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As of 13 August 2026, AI can only partly compare supplier samples with your approved sample.
Most people should hand this to a purpose-built tool.
Can you do it?
15 minutesto a draft.
30 minutesto something you’d act on.
Cost, all in£0
Skill neededchat-fluent
Who has to check ita colleague
What the alternative costsNanonets is an AI document-processing tool for invoices, receipts and forms, rather than a physical sample inspection service.
If this goes wrong: a non-conforming supplier sample is accepted and reaches production, customers or further inspection before the defect is found.
What to actually do
Use a tool built for this
The route this page recommends
Do it yourself
Second choiceA chat interface, chat-fluent skill, and roughly 30 minutes until you can act on the result.
How to actually do it
- Open the current approved sample specification and record its reference number, revision, tolerances and acceptance criteria.
- Photograph the approved sample and each supplier sample from the same angles, using a ruler or other scale where dimensions matter, and label every image with the sample and inspection area.
- Gather the supplier's batch, lot, material and production information, then paste the factual details and the specification into the prompt.
- Upload the labelled photographs and supporting notes to a multimodal chatbot and run the comparison prompt.
- Copy the model's comparison into the quality inspection record and mark every unable-to-assess item for a physical check.
- Have a quality colleague compare each flagged point and each pass or fail observation against the actual approved and supplier samples.
- Record the colleague's measurements and decision in the controlled quality system, and send the supplier the confirmed non-conformities or approval outcome.
Prompt
Compare the attached supplier sample images and notes with the attached approved sample images and specification. Treat the approved sample as the reference. Create a table with these columns: inspection area, approved sample evidence, supplier sample evidence, difference, pass or fail or unable to assess, confidence, and physical check required. Assess only what is visible or explicitly measured. Do not infer hidden properties such as material strength, chemical composition, durability, odour, weight, dimensions not shown, or compliance with a standard. Flag lighting, camera angle, scale and image-quality problems. Do not make the final accept or reject decision. End with a short list of checks a quality colleague must perform on the physical samples before release. Context: [product and batch]. Acceptance criteria: [paste the current specification]. Supplier sample identification: [supplier, lot or batch, date]. Approved sample identification: [reference number and revision].
Open it prefilled in ChatGPT or Claude, or copy it into Gemini, which takes no prefill link.
Hand it to a person
The distant thirdA person who owns the outcome does this end to end, worth it when the failure is dear.
What it gets wrong
- AI cannot inspect texture, odour, weight, strength or other properties that photographs and supplied notes do not capture.
- AI cannot guarantee that different lighting, camera angles or colour reproduction have not distorted the visual comparison.
- AI cannot decide whether an ambiguous difference is acceptable under your quality system or customer agreement.
- AI does not carry responsibility for releasing a non-conforming sample, so your quality team still owns the decision and evidence.
What caps this at PARTLY: physical presence, judgement under ambiguity and stakes of error.
How we scored this
Five axes, each scored nought to two by hand: ten means AI carries the task cleanly, and the thresholds that turn a total into YES, PARTLY or NO are published in the methodology. Each axis name links to its definition.
| Axis | Score (0–2) |
|---|---|
| Output | 2 |
| Inputs | 1 |
| Verification | 1 |
| Liability | 1 |
| Effort delta | 1 |
| Total | 6 / 10 |
The methodology and its thresholds are published in full.
FAQ
- Can ChatGPT compare two product samples?
- It can compare photographs, measurements and written specifications and organise visible differences into a table. It cannot replace a physical inspection of properties such as texture, weight, odour or strength.
- Can AI tell if a supplier sample matches an approved sample?
- It can identify apparent differences when you provide consistent images, measurements and acceptance criteria. A quality colleague must confirm the result against the physical samples before the supplier sample is accepted.
- How do I use AI for supplier quality control?
- Photograph the approved and supplier samples from matching angles, include scale and paste the current specification into a multimodal chatbot. Ask for evidence-based differences and a list of physical checks, not an automatic release decision.
- Can AI approve a supplier sample?
- No, not safely on its own. AI can prepare comparison evidence, but your quality decision-maker must verify the sample and record the approval or rejection.
Nearby answers
Assessed by gpt-5.6-luna (gpt-5.6-luna) on 2026-08-13, second-checked by an independent model. Wrong somewhere? Email [email protected] and it gets re-checked.
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