Ambient Clinical Note Assistant

For

Clinicians at small GP practices

Solves

Documentation burdens consume evenings and reduce time with patients.

Delivers

Structured clinical notes, draft records, audit trail of the consultation

Built in

about 4 weeks of creation time, MVP in 4 days

Investment

$7,500 for the MVP, $28,000 for the full product

Run it

Inside your business, or as part of your offer to clients

For clinicians at small GP practices, turn consultation audio and dialogue into structured clinical notes and draft records, and the problem it addresses is evenings spent typing notes from memory or shorthand.

1. Capture audio in real time.
2. Distinguish speaker roles.
3. Transcribe dialogue to text.
4. Map text to structured templates.
5. Provide secure review and approval.
6. Export to EHR systems.

What goes in, what comes out

The workflow

1. In
   Start with

   Consultation audio, patient dialogue, clinical note templates

2. 1

   Start consultation

3. 2

   Capture audio

4. 3

   Generate draft

5. 4

   Review and edit

6. 5

   Approve note

7. 6

   Save to EHR

8. Out
   Finish with

   Structured clinical notes, draft records, audit trail of the consultation

AI does the heavy lifting, people stay in charge

Use speech-to-text and language models to structure dialogue into clinical templates. Detect speaker intent and context. A clinician reviews the draft for clinical accuracy and privacy compliance before saving.

What your team sees

Key screens: Consultation dashboard, draft editor, template configuration. Use a central dashboard showing the consultation timer and draft status. Display the draft note in a split view with the original audio transcript. Allow inline editing and template switching. In this product, the first view is the consultation dashboard, followed by the draft editor.

Accounts and administration

User roles, template management, approval workflows, audit logs of edits, data retention policies

Integrations and data access

EHR systems (HL7 FHIR), secure audio storage

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   4 days

   One buyer segment, one recurring use case. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   5 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   9 days

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will clinicians at small GP practices use it to solve "documentation burdens consume evenings and reduce time with patients"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Run for two weeks with one clinician.
4. Measure, then decide. Track reduction in documentation time per day and clinician satisfaction score. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. One clinician, one consultation room, one template, one EHR integration

After the MVP. Multi-room support, multi-clinician support, advanced analytics, voice biometrics

What the build depends on. High-speed local processing, reliable microphone arrays, secure data governance frameworks

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test pricing at 150 USD per clinician per month

Message to test

Capture the consultation, not just the words.

Where to find buyers

Medical conferences, healthcare IT publications, practice management associations

Lead magnet

Free demonstration of a 15-minute consultation draft

The first 30 days

1. Week 1: Install hardware in one room.
2. Week 2: Configure templates and EHR link.
3. Week 3: Train staff on review process.
4. Week 4: Review data privacy and accuracy metrics.

Paid pilot

Run for two weeks with one clinician. Baseline: 2 hours of documentation time per day. Decision: If time saved exceeds subscription cost, expand to two rooms.

Success metrics

Reduction in documentation time per day, clinician satisfaction score

Retention and expansion

Renew based on continued time savings and data security compliance

Why clients would pick it

Privacy compliance (GDPR/HIPAA) and deep integration with specific clinical workflows/templates

Alternatives and positioning

Manual typing, generic transcription services, other SaaS note-taking tools

Main delivery costs

Hardware procurement, cloud processing, support staff

Strict consent recording, automatic deletion of audio after note approval, role-based access