Voice-to-Ops: field reports that write themselves

A field technician finishes an inspection — on a roof, in gloves — having found real problems: a cracked heat exchanger, a blocked drain line, a worn belt. That information exists for about thirty seconds as spoken words on site, then has to survive being remembered and typed into a form hours later. Structured intake forms lose to how people actually talk in the field, and a voice memo that just sits on a phone doesn't get anyone dispatched. Voice-to-Ops closes that gap. The technician dictates exactly as they would anyway; the app transcribes the audio with Speechmatics, then an open model (Llama-3.3-70B on Featherless) structures the transcript into a validated report: findings graded critical/major/minor, each with a verbatim evidence quote proving where it came from; action items with due windows; required materials; and an explicit "not determined on this visit" list. A dashboard tracks jobs and open actions, and a one-click follow-up email turns the report into customer communication. The pipeline is designed for restraint, not just capability: a strict JSON schema is enforced at the boundary, every finding must quote the transcript, hypotheticals and mentions of other job sites never become findings, and when the model can't determine something it says so instead of guessing. Built primarily with native.builder (Product Architect scoping, then Builder-agent generation against a specified design system). Because the generated app is client-only, both AI providers are called through Supabase Edge Functions — no API key ever reaches the browser, and the public endpoint carries per-caller rate limiting. Target users: field service technicians and inspectors in any dictation-heavy trade, demoed with an HVAC inspector persona.