Case Study: Reliability-First Mobile AI


Problem

A mobile identification app had clear demand, but retention was suffering because users didn’t trust the output. False positives were especially damaging — the app would confidently identify random objects as valid targets, causing users to quickly decide the product “doesn’t work.”


Diagnosis

This wasn’t a “train one model and hope for the best” situation. The real blocker was reliability: dataset quality, inconsistent categories, and missing pipeline controls were preventing measurable improvement over time.


Approach

I proposed (and shaped) an end-to-end system that keeps inference on-device for speed and offline use, while using backend services for submissions, expert review, retraining, evaluation, and controlled model releases.


Critically: I delivered a working proof-of-concept that proved end-to-end feasibility and measurably reduced false positives, confirming the approach before full production investment.


Key design decisions

  1. Stop false positives first with a staged pipeline: detect “point vs not point” before classification, so the app can honestly say “no point detected” rather than forcing a wrong label.
  2. Provide top-3 + confidence + uncertainty handling to improve trust and reduce overconfident wrong answers.
  3. Build a human-controlled improvement loop: hard cases → expert review → versioned dataset snapshots → retrain → evaluate against stable test sets → release models in controlled batches.
  4. Add dataset quality tooling (quality scoring, duplicate detection, labeling UI, customer-editable taxonomy) so training data becomes a managed asset instead of a constant source of chaos.


Results / Outcome

The product path shifted from “accuracy as a one-time deliverable” to a measurable reliability system: reduce false positives first, then continuously improve category performance through controlled feedback and model releases — improving user trust month by month.


Takeaway

Accuracy is not the deliverable. Reliability is a system — and the POC is the proof it works.