AI-integrated field service platform

Field service teams run on a patchwork: scheduling in one tool, reporting in another, calibration somewhere else, expenses in a fourth and tribal knowledge in the heads of the engineers who happened to be on site that day. The real data lives in photos on someone's phone and notes scribbled between visits, then it gets retyped at the end of the day if it gets typed at all.

The brief was a modern field service platform from scratch on a tight timeline, with AI built into the core workflows rather than parked in a separate “AI features” tab. Small team, weekly cadence, no room to ship a v1 that would need rewriting once the AI layer arrived.

Architected the platform so AI doesn't sit next to the workflows, it sits inside them. Every artifact captured in the field (a photo of equipment, a voice memo from the engineer, a receipt, a before/after pair) lands in a structured ingestion pipeline that turns raw input into typed, queryable data the rest of the platform can use.

The choice that shaped everything else: separate identity from operational data at the schema level, so service history can compound into pattern recognition without dragging customer identities along with it. Privacy by architecture, not by policy. The same shape that makes the AI layer cheap to extend later.

Three surfaces on a shared backend: a mobile-first capture experience for engineers in the field, a responsive web app for back-office reporting and a thin admin panel for ops. Node.js in the middle, Claude and OpenAI behind structured prompts that map captured artifacts to typed report fields: photo and equipment recognition, voice transcription with key-value extraction, receipt-to-expense parsing.

Three people on the team (product owner, solution architect and backend, frontend), eight cuts, the same ritual every week: ship what's done, demo what's done, plan one week. The cadence stayed strict on purpose. It kept the scope honest and surfaced architecture mistakes while they were still cheap to fix.

Functional MVP in 8 weeks with AI as a first-class part of the platform, not a later retrofit. The mobile app captures in the field, the backend turns capture into structured reports and the historical data compounds into something the platform can search across.

The cadence held end-to-end. No skipped weeks, no “AI sprint” tacked on the end, no rewrite of the data layer to make the intelligence fit. The architecture is built to grow new capabilities on top without disturbing what's underneath.