Ambient documentation
The visit becomes a structured note — history, exam, assessment, plan, plus discrete data that lands in the right fields, not a wall of text you have to re-key.
AI-native healthcare
AI shouldn’t be another tab.
Most healthcare AI is a chatbot parked beside the software people actually use. Ours is inside the work: in the note, in the inbox, in the coding queue, in the schedule, in the report you were about to ask an analyst for. Labeled where it acts, explainable when you question it, and off wherever you say so.
Ask anything
Two questions nobody should need a report for.
One about a patient, one about your operations. Same interface, same permissions, same audit trail.
When did this patient’s A1C first exceed 7?
Answer
14 March 2024 — A1C 7.2% (Quest, ordered by Dr. Aguilar). It had been 6.4% at the prior draw in September 2023.
Because of- Lab result · 14 Mar 2024
- Progress note · 21 Mar 2024
- Flowsheet · A1C trend
Model and version are recorded with this answer, and the whole exchange appears in your audit log.
Document
The visit becomes the note.
Structured, not a transcript — with discrete data landing in the right fields, and a clinician reviewing before anything is signed.
Search patients, orders, reports, settings…⌘K
AR
Encounter · Ambient documentationLive
Recording11:42consent on file
Clinician
How have the mornings been since we changed the dose?
Patient
Better. The numbers are usually around 120 now, and I’m not getting the afternoon crash.
Clinician
Good. Any dizziness, swelling, anything new?
Patient
No. My feet were sore for a while but that stopped.
Draft noteunsigned · AI-drafted
- Subjective
- Reports improved morning glycemic control since dose adjustment, typically ~120 mg/dL. Denies dizziness, edema, or new symptoms. Prior bilateral foot soreness resolved.
- Assessment
- Type 2 diabetes mellitus — improved control on current regimen.
- Plan
- Continue current dose. A1C in 3 months. Retinal screening overdue — order placed.
Review & signEdit3 discrete data points captured
Triage
An inbox that arrives sorted.
Summarized, categorized, prioritized, and drafted. Nothing is sent on your behalf — the work is done, the decision is still yours.
Search patients, orders, reports, settings…⌘K
AR
Inbox · 4 of 61 need youLive
Quest · CMP panelAbnormal
Creatinine 1.6 (up from 1.2). Two prior values trending up.
Refill · metformin 1000mgRoutine
Within protocol. Last A1C 6.8%, seen 6 weeks ago.
Patient · Maya KesslerQuestion
Asking whether to hold medication before Thursday’s procedure.
Prior auth · imagingBlocked
Payer needs conservative-therapy documentation; agent found it in the 12 Jun note.
57 items summarized, categorized, and routed automatically · nothing auto-sent
Eight capabilities
Where the AI actually shows up.
Clinical intelligence
Relevant history, medications, trends, and care gaps surface at the moment they matter, with the reasoning and the source visible behind every suggestion.
Chart search in plain language
Ask “when did this patient’s A1C first exceed 7?” and get the answer with the result that proves it — across scanned documents, notes, and structured data.
Inbox intelligence
Results, refills, and messages summarized, categorized, prioritized, and drafted — so the inbox is a review queue instead of an evening.
Coding assistance
Documentation improvements and coding suggestions with the supporting text quoted, so the coder is checking work rather than hunting for it.
Patient communication
Personalized instructions and follow-ups at the reading level and in the language the patient actually uses, reviewed before they send.
Workflow agents
Build agents that carry out the repetitive administrative work — prior auth packets, referral follow-through, recall lists — with defined stopping points.
Conversational analytics
Ask about your own operations and get a chart back. “Which providers’ average wait time rose more than 20% this quarter?”
Responsible by construction
The part every vendor skips.
Healthcare AI is only useful if a compliance officer, a medical director, and a clinician can all say yes to it. These are the properties that make that possible — and they are architectural, not policy.
Labeled, always
Anything a model wrote is marked as such wherever it appears, including in the chart.
Reviewed by a person
AI drafts. A licensed human signs. The checkpoints are part of the workflow, not a policy PDF.
Explainable
Every suggestion shows what it was based on — the note, the result, the document, the trend line.
Auditable
Model and version are recorded with the output, and every AI action appears in the same audit log as everything else.
Permissioned
AI sees exactly what the user sees. Nothing is escalated to make a feature work.
Yours to turn off
Feature by feature, role by role, location by location. Including entirely.
A model can draft, surface, summarize, and chase. A licensed human decides.