AI-Assisted Consultation Simulation
Consultation-to-treatment conversion improved from 58% to 76% within six months; revenue per consultation increased by 34%.
The challenge
Consultation quality was high but conversion was falling — patients were leaving informed, not committed.
The consultation funnel was the practice's primary commercial lever, but it was operating on instinct and experience rather than process. Different practitioners handled consultations differently. There was no standardised approach to patient objective-setting, no visual simulation of proposed outcomes, and no structured follow-up sequence for patients who didn't book on the day. Outcome photography existed but was inconsistently captured and rarely used in consultations. The practice was spending heavily on patient acquisition — cost per consultation had risen to £160 — and converting less than 6 in 10 of those patients to their first treatment. The unit economics were deteriorating, and the solution wasn't more marketing spend.
The system
Decision system built
We designed a structured consultation conversion system combining AI-assisted facial simulation, a standardised consultation protocol, and a tiered post-consultation follow-up sequence. The system was built to complement — not replace — the clinical team's judgment, giving practitioners structured tools and giving patients a clearer bridge between proposal and decision.
System components
AI facial simulation integration: real-time outcome visualisation for core injectable and skin treatment categories, configured for the clinic's specific treatment portfolio and practitioner style preferences
Standardised consultation protocol: structured objective-setting framework (patient goals, timeline, concerns, budget), piloted with each practitioner and refined over a 4-week calibration period
Outcome photography standardisation: lighting, positioning, and camera protocol implemented to enable consistent before/after documentation usable both clinically and in consultations
Post-consultation follow-up sequence: three-tier automated sequence for non-converting consultations, differentiated by treatment category and patient-stated decision timeline
Conversion analytics layer: per-practitioner conversion tracking, drop-off point identification, and weekly review cadence with clinical lead
How we worked
Engagement scope
AI simulation tool selection and configuration, consultation protocol design, outcome photography standardisation, post-consultation follow-up automation, and conversion analytics.
Timeline
12-week implementation with a 4-week practitioner calibration phase. Conversion analytics reporting live from week 6.
Operating model
Delivery team worked directly with the clinical lead and practice manager. Each practitioner received individualised onboarding to the simulation tool and consultation protocol. Weekly conversion reviews for the first 8 weeks post-launch.
Outcomes
Business impact & measurable results
Consultation-to-treatment conversion improved from 58% to 76% within six months; revenue per consultation increased by 34%.
Consultation-to-treatment conversion improved from 58% to 76% over six months — recovering and exceeding the practice's historical peak performance
Revenue per consultation increased by 34%, driven by both higher conversion and a modest increase in average treatment value as simulation tools supported more comprehensive treatment planning
Post-consultation follow-up sequence converted 18% of patients who did not book on the day — a segment that had previously generated zero revenue
Outcome photography database grew to 1,400 documented cases within 9 months, becoming an active clinical marketing asset used in consultations and digital content
Governance
Trust, collaboration & governance
Simulation tool outputs clearly framed to patients as indicative, not guaranteed — clinical team approved all language used in consultations
Practitioner autonomy preserved: the protocol provided structure, not a script — clinical judgment remained the primary guide
Post-consultation communications reviewed and approved by the clinical team before automation
No outcome photography used in marketing materials without explicit patient consent — consent workflow built into the photography protocol
Reframe
Patients arrived interested and left uncertain — the simulation gave them a visual basis to decide.
Across every engagement, the goal is the same: engineer a system that makes better decisions — faster, more consistently, and at scale — than the process it replaces.
Start a discovery
Most engagements begin with a conversation about context.
We do not send a proposal before we understand the problem. Start by telling us about your decision context — we will identify the highest-leverage intervention areas before any scope is agreed.