Phase 01
PHI classification first
PHI fields are identified and classified in the data model before development begins — so every API, job, and AI touchpoint knows what it is handling.
HIPAA-Compliant Development
AIDevelopmentforHealthcare&MedTech
We build HIPAA-compliant AI software for healthcare startups and MedTech companies — clinical decision support, patient intake automation, EHR integration, and medical document processing. Compliance is not an afterthought — it is the foundation.
HIPAA-first architecture
Compliance is built in, not bolted on
Every healthcare project we take on starts with a compliance architecture review. PHI handling, audit trails, and access controls are defined before the first line of code.
Phase 02
Encryption everywhere
Encryption at rest (AES-256) and in transit (TLS 1.3) for PHI, with key management aligned to your cloud and BAA requirements.
Phase 03
Audit trails on PHI access
Immutable-style audit logs for PHI reads and writes support security reviews, breach analysis, and compliance questionnaires.
Phase 04
Least-privilege access
Role-based access control with least-privilege defaults for clinicians, admins, and integrations — including AI pipeline service accounts.
Phase 05
BAA-ready workflows
Vendor and subprocessors are tracked; BAAs and data processing expectations are reflected in how we build and deploy.
Phase 06
HIPAA risk documentation
Risk assessment artifacts and remediation plans tie architecture decisions to HIPAA safeguards — not slide decks after launch.
See how our six-phase AI harness enforces QA, metrics, and HIPAA-aligned checkpoints on every engagement.
Read the full process →Healthcare AI use cases
What we build for healthcare companies
Clinical Decision Support
AI models that analyze patient data and surface evidence-based recommendations at the point of care — reducing diagnostic delays and improving care consistency.
Patient Intake Automation
Automated intake workflows that collect structured patient data, verify insurance, and route cases — reducing administrative burden and manual data entry.
Medical Document Processing
NLP pipelines that extract structured data from clinical notes, discharge summaries, and lab reports — making unstructured data searchable and analyzable.
EHR Integration & FHIR APIs
SMART on FHIR-compliant integrations with Epic, Cerner, and other major EHR systems — enabling data exchange without breaking compliance boundaries.
PHI-Safe Data Pipelines
Encrypted, audit-logged data pipelines for PHI — with field-level classification, access controls, and AWS HealthLake for HIPAA-eligible cloud storage.
Compliance Architecture Review
Architecture audit and compliance hardening for existing healthcare platforms — identifying PHI exposure risks and implementing remediation plans.
Technology stack
Tools we use for healthcare AI
OpenAI APIClaude APILangChainRAG PipelinesAWS HealthLakeFHIR APIPythonNode.jsPostgreSQLEncryption at RestAudit LoggingSMART on FHIR
Common questions
Healthcare AI development — answered
We classify PHI fields at the data-model level before development begins, enforce encryption at rest and in transit, implement audit trails for all data access, and apply role-based access controls. HIPAA requirements shape architecture decisions from day one — not retrofitted after launch.