch and phone so detection runs locally. Identifiers stripped on the device — names → UUIDs, exact ages → ranges — before telemetry ever leaves. Battery-aware duty cycling, tiered model cascades. The data stays where the patient is.
Textract Queries for structured extraction (not just text). Titan v2 embeddings for semantic canonicalization across inconsistent lab test naming. Claude Haiku as LLM tie-breaker only when needed. Hallucinated order IDs rejected at the API layer.
Tap any system to see the architecture, the trade-offs we made, and the operating numbers.
Causation-grade fact extraction with citation guarantees, all inside one signed AWS BAA boundary.
OR-Tools VRPTW + 2-opt + K-Means, with full decision auditability for every routing assignment.
Three-stage LLM orchestration (planner → agent → parser), versioned prompts, graceful degradation.
Multi-sensor fusion · personalized per-user models · battery-aware tiered cascade · privacy-first telemetry.
Textract Queries + Titan embeddings + Claude tie-breaker. Hallucinated order IDs rejected at the API layer.
Four-layer PHI redaction. Eight MCP tools with context-scoped authorization. Six anti-hallucination mechanisms.
Compliance is not a checklist we add at the end. It is the constraint we design under from day one.
Senior engineers — typically the same people who built your platform. Tiered by issue priority.
If you're thinking about AI for your business and you want to talk to someone who will tell you the truth — including when AI is the wrong answer — book a call. We'll spend 30 minutes understanding what you're trying to do, what you're worried about, and whether we're the right fit.