Independent reliability testing for vision AI

Site photos in. Proof out.

Your defect AI looks great in the demo. I test it on the photos real customers take: dark, shaky, compressed. Then I show you, with numbers, exactly where it breaks.

Scroll to walk inside

Outside

From the street it's a clean house.

Two storeys. Fresh render. Warm windows. Nothing to report.

01The problem

The demo photo and the site photo are not the same photo

A technician shoots a ceiling while walking down a dim corridor. The picture goes over WhatsApp. The crack sits half in shadow. That's the photo your model actually gets, and it's where a model that tested at 95% starts quietly making mistakes.

DemoSite
Demo. Daylight, steady hands, defect centred.Site. Dim stairwell, walking shake, WhatsApp.

Up the stairs

Even the stairwell looks perfect.

The prints on this wall are site photos. Keep an eye on them.

Under raking light

Hairline cracks. Damp behind the paint.

Light across the wall at a low angle, the way inspectors look. Now it shows.

Look up

A stain the chandelier hides.

A demo is the view from the street. A model's mistakes hide until someone looks closely. That's our job.

02The method

How a photo becomes proof

The photos come off the stair wall and go through the same five steps your model will. Hover one to inspect it.

Step 01 / 05Collect

Collect real photos

Your customers' photos, our real-world set, or both. Every photo is labelled by a person first.

Obvious defectBorderlineClean

Step 02 / 05Condition · dark

Turn the lights down

Each photo gets darker versions, like the light in a dim stairwell or an unlit basement.

γ 1.8γ 2.6γ 3.5

Step 03 / 05Condition · shake

Shake and squeeze

Walking shake, cheap sensors and WhatsApp compression. What happens to every site photo before your model sees it.

Blur 7 px15 px25 pxJPEG q8

Step 04 / 05Score

Run your model. Check every answer.

Your model sees every version. Its answers are checked against the human labels, defect by defect and box by box.

PrecisionRecallPer typeIoU

Step 05 / 05Calibrate

Does 90% sure mean 90% right?

Every photo becomes a point of light. On the gold line, the model's confidence can be trusted. Each thread shows how far a point is from it.

CalibrationReview rateError left

03The Real-World Reliability Audit

What you get in 10 business days

Real numbers, not "your model performs well."

01

Accuracy you can quote

Defects caught and false alarms, broken down by type: cracks, damp, peeling, mould, stains.

02

Bad-photo stress test

How much performance drops in the dark, with blur, and after compression.

03

Confidence check

Whether a 90% score means 90%, so the scores can be used for decisions.

04

Human-review rule

Which photos still need a person, and the error rate on everything else.

05

Failure catalogue

The photos it got wrong, grouped by the pattern behind each mistake.

06

One-page summary

An independently tested result you can show your team or your customers.

07

A straight answer

Ready, ready with a review rule, or not ready yet. If it isn't ready, you'll hear that too.

04Who it's for

Built for teams whose product is a vision model

Main

Companies building photo AI

  • Inspection and survey software
  • Property condition and move-out tools
  • Construction defect and punch-list AI
  • Damage and claims assessment

You need proof your model holds up outside the lab, and a list of what to fix next.

Also

Large teams that use it

  • Multi-inspector firms
  • Property managers with 1,000+ units
  • Contractors piloting AI on site

You want to know whether to trust your vendor's AI on your own photos.

05Your data

Yours before, during and after

You own your photos and your model. Nothing is reused or shared without written permission. Faces and number plates are blurred before analysis. An NDA is signed on day one, and everything is deleted when the audit ends unless you ask me to keep it.

06Pricing

Fixed price. Fixed scope.

Founding clients · 2 places
$1,750

The full audit, for a testimonial and an anonymised case study. Your name is only used if you agree.

Reliability audit
$3,500

10 business days. About two hours of your team's time. Credited toward an eval suite if you continue within 30 days.

Every release, re-tested
$1,500 /month

From $1,500. Each new model version is checked before it ships, so accuracy never drops quietly.

No new finding, no feeIf the audit doesn't find at least one failure pattern you didn't already know about, you don't pay.

07Book a call

Find out where your model breaks

Twenty minutes. Tell me what your model detects and what photos it sees. I'll tell you honestly whether an audit would show you anything useful.

afzal@visionprooflabs.com