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InVinetory: iOS app with AI vision and voice

Point a phone at a wine label and it identifies the bottle, researches it once and files it in a shared household cellar. Describe dinner out loud and a voice assistant picks from bottles you actually own. The six planned phases were built in two days, against an estimate of 9–13 weeks part-time.

The InVinetory iPhone app showing the cellar list with readiness and locations, the voice sommelier with recent requests, and the shared-cellar settings.

What it does

  • Label scanning: one photo or a batch, read by a vision model in under three seconds, with a review step for anything uncertain.
  • Shared cellars: a household shares inventory and history; ratings and taste profiles stay personal.
  • Voice sommelier: speak the menu and get three picks, only ever from bottles in the cellar.
  • Phone and desktop: the same codebase runs as an iOS app, in a TestFlight pilot, and as a desktop web app. The code is Android-ready, but no Android build has been distributed yet.

Since the first release

  • What a bottle is worth: market value from US shop listings, and the competition awards it has won.
  • Drinking it at the right time: a weekly “drink soon” push notification from the cellar’s drinking windows.
  • Personal taste: a taste survey that feeds the voice sommelier, and a tasting journal.
  • Buying again: a shared buy-again list that shows the three cheapest shops for each wine.
  • Everyday use: undo for changes, and a copy of the cellar that works offline on the phone.

The pilot is with friends and family, with more households invited in October 2026.

AI, engineered like a product feature

  • Measured, not assumed. An evaluation set of real label photos with known answers scores every model or prompt change: 13 of 14 correct, 2.7 s median, about 0.4¢ per read.
  • Model confidence isn’t trusted blindly. A misread came back at 0.85 confidence, so hard rules gate the result: a producer is required, and a vintage too unless the label says non-vintage.
  • The assistant can’t recommend what you don’t own. It only sees short codes for bottles in the cellar, and the server drops anything else.
  • Cost control: every AI call is logged and priced, with a monthly allowance per household and alerts at 80% and 100%.

Engineering underneath

Two layers of access control (API membership checks plus Postgres row-level security), a job queue inside Postgres, live updates over server-sent events, and every server-side fetch checked against private network addresses. Production runs as four containers: the database, the API, a background worker and the tunnel connector.

Personal details in the phone screenshots have been changed.

InVinetory's desktop cellar table.
The same codebase runs as a desktop web app, with a sortable cellar table and bulk actions.
Cellar view drawing a wine fridge as drawers by left, middle and right with a dot per bottle.
Each storage unit is drawn from its own levels, and bottles can be dragged between spots. Demo data.
Insights with bottle counts, a when-to-drink chart and breakdowns.

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