Compare · Document AI
Coatables vs generic document-AI extractors
Upload-anything extraction tools are genuinely fast and handle almost any layout, including scans, in many languages. They’re great when your documents are varied. The catch with a Certificate of Analysis is that they extract fields, not meaning — and on a CoA the meaning is the part that carries the liability.
Where generic document-AI slips on CoAs
- It can accept
<0.01as the value0.01— detection-limit notation becomes a real number. - It can treat
mg/Las if it weremg/kg, or readNMTwhere the spec saysNLT— inverting the test. - It captures the lab’s printed Pass/Fail verbatim, without ever checking it against the numbers.
None of those are reading errors you’ll notice in a clean-looking spreadsheet — which is exactly why they’re dangerous.
At a glance
| Generic document-AI | Coatables | |
|---|---|---|
| Extracts from any layout, fast | ✓ | ✓ (bounded to CoA formats) |
| Handles scans | ✓ | ✓ |
Normalizes ≤ / NMT / NLT / ranges / ND / <LOQ | — | ✓ |
Canonicalizes units by class (mg/kg ↔ ppm, mg/g ↔ %) | — | ✓ |
| Recomputes pass/fail from the numbers | — | ✓ |
| Cross-checks the lab’s printed verdict | — | ✓ |
| Flags low-confidence scan fields | — | ✓ |
When generic document-AI is the right pick
If you process many different document types and just need fields out, a general extractor is the pragmatic choice. Coatables is purpose-built for one job: turning supplement-lab CoAs into verified Excel/JSON.
Why Coatables
It reads every analyte, result, unit, and limit, then recomputes pass/fail and reconciles it against the verdict the lab printed — flagging verdict_mismatch, missing_limit, ambiguous_unit, and low-confidence scans. The point isn’t just having the data; it’s trusting it before it reaches your spreadsheet, LIMS, or ERP.