Doctors finish notes at night, and patient data must not go to a third-party cloud.
Structured reports and letters written by hand after every appointment.
You want AI scribes, but not a new data processor for every patient record.
GDPR special-category health data, HIPAA, or national rules that keep records in the country.
Doctors spend more time on the record than with the patient — and health data is the most expensive data to lose.
of EHR and desk work for every hour of direct patient time; in the office, 49% of a doctor’s day goes to screens and paperwork.
of after-hours work every night, mostly in the electronic record — the documentation that did not fit into the day.
of documentation time saved in one year by ambient AI scribes across 7,260 physicians and 2.5 million visits.
average cost of a healthcare data breach — the most expensive industry for the 14th year in a row.
of organisations say non-public information has already been typed into public GenAI tools — in a clinic that means patient data.
Put your monthly volume into the calculator and see hours and dollars.
The path of one appointment. The recording never leaves the building, and the doctor signs every note before it reaches the record.
The doctor starts a recording on a tablet or desk mic after the patient agrees; the visit ID comes from the schedule.
RecordWhisper runs on the clinic’s GPU and turns speech into text, telling the doctor and the patient apart.
RecordGreetings and side conversation are dropped; medical content is kept with timestamps.
RecordA local model writes the note in the clinic’s format — complaints, history, findings, assessment, plan.
WriteDiagnoses and prescriptions are matched to your code lists and formulary, as suggestions for the doctor.
WriteInaudible doses, contradictions or missing findings are highlighted instead of guessed.
WriteThe draft opens next to the transcript; the doctor edits, accepts and signs — only then does it go to the record.
WriteThe signed note is saved to the patient record through the EHR’s API or HL7/FHIR interface.
SendReferral letters and patient instructions are drafted from the signed note in plain language.
SendStaff ask questions across the patient’s history and get answers with the visit date and document.
SendAfter the note is signed, the recording is deleted on schedule; the audit log keeps who did what and when.
SendA local AI scribe records the consultation with the patient’s consent, transcribes it and writes the visit note in the clinic’s own template — all on a server inside the clinic. The doctor reads the draft, corrects it and signs it; only the signed note goes into the medical record. The same model drafts referral letters and answers questions across a patient’s history.
Documentation is the biggest time sink in outpatient care: a well-known time-and-motion study found two hours of record and desk work for every hour with patients. Ambient AI scribes give that time back — and running them locally means patient audio and records never go to a new cloud processor.
Doctors look at the patient instead of the keyboard, notes are finished before the next visit, and letters are drafted in seconds. The clinic keeps the data, the model and the audit trail.
Typical minutes per consultation in a private outpatient clinic. Your own numbers go into the calculator below.
Based on 20 min by hand and 4 min with local AI per item, from the table above.
The model listens, writes and searches. Diagnosis, treatment and every signature stay with the doctor.
The model never decides; it writes down what the doctor said and found.
Nothing enters the record until the doctor has read and signed it.
Recording starts only after the patient agrees; they can say no.
Suggested from the conversation, confirmed and issued by the doctor.
Bad news and sensitive talks can be left out of recording entirely.
Flip a switch to hand a step to the model or take it back.
The Permanente Medical Group’s rollout shows the size of the prize: tens of thousands of hours of documentation returned to doctors. A local setup brings the same idea without a new cloud processor for patient data.
We look at the documents and the work, agree what the model may and may not do, and size the hardware.
The model on a server in your network, on your real documents, with a small group of users.
Access rules, logging and integrations, then the whole team — with the old way kept as a fallback.
Model updates in maintenance windows, monitoring and a monthly report of use and time saved.
Running the model on your own hardware means no new external processor for patient data, which removes the hardest part of the assessment. You still need consent for recording, access rules and a retention policy — we set them up with your data protection officer.
Whisper large models are accurate on clear speech in most languages; medical terms improve with a short custom vocabulary. Anything the model is not sure of is highlighted for the doctor rather than guessed.
Yes. Whisper and modern open models handle Ukrainian, English, German and many other languages, including a mix of two in one consultation.
Yes, and that is the point: the model writes the draft, the doctor reads, edits and signs. Notes never enter the record unsigned.
For a clinic with 5–15 doctors, one server with a single 24–48 GB GPU handles transcription and note writing during the day. We size it on your visit volume.
Through the EHR’s API or an HL7/FHIR interface where one exists; otherwise the signed note is copied in one click. We check your system in the first week.
Describe your use case in two sentences — we reply within a day with a hardware and model recommendation.
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