---
title: "Run a computer vision proof of concept"
description: "A four-step playbook to test whether computer vision can support a real decision, using your own images and a clear pass-or-fail test agreed up front."
url: https://lidvizion.ai/guides/run-a-computer-vision-proof-of-concept/
site: Lid Vizion
index: https://lidvizion.ai/llms.txt
---

[Field guides](https://lidvizion.ai/guides/) 4 steps

# Run a computer vision proof of concept

A four-step playbook to test whether computer vision can support a real decision, using your own images and a clear pass-or-fail test agreed up front.

For: Operations and product owners who suspect cameras or photos could replace manual checks.

Vision demos look impressive on clean sample images and disappoint on real footage. A useful proof of concept tests one decision, on your own cameras and conditions, against a pass-or-fail test agreed before anything is built. The result is either a clear go-ahead with known limits, or a cheap, early no.

## Step 1 — Pick one decision

**Goal:** Tie the proof of concept to something a person decides today.

1. Name the decision: a count, a defect, an empty space, a movement, a missing item.
2. Describe who makes it now, how often and what a wrong call costs.
3. Decide what the system should do with its answer: act, alert or ask a person.
4. Agree the pass-or-fail test before collecting any data.

**You finish with:** A one-page brief with the decision, the stakes and the success test.

## Step 2 — Collect representative images

**Goal:** Test on the conditions the system will really face.

1. Gather images or clips from your actual cameras, sites and times of day.
2. Include the hard cases: poor light, clutter, unusual angles and rare events.
3. Label a sample with the correct answer, checked by the person who makes the decision today.
4. Remove or blur anything that identifies people unless it is essential and approved.

**You finish with:** A labelled, representative sample set.

## Step 3 — Build and test the smallest model

**Goal:** Find out quickly whether the approach can work.

1. Start from an existing model where one fits, and adapt it only if needed.
2. Measure missed items and false alarms separately on held-back images.
3. Review failures with the domain expert to see which are fixable.
4. Estimate what running it for real would need: cameras, compute and review time.

**You finish with:** Measured results against the agreed test, with failure examples.

## Step 4 — Decide go, adjust or stop

**Goal:** Turn the result into a clear next step.

1. Compare results against the pass-or-fail test, not against a demo.
2. If it passes, scope a pilot with a person reviewing uncertain cases.
3. If it nearly passes, list exactly what data or changes would close the gap.
4. If it fails, record why so the question is not reopened without new evidence.

**You finish with:** A written decision with limits, next steps and owner.

## Common pitfalls

- Testing on clean demo images instead of your own footage.
- No success test agreed before building.
- Treating one accuracy number as the whole story.
- Collecting images of people without a clear, approved reason.

Want a hand getting started?

## Start with a workflow audit

We pick the workflow with you, set the baseline and write the bounded design.

[See the audit](https://lidvizion.ai/workflow-audit/) [Talk this playbook through](https://lidvizion.ai/contact/?need=milestone-delivery&from=%2Fguides%2Frun-a-computer-vision-proof-of-concept%2F#write)

## See it as a workflow

- [Visual quality inspection](https://lidvizion.ai/use-cases/visual-quality-inspection/): Manufacturing & quality Check parts or products from camera images for visible defects or missing components, and send uncertain cases to an inspector instead of guessing.

## More guides like this

- [Decide whether to build, buy or configure](https://lidvizion.ai/guides/decide-build-buy-or-configure/)
- [Use your existing cameras for computer vision](https://lidvizion.ai/guides/use-existing-cameras-for-computer-vision/)

## Related answers

- [When should a company build custom AI software instead of using existing tools?](https://lidvizion.ai/answers/when-should-a-company-build-custom-ai-software/)
- [What can computer vision do for a business?](https://lidvizion.ai/answers/what-can-computer-vision-do-for-a-business/)

Bring the messy version

## One workflow. A better way to run it.

Show us the tools, the handoffs, and the part that keeps slowing your team down.

[Let’s map your workflow](https://lidvizion.ai/contact/) · [Read our approach](https://lidvizion.ai/approach/)