Case study · Remove the bottlenecks

An hour a letter, down to five minutes.

BZ Clinic's specialist medical typist was retiring, and replacing her was going to be hard and expensive. We built a custom AI tool for clinical practice into the clinic's existing workflow instead: the clinician dictates as normal, and a fully formatted patient letter comes back in minutes. It's live today.

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No sensitive patient data goes into anything we build. It formats, it doesn't decide.

5 min down from an hour
per letter
Live today, and owned outright by the clinic.
The problem

A retiring typist, and a strict privacy constraint

BZ Clinic is a neurodevelopmental practice in Surrey, run by Dr Bozena Zoric with support from their Clinic Co-ordinator on admin. Every clinic letter, diagnosis, medication, risk, next steps, followed a strict clinical template and was produced by a dedicated medical typist who was retiring in mid-2026. Replacing a specialist medical typist is hard, and it's expensive.

The letters cover child health records, so the constraint was non-negotiable: no patient names, dates of birth, addresses or GP details could ever reach an AI tool. Whatever we built had to make that true structurally, not as a rule someone had to remember to follow.

The workflow

Three layers, and identifying data never touches the middle one

Privacy is structural here, not procedural. The layer that does the AI drafting never sees a patient's name, date of birth or address, because it's never given them in the first place.

A clinician dictating notes while walking between appointments
01

Dictation

Unchanged routine, fully anonymised

Dr Zoric dictates the clinical content using dictation software, exactly as before. Patients are anonymised, so no identifying details ever reach the tool.

AI drafting

Transcript in, formatted letter out

The transcript is pasted into a custom tool we built, configured to match the clinic's own template, structure, headings and tone. It auto-detects the three letter types (new patient letters to parents, GP follow-ups, video follow-ups), handles gender-specific content and medication side-effect text with BNF links, and ends every letter with a flags checklist for the Clinic Co-ordinator.

Details + QA

Under 5 minutes, then approved as before

The Clinic Co-ordinator adds the patient's identifying details from Cliniko, the practice system, in under 5 minutes. Dr Zoric reviews and approves, and the letter goes out exactly as it always did.

The build

Trained, not just built

Across five iterative rounds, we tested the tool's output against real, anonymised patient letters, including Dr Zoric's own red-text corrections, extracted programmatically so every fix fed straight back into the next draft. The retiring typist's own letters set the structural authority throughout. Built and handed over in under a month, ready before their last day.

Round 1

Baseline draft, tested against the retiring typist's own letters as the structural template.

Round 2

First correction pass, using Dr Zoric's real red-text edits on a fresh batch of anonymised letters.

Round 3

Refined again against further real, anonymised letters.

Round 4

Consistency check across new patient, GP follow-up and video follow-up letter types.

Round 5

Final validation round, then handover training for the Clinic Co-ordinator to run it independently.

What changed

Live today, owned outright

~92% less time per letter
<1 month built and handed over, ready before the typist left
“Excellent.”
Dr Bozena Zoric · on the first test letter
Said plainly

No identifying patient data ever touches the AI. That's structural, not procedural: the layer that drafts the letter is never given a name, date of birth, address or GP detail in the first place, so there's nothing for it to leak. GDPR-safe by design, not by policy.

Where this fits

This is what "removing the bottlenecks" actually looks like

Remove the bottlenecks

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