AI medical coding & billing
ICD-10 + CPT autosuggestion with explainability per code. Coder review queues, accuracy tracking, payer-specific bias correction.
Service · Healthcare AI & ML
Medical-grade AI — not chatbots bolted to a database.
Clinical models trained, validated, and deployed under audit-grade governance. Medical coding, decision support, voice documentation, risk stratification — with explainability built in.
Inference · onix-clinical
v3.2Input · vitals + labs
Readmission risk · 30d
High risk74%
Moderate19%
Low risk7%
p95 · 47 msclinically validated
What we build
Six things, in one engagement.
Ambient voice documentation
Voice-to-note during patient visits. HIPAA-compliant pipeline, edge processing where required, structured note output.
Clinical decision support
Evidence-graded recommendations at the point of care. Rule-based, ML-driven, or hybrid — with full audit trails.
Risk stratification models
Readmission risk, no-show risk, sepsis risk, mortality risk. Validated on your population, not someone else's.
Fraud + anomaly detection
Claims-level anomaly detection for payers and self-insured providers. Provider-pattern outliers, geographic clustering.
Imaging + computer vision
Radiology, pathology, intraoperative guidance. PACS integration, DICOM workflow, FDA-submittable when needed.
Who it's for
Built for these teams.
Recent work
One example, in numbers.
Tech we work with
PyTorchTensorFlowHugging FaceOpenAIAnthropic ClaudeAWS BedrockAzure OpenAIMLflowFHIR R4DICOM
Compliance scope
- HIPAA (de-identified training data)
- FDA premarket guidance (for SaMD ML)
- Model risk management documentation
- Bias evaluation reports
Our process
Predictable delivery — even when the scope isn't.
015 days
Audit & plan
We review your code, infrastructure, and compliance posture. You get a written report, architecture diagram, gap analysis, and a fixed-price roadmap.
022-week sprints
Rescue or build
Weekly demo. Live dashboard with sprint velocity, open issues, and burndown. Read access to our repo from day one.
03Continuous
QA & compliance
Automated and manual testing, security review, HIPAA-readiness check, optional third-party penetration test.
04Ongoing
Deploy & optimize
Production launch with monitoring, on-call rotation if you want it, continuing development at a steady cadence.
FAQ
Questions we get on the first call.
How do you handle PHI in training data?
Two patterns: (1) de-identify per Safe Harbor or Expert Determination before training, or (2) train inside the BAA-covered environment with no data exfiltration. Per-engagement choice with your compliance lead.
Are your models FDA-submittable?
Yes — when the use case is SaMD. We produce the required cybersecurity documentation, validation evidence, and clinical validation reports alongside the model artifacts.
What about LLMs — can you use GPT-4 or Claude for clinical use?
Yes, when appropriate. We use BAA-covered offerings (Azure OpenAI, AWS Bedrock Claude) and design prompts + guardrails to prevent unbounded outputs. We don't use unbounded LLMs for autonomous clinical decisions.
Insights · Read more
Background reading for buyers.
AIDec 15, 2025
Building a clinical validation harness for ML in healthcare
Models that work on benchmarks don't always work in your hospital. Here's the validation infrastructure we wish every team had on day one.
Serhii Kholin · 11 min
EngineeringNov 30, 2024
Ambient voice documentation: pipelines that don't leak PHI
Voice-to-note is one of the highest-leverage AI uses in clinical practice. It's also one of the easiest places to ship a HIPAA violation. Here's how to architect it.
Serhii Kholin · 13 min
ComplianceJun 19, 2021
What a HIPAA audit actually looks like — from the inside
Most teams build to a checklist and hope. We've sat across from the auditor — here's what they actually ask, and what surprises engineering teams.
Denis Sheremetov · 8 min
Start with the audit
5 days. Written report. No commitment.
Tell us what you're building or what's not working. We'll come back with a written audit and a fixed-price plan.