Happy Webs

Spot the Defect Before It Leaves the Bay.

You don't need a million-pound machine-vision rig to catch quality issues. A modern AI model running on a phone or a £400 fixed camera can spot missing welds, wrong components, surface defects, and assembly errors, instantly, consistently, on every part. We build the pipeline that turns your existing photos into a quality safety net.

A shop-floor worker using a digital production system
A practical industrial workflow, not a generic AI demo.

The problem, in plain English.

01

QC is the most boring job in the building

Eyeballing 300 identical parts for the one with a missing weld is exactly the work humans are worst at. Attention drifts. Defects slip through. Costs end up on your warranty bill, not your QC line.

02

The good QC person leaves

Quality control depends on experience, knowing what to look for, what "normal" looks like for this product. When that person retires, quality dips for six months while you train the next one.

03

Customers want photo evidence anyway

Modern B2B customers ask for pre-dispatch photos. You're already taking them. You're not yet using them to actually inspect, they're just sitting on someone's phone.

04

Off-the-shelf machine vision is overkill

Industrial vision systems are £20k+ per station and assume a production line that runs the same product all day. For SMBs with varied work, that maths never works.

What actually happens.

01

Define what "right" looks like

A handful of reference photos and a written checklist per product type. We translate that into a structured inspection schema the AI can apply consistently.

02

Capture however you already capture

Phone, fixed camera, drone shot of a roof, scanner output. The same model handles any image source. No specialist hardware in 90% of cases.

03

Inspect against the schema

For each photo: pass/fail per criterion, with reasoning. Missing weld? Flagged. Wrong colour finish? Flagged. Damaged corner? Flagged. Clean? Through to dispatch with a confidence score.

04

Build an audit trail

Every inspection, every decision, every reason, stored against the job. When a customer raises a warranty claim six months later, you can prove the part left your facility right.

Fixed fee, phased delivery.

£3,200-£10,000 for build, ~£150-£400/month for hosting + accuracy monitoring

From £3,200

QC inspection

  • Workflow audit + scope
  • Build, integrations, and tuning to your real data
  • Live deployment in your environment
  • Ongoing tuning + accuracy monitoring

What people ask us.

01Is this actually accurate enough to trust?

Yes, provided you set the confidence thresholds sensibly and keep humans in the loop on edge cases. We typically run with the AI handling 80-90% of inspections fully autonomously, with the rest reviewed by a human. The compound effect is that humans only look at hard cases, where they're much more reliable.

02What about ISO 9001 and traceability?

Every decision is logged with the input image, the schema applied, the confidence scores, and the outcome. That's a cleaner audit trail than a paper QC log signed by a person who looked at 200 parts that day.

03Does this replace our QC inspector?

It changes their job, not eliminates it. They become the person who reviews flagged cases, tunes the system, and handles complex inspections, much less boring, much more skilled. Most clients keep the same headcount and just take on more work.

04What about parts that need physical measurement?

AI vision can't replace a vernier gauge. But it can flag "this looks visually wrong, take a measurement", which is often the trigger that's missing today.

05Where does the data go?

Photos and decisions stay in your environment. Models we use don't train on your data. We can run fully on-premise if that's a requirement.

Ready to win more
and do more with
less staff?

  • More enquiries & better rankings
  • Smoother operations & less admin
  • Faster quoting & better follow-up
  • More jobs completed, without hiring more

Book a free strategy call

and let’s map out your clear next step.

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