AI booking assistant
====================

Augusta Labs × 3t Global · 2026 · Pilot

Four siloed systems, one human-approved offer.

3t Global trains offshore and industrial crews. Coordinators re-key every booking across four systems that share no IDs. This assistant reads them all and drafts dated, prerequisite-checked offers — a coordinator approves every send and every booking.

I built the intake that turns two request sources into one case shape, the prerequisite engine — rule checks plus an LLM reader whose every point must quote the source text — e-learning routing, and the whole certificate-expiry agent, including a port of the legacy SQL procedure that reproduces its output row for row.

Piloting on live requests; writes to 3t’s systems stay held back until a person releases them.

How it works
------------
Certificate-expiry agent: daily sweep → [legacy TMS (ported SQL procedure) | training platform (expiries export)] → group (one case per delegate) → refresher resolver (from prerequisite rules) → [already handled? (bookings · newer certs) | prerequisite check] → dates before expiry (last 90 days first) → coordinator gate
Request pipeline: [legacy TMS (request forms) | training platform (request export)] → intake & dedupe (one case shape) → [course resolver | e-learning router] → prerequisite reader (LLM, quote-verified) → date search (live runs) → draft reply → coordinator gate

Outcomes
--------
- 83%  of pilot cases (714 of 855) needed no human read of prerequisite text
- Daily  expiry pickup, down from a 19–36-day cycle
- 43 / 43  rows matched when porting the legacy procedure

Stack: TypeScript, Bun, Azure SQL, Azure AI Foundry, App Service

https://godnik.in/projects/booking-assistant