Compare · CoA verification · Ameya AI
Coatables vs Ameya AI: CoA verification compared
Ameya AI’s CoA Extractor reads a Certificate of Analysis, checks the results against a spec you provide, and flags anything out of range. If that’s the job, it does it. The question worth asking before you pick a tool: does it also check whether the lab’s own pass/fail was right?
At a glance
| Capability | Ameya AI | Coatables |
|---|---|---|
| Extracts analytes, results, units, limits to Excel/JSON | ✓ | ✓ |
| Checks results against a spec you provide | ✓ | ✓ |
| Recomputes pass/fail and reconciles it against the verdict the lab printed | — | ✓ |
The difference that matters: reconciling the lab’s verdict
Comparing a result to a spec tells you whether the number is in range. It does not tell you whether the “Pass” the lab printed was correct. Labs apply their own tolerances, and verdicts get copied across templates — so a CoA can show “Pass” on a row whose result is over the limit.
Coatables recomputes pass/fail from the result and the normalized limit, then compares that to the verdict the lab printed on the certificate. When they disagree, you get a verdict_mismatch — the moment a lab’s own mistake would otherwise have slipped through. A spec-comparison tool checks the result against your numbers; Coatables also checks the lab against itself. That reconciliation is the one thing it does that extract-and-compare tools don’t.
It also normalizes the notation that breaks naïve comparison (≤, NMT, NLT, ranges, Absent in 25 g, ND/<LOQ) and canonicalizes units across classes (mg/kg ↔ ppm, µg/g ↔ ppm, mg/g ↔ %) before deciding anything.
Built to be the fast, honest check
- Verdict reconciliation — the verdict is recomputed and cross-checked against what the lab printed, not just compared to a spec.
- Radically simple — no account, prices up front; verify a single CoA without onboarding a platform.
- Honest on scans — low-confidence fields are flagged for review, never silently accepted.
Drop in a certificate and see for yourself.