Verifiable check-in and automated pay in a tour-operations platform.
A multi-tenant system for scheduling tours, confirming attendance with photo-based group counts, and calculating guide commissions. When counts disagree, a person still reviews the case.
Tour planning, attendance confirmation and guide pay were fragmented. Disputes around group size were hard to settle without a shared process you could audit.
Put scheduling, check-in, evidence and commissions in one platform. Use local computer vision to count faces in a group photo (not to identify people), and send mismatches to human review instead of approving them automatically.
What I built
- Multi-tenant management for teams, tours, bookings, calendars and rebooking.
- Guide check-in and group-completion workflows.
- A local OpenCV-based service that counts faces in an uploaded group photo.
- Mismatch handling: detected count vs reported count, flagged for human review.
- Commission creation after completed check-ins, including guide-specific rate exceptions.
- Feature tests covering the check-in and commission path.
Attendance evidence and pay lived in the same workflow. The AI step supports the process; it does not silently decide compensation. Important: this is face counting for group size, not facial recognition of individuals.
AI systems
AI inside a real workflow, with evaluation and human review where it matters.
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