Clinical documentation
Ambient capture and note drafting that writes back into the EHR, reducing after-hours charting. The integration and the entitlement model are the hard parts, not the transcription.
AI for Healthcare
Clinical and operational AI deployed inside your environment. PHI never leaves the covered entity’s control.
The constraint
Two constraints, not one
Health systems face a harder version of the enterprise AI problem than most sectors, because two constraints bind at once.
PHI cannot leave the covered entity’s control without a Business Associate Agreement, and even with one, many compliance officers will not accept a third-party processor in the path for clinical data. That eliminates most managed-API architectures before the technical evaluation begins.
Clinical risk tolerance is near zero. A system that is right 92% of the time is a research result, not a deployable tool, when the remaining 8% touches patient care. That raises the bar on evaluation, human oversight, and the ability to trace any output back to its source document.
Both are solvable. Neither is solved by choosing a better model.
Applications
Where AI creates value in a health system
Prior authorization
Assembling the clinical evidence package for a payer request from records scattered across encounters. High volume, highly structured, and measurably expensive today.
Coding & revenue cycle
Suggesting codes with citations to the supporting documentation, and flagging under-documented encounters before they become denials.
Clinical trial matching
Screening patient populations against protocol criteria that are written in prose and change frequently.
Care-gap analysis
Surfacing overdue screenings and follow-ups across a panel, with the record excerpt that justifies each finding.
Compliance
Regulatory landscape
| Framework | What it drives in the architecture |
|---|---|
| HIPAA | Governs PHI use and disclosure. On a private deployment there is no third-party processor, which removes the BAA question from the architecture rather than answering it. |
| HITRUST CSF | Frequently required by health systems of their vendors. Control mapping and evidence collection should be designed in, not reconstructed later. |
| 21 CFR Part 11 | Applies where AI output enters regulated records. Drives audit trail, e-signature, and validation requirements. |
| State privacy law | Varies and can be stricter than HIPAA, particularly around behavioral health and reproductive care records. |
We align AI governance to NIST AI RMF and ISO/IEC 42001 alongside your sector-specific obligations.
Integration
Integration with clinical systems
Value depends on writing back into the systems clinicians already use. We integrate with Epic, Oracle Health (Cerner), and Meditech via HL7v2 and FHIR, plus document repositories and PACS where imaging context matters. Retrieval runs under the requesting clinician’s existing entitlements, so a user cannot surface through AI what they could not open directly in the chart.
Questions
Frequently asked
Can this run inside our data center alongside Epic?
Yes. That is the most common architecture we deploy in health systems — model serving on GPU infrastructure in your datacenter, retrieval against your document stores, with no outbound dependency for inference.
How do you handle the accuracy bar for clinical use?
Citation-grounded retrieval so every assertion traces to a source document, an evaluation harness built on your own records with acceptance criteria agreed before development, and human review positioned where the clinical risk actually sits rather than uniformly.
Do you sign a BAA?
We will sign your BAA and NDA before detailed technical discussion. On a fully private deployment we are not a data processor in the production path at all — the BAA covers our access during implementation.
Related
Private AI
Production AI systems running entirely inside your infrastructure, with no third-party processor in the data path.
Private AIDeployment Models
Managed API, private cloud, on-premise, edge, and air-gapped compared — including where each one fails.
Compare modelsDiscuss your constraints.
The assessment evaluates your data, infrastructure, and regulatory position, then recommends a deployment architecture your review process can actually approve.
We will sign your NDA before a detailed technical discussion.