Industry - Healthcare

Engineering for systems where a production bug is a patient safety issue.

Your clinical AI feature is blocked because a clinician asked how it decided. Your EHR integration is months behind schedule. Your data pipeline is HIPAA-adjacent but has never been formally audited. In healthcare, the engineering has to survive patient safety requirements — not just software tests.

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On HIPAA compliance

We are an engineering firm, not a HIPAA compliance consultancy. We build systems that are architected to be HIPAA-aware — PHI boundaries enforced in code, access controls properly implemented, data flows documented. Final HIPAA compliance certification is with your legal and compliance teams. We have supported that process many times.

Where we help

The engineering problems we see most in healthtech.

These come from engagements with healthtech teams, hospital systems, and clinical AI platforms across patient intake, EHR integration, and data infrastructure.

AI features that clinical stakeholders will not trust

An LLM that summarises notes or flags anomalies is valuable until a clinician asks how it decided. Production healthcare AI needs explainability, human-in-the-loop controls, and audit trails from day one.

EHR integration that takes months instead of weeks

HL7 FHIR, Epic, Cerner — every integration is different, slower than expected, and full of edge cases that only surface in production. Getting it right requires experience with the specific quirks of each system, not just knowledge of the standard.

Data pipelines that are not safe for clinical use

PHI leaking across environment boundaries. Logging that captures data it should not. Training pipelines that mix de-identified data with production records. These are not theoretical.

Products built for a single site that need to scale to ten

Hardcoded workflows. Configuration managed in spreadsheets. Multi-tenancy bolted on after the fact. The architecture worked for the pilot site — it does not work for enterprise rollout.

Inference latency that makes clinical AI impractical

Models that score accurately in evaluation but are too slow to be useful in a clinical workflow. If it does not fit the clinical latency requirement, it does not get used — regardless of accuracy.

Systems that cannot survive an audit

Incident response that is undocumented. Access controls that are technically present but not enforced. Change logs that exist but are not searchable. When an auditor arrives, these are not theoretical gaps — they are findings.

Building a new healthcare product that passes its first compliance review

PHI boundaries, access controls, audit logging, and data-flow documentation are architecture decisions, not compliance patches. Healthcare products launched without these built in spend the next two years retrofitting them — while trying to scale to new sites and enterprise customers.

Solutions that require subject matter experts

Some engagements span industry verticals and technical challenges together. Explore the full solutions map to find the right match.

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Proof of work

What comparable engagements delivered.

Healthtech - Series AClinical AI
Inference cost reduced by 73% - same clinical accuracy

A patient-intake AI was running GPT-4o for every interaction. Accurate, but expensive and too slow for clinical workflow latency requirements. We rebuilt the pipeline as a routed multi-model system — small models for triage routing, larger models only when complexity requires it. The result: one-third the cost, half the latency, same accuracy on the clinical evaluation set.

PythonvLLMAWSHL7 FHIR
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System types

What we build for healthcare teams.

These are the categories of healthcare systems we build and modernize most often. Most start with a Product Pilot to validate the technical approach before committing to a full build.

Learn about Product Pilot →
  • Clinical AI with explainability and audit trails
  • Patient intake and triage automation
  • HL7 FHIR / EHR integration (Epic, Cerner)
  • HIPAA-aware data pipeline architecture
  • Medical document processing and classification
  • Interoperability and data exchange systems
  • Multi-tenant healthcare SaaS platforms
  • Inference optimization for clinical workflows
  • Remote patient monitoring (RPM) systems
  • Telemedicine and virtual care platforms
Selected work

Personalized care AI built to HIPAA-compliant architecture standards.

Client feedback

What clients say about working with us.

Collaborating with Insoftex on our healthcare project proved to be transformative. Their team skillfully re-architected our platform based on comprehensive feedback, delivering exceptional results. They effectively addressed complex challenges while maintaining a strong emphasis on quality and precision. We look forward to continuing our partnership and highly recommend Insoftex to anyone seeking innovative, high-quality solutions.
Dmitry Shteyn

Dmitry Shteyn

CTO · VURVhealth · USA

Building a clinical AI system that has to work?

Book a 30-minute technical call. We will ask about your workflow, your constraints, and what safe means for your specific deployment context.

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