HealthcareExpertise
We build EHR systems, telemedicine platforms, and clinical AI tools with HIPAA compliance and patient safety built in from day one, not added after a pilot succeeds.
Trusted by engineering teams across 15+ countries
From startups to Fortune 500 companies
Production Reality
Where Healthcare AI Projects Fail
Only practitioners who've shipped real systems handling patient data can flag these accurately.
PHI exposure through agent context
We architect PHI handling with redaction, scoped access, and HIPAA-compliant logging from day one.
No clear escalation when the agent is uncertain
We build explicit confidence thresholds and escalation to a clinician for anything outside a narrow, validated scope.
Documentation automation that breaks under real clinical language
We build retrieval and prompting around your actual clinical vocabulary and chart structure, not generic medical text.
Integration that can't talk to your EHR
We design integration around your specific EHR's actual API and data model before committing to an architecture.
IS THIS YOU?
Three situationsthat bring healthcare teams to us
If any of these sound familiar, we've solved this in production, not just in theory.
Clinicians and staff are spending hours on documentation that follows a predictable pattern.
You have patient or operational data that could flag risk or inefficiency automatically, but it's still reviewed manually.
You're piloting clinical AI tools and hitting HIPAA, accuracy, or integration issues before reaching production.
Delivery Structure
POC to Production Process
Discovery
POC Build
Production Architecture
Deploy & Monitor
Technical Stack
Models & Frameworks
Preferred for deterministic business workflow agents where state management and branching logic matter.
Multi-agent conversations where agents critique and revise each other's outputs.
Role-based agent teams with defined task sequences and human-in-the-loop checkpoints.
Managed vector store for production workloads that need fast ANN search at scale.
Hybrid search combining BM25 + vector, when keyword precision matters alongside semantic similarity.
Local and dev-stage retrieval for rapid prototyping before committing to a managed store.
GPT-4o for tool-use tasks requiring fast structured output and broad API ecosystem compatibility.
Claude for long-context tasks, instruction following, and workflows where refusal behavior needs to be controlled.
Multimodal inputs — document, image, and video understanding in a single model call.
REST and GraphQL integrations so agents can read and write to your existing systems without rebuilding them.
Event-driven triggers that let agents respond to external signals in real time.
Direct database read access for agents that need structured data alongside unstructured retrieval.
The Team
Engineers Behind the Work
Discipline-specific AI engineers not generalists. Each has shipped real systems in production.
Designs multi-agent workflows with audit trails and human-in-the-loop checkpoints for regulated decisioning.
Designs multi-agent workflows with audit trails and human-in-the-loop checkpoints for regulated decisioning.
Integrates and monitors model providers in production, with fallback logic and cost controls.
Common Questions
Everything you needto know
Which industries do you specialize in?
We have deep expertise in Fintech, Healthcare, Logistics, and E-commerce. Each practice area is staffed with specialists who understand the regulatory requirements, architecture patterns, and competitive landscape of that industry, so your team isn't educating ours from scratch.
Not sure if Healthcare AI is right for your use case?We'll tell you in 30 minutes.
No pitch. No obligation. Just an honest answer about whether we can help.