The extraction looked fine. The confidence score said so.
Then the audit hit and nobody could explain where the number came from.
A 90% score means nothing unless 90% of those predictions are actually correct, and you can trace each one to its source.
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Most PDFs in a back office were never scanned. The text is already in the file.
Flash reads the text layer and grounds it in the layout. No model call. 7 credits/page instead of 25, same blocks and bounding boxes as every other Parse mode.
anyformat.ai/blog/parse-flas…
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We ran 11 parsers. The one that topped the benchmark cost less than most below it.
anyformat: 78.1% parse, $25 per 1,000 pages, calibrated confidence, visual citations.
More models gave teams more invoices, not more production signal.
Full breakdown 👉
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The map ends where the work begins.
Validation, routing, orchestration, compliance: none of it shows up in the output JSON.
On a 2026 benchmark of 1,000+ real documents, anyformat scored 78.1% to AWS Textract's 65.4%.
👉 anyformat.ai/
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Your document workflow shouldn't sit in a developer backlog.
In anyformat Studio, ops teams build the workflow, write the
validation rules, and set the confidence thresholds themselves.
No code.
When operations owns the pipeline, the work doesn't queue.
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Our L'Oréal deployment numbers speak for themselves.
99% extraction accuracy, validated on 1,500+ monthly invoices in live production.
60% less processing time: from 38 hours a week of manual entry to validating only the uncertain fields.
The work earned the stage.
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Knowledge Base is live in the anyformat platform.
One node on the canvas, no wiring: it answers questions over every document the workflow already parsed, and cites the file, the page and the region.
anyformat.ai/blog/knowledge-…
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Still water holds more than it shows.
What customers see is the surface: clean, structured data arriving reliably.
What holds it is every layer underneath. Parsing. Validation. Compliance. Confidence routing.
The surface is calm because the depth is sound.
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annie builds your extraction workflow, then improves it.
Describe it in plain language. She composes parse → classify → extract, flags what doesn't reconcile, hands it back for approval. Then tunes it against your real examples.
No templates. No training data.anyformat.ai
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anyformat is at @HumanXCo Amsterdam, 22 to 24 September.
- Startup Kiosk SK11 all three days.
- @juan2text pitching Wed 23 at 16:18.
Bring your messiest documents, the bad scans and the nested tables. That is the demo we like doing.
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Document AI told you what a document said. It never told you when to look.
Slack alert runs at the end of a branch. Document in, message out. No dashboard, no queue.
👉 full info:
anyformat.ai/blog/workflow-a…
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Document AI told you what a document said. It never told you when to look.
Slack alert runs at the end of a branch. Document in, message out. No dashboard, no code.
Live in the platform
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Short-doc benchmarks don't predict production. The gap opens at 16+ pages.
anyformat holds 80%. GPT-5.6 drops to 53%. Claude Opus 4.8 and Gemini 3.5 Flash both fall to 27%.
Full benchmark, link in bio 👉
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Document AI has always run one way: documents in, data out.
Edit runs the other way. Blank form in, filled PDF out. No template, no coordinates. The fields are read off the form itself, so an unseen layout works like any other.
👉 full info: anyformat.ai/es/blog/edit-fi…
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Document AI has always run one way: documents in, data out.
Edit runs the other way. Blank form in, filled PDF out. No template, no coordinates. The fields are read off the form itself, so an unseen layout works like any other.
Live in the platform
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A 90% score means nothing unless 90% of those predictions are actually correct.
Three tests. Most systems fail two.
→ Plot confidence against real accuracy.
→ Check if calibration is per-field or global.
→ Run it on your hardest documents first.
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Every model looks accurate on a one-page invoice. The benchmark data shows what happens on page 30.
We call it Context Decay: extraction accuracy degrades silently as documents get long. And we have fixed it.
👉 anyformat.ai/blog/anyformat-…
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Build vs. buy in document automation quietly eats six months.
Build in-house and you own the pipeline: OCR, the LLM layer, validation, human review. The cost shows up around month six, when suddenly you have a team devoted to fixing edge cases instead of your backlog
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The extraction runs. No error appears. The output looks clean...but rows have vanished.
Push a table to 50 pages (~2,400 rows). GPT-5.6 and Gemini degraded by page 20.
anyformat held ~99% row recovery 👉 anyformat.ai/blog/anyformat-…
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Every model looks good on page one.
Production is a different beast. We suffered it. We fix it. Do not reinvent the wheel.
👉 anyformat.ai/blog/anyformat-…
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