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Building & Shipping an AI Feature

🗓 May 31, 2026 • ⏱ 3 min read

From idea to shipped feature

This final lesson ties the course together with a repeatable workflow you can apply to any Mobile AI feature — using a concrete example: “scan a receipt and auto-fill an expense form.”

Step 1 — Define the job clearly

Write down exactly what the feature does and what “good enough” means. For our example: detect text on a receipt, extract the total and date, and pre-fill the form — correct most of the time, with easy manual correction.

Step 2 — Choose the approach

Apply the on-device vs cloud framework. Receipts are sensitive and the user may be offline, and text recognition is a solved on-device problem — so use on-device OCR (ML Kit / Vision), with optional cloud parsing only if needed.

Step 3 — Build the pipeline

  1. Capture — let the user photograph the receipt (with a guide overlay).
  2. Pre-process — crop, correct orientation.
  3. Inference — run OCR to get the text.
  4. Post-process — use rules/entity extraction to find the total and date.
  5. Present — pre-fill the form and let the user confirm/edit.
val text = recognizer.process(image).await()          // OCR
val total = extractAmount(text.text)                  // your parsing logic
val date = extractDate(text.text)
form.prefill(total = total, date = date)              // user can edit

Step 4 — Design for trust and errors

  • Show what was detected and make every field editable.
  • Indicate low confidence (“please check the total”).
  • Provide a fully manual path if scanning fails.

Step 5 — Optimise & test

  • Run inference off the main thread; keep capture smooth.
  • Test on real low-end devices and with messy, real-world receipts.
  • Measure latency and accuracy; iterate on pre/post-processing.

Step 6 — Ship and learn

  • Roll out gradually; watch crash and performance metrics.
  • Collect (with consent) feedback on wrong results to improve parsing.
  • Improve over time — swap in a better model or move parsing server-side without changing the UX.

A reusable checklist

  • Is the on-device vs cloud choice right (privacy, offline, cost)?
  • Is inference off the main thread and fast on low-end phones?
  • Are confidence, errors and a manual fallback handled?
  • Is sensitive data protected, and is the user informed?
  • Did optimization keep accuracy acceptable?

Common mistakes

  • Shipping the “happy path” only, with no correction or fallback.
  • Adding AI for its own sake instead of solving a real user problem.
  • Not measuring real-world accuracy after launch.
Summary: Ship AI features with a clear workflow: define the job, choose on-device vs cloud, build the capture → pre-process → infer → post-process → present pipeline, design for trust and failure, optimise and test on real devices, then roll out and keep improving. Solve a real problem — not AI for its own sake.