AI Inspection & Defect Reporting

Photograph a site; get a structured, drafted defect report before you are back in the van.

Discuss this See example work

Time to value: Pilot on historical photo sets in 3–4 weeks.
Deployment: Can run fully on your infrastructure
Category: Data, Knowledge & Documents

A man rubbing his eyes while writing at a desk late at night under a lamp

The manual version of this

Invictt AI layer

Capture

Detect

Classify

Draft

Review and issue

Two inspectors in hard hats smiling while reviewing a document together

Once it's AI-assisted

01/The problem

Field inspection is two jobs: looking at the thing, and writing it up. The write-up happens at night, from memory and a camera roll of two hundred photos, and it is the half that decides whether you get paid this month or next.

02/See it in action

What you'd actually see

A demonstration of the mechanism — not a live system reading your data.

Inspection report

Equipment
PPE — compliant
Work zone

Report drafted→ reviewer queue

03/How it works

The operating model

What enters, what the system does with it, and what reaches you.

Input

Capture

A mobile flow built for the actual conditions — one hand, gloves, bad light, no signal. Everything works offline and syncs when a connection returns.

System

Detect

Detection models trained on your defect categories, not a generic object set. Cracking, corrosion, water ingress, missing fixings — whatever your standard actually enumerates.

System

Classify

Each finding is assigned a severity against your grading scheme, with the model's confidence surfaced rather than hidden.

System

Draft

Findings are written up into your report template, in your standard's language, with the photographic evidence placed and captioned.

Human

Review and issue

The inspector corrects and approves — always. Their corrections feed straight back into the training set, so the model gets measurably better at your specific work.

Read the detail behind each stage

Capture

A mobile flow built for the actual conditions — one hand, gloves, bad light, no signal. Everything works offline and syncs when a connection returns.

Detect

Detection models trained on your defect categories, not a generic object set. Cracking, corrosion, water ingress, missing fixings — whatever your standard actually enumerates.

Classify

Each finding is assigned a severity against your grading scheme, with the model's confidence surfaced rather than hidden.

Draft

Findings are written up into your report template, in your standard's language, with the photographic evidence placed and captioned.

Review and issue

The inspector corrects and approves — always. Their corrections feed straight back into the training set, so the model gets measurably better at your specific work.

04/What's included

What you actually receive

Concrete deliverables, not a statement of intent.

Offline-capable mobile capture flow with background sync

Detection models trained on your defect taxonomy

Automatic association of photos to asset, location and job

Severity grading against your own standard

Drafted findings in your branded report template, exported to PDF

Reviewer interface for correction and sign-off

Correction feedback loop into the training set

Built with

YOLO
PyTorch
FastAPI
ONNX Runtime
PWA

Tooling is chosen per engagement. This is what this kind of build typically uses, not a fixed stack we sell.

05/Example work

All work

What this looks like in practice

Reference implementations, including what went wrong.

Reference Implementation2025

Drafting inspection reports before the inspector gets back to the van

Inspectors were spending their evenings writing up the day's sites from memory and a camera roll. We built an offline-first capture app and a vision model trained on their own defect taxonomy that drafts the report during the visit.

Read it

06/Also in this category