TrichoTrack
Standardized AI scalp diagnosis, run as a business at production scale
2021–2024 · Alvisual
Business impact
- scans across 6 outlets in 15 months
- 24,000+
- infrastructure cost per scan
- ~S$0.87
- per outlet per month, all-in
- ~S$232
- Virtually zero downtime across 15 months of production.
- Objective scalp measurement in seconds by non-expert staff — standardized diagnosis across every outlet.
- Near-zero marginal cost per new outlet: SaaS operating leverage built in.
Problem
A hair-care chain needed to standardize scalp diagnosis across its outlets and reduce its dependence on scarce trichologists in order to scale. Objective scalp measurement — hair and follicle density — is impractical to do manually, so in practice it was either skipped or bottlenecked on a few experts.
Approach
- Explainable hybrid AI: deep learning detects only what a human can verify with their own eyes — hair strands, follicles, dandruff, pimples — and a rule-based expert system turns those observations into the diagnosis. Trust is what drives adoption: staff and customers can always see why.
- A genuinely hard data regime — cold start, out-of-domain microscope imagery, severe class imbalance, no gold-standard labels — solved with concept decomposition for annotation, self-supervised pretraining, and focal loss.
Tech
Multi-tenant SaaS (DDD, FastAPI, PostgreSQL), PyTorch → ONNX CPU inference, Stripe & Twilio, Docker + CI/CD
My role
CEO & co-founder — took it from the first business conversation to production and 15 months of operations, leading a 5-person team.
