The deterministic document platform for agents and humans. SDKs, cloud APIs, integrations, and workflow automation turn documents into reliable workflows.
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Fast answers, straight from the document. Watch the mailroom cat make a decision with Nutrient Data Extraction.
Most questions you ask about a document have a short answer.
Is this an invoice or a contract? Is this extracted value actually on the page? Where does one invoice end and the next begin?
Yet the usual approach is to send the whole file to a general-purpose LLM and wait while it writes a reply, one token at a time.
So we built something different: the Nutrient document decision model.
It reads a document, both the page image and the text, and returns a choice plus a calibrated confidence score. No generated paragraphs, no waiting.
What that gets you:
→ About 200 ms per answer
→ Scores calibrated to how often the model is actually right
→ Visual understanding that text-only approaches miss
It’s already live in Nutrient DWS, powering our classify and split endpoints and checking extraction results against the source document.
Nutrient AI engineer, Austin Nguyen, explains why we built it, how we trained it, and where it falls short:
bit.ly/4jpIZyF
Try the demo on Hugging Face:
bit.ly/4i8tEjE
Building on the Nutrient Data Extraction API is a breeze.
Our customer came to us with a problem: they receive invoices from all across the globe. Different languages, different currencies, and each one checked by hand.
Not anymore.
In less than 30 minutes, we built them a custom app that identifies discrepancies, and notifies their accounts team when something needs their attention.
All built inside of Claude using only natural language.
If you can't trust your OCR some of the time, can you trust it at all?
OCR turns a scan or photo of a page into text your software can use. Clean, typed pages are a breeze.
Real documents aren't. A rotated scan, small print, a long table, a handwritten form: that's where a number gets misread or lands in the wrong row.
And the response still looks fine. Your agents can't tell. If you aren't looking closely, you won't either. But someone still has to sign off on it.
So the question isn't whether OCR works on an easy page. It's whether you can tell when it got something wrong.
We compared nine OCR APIs, including ours, and we didn't pull any punches. The results might surprise you.
Nutrient links extracted content back to where it appears on the page and marks the values that need a second look, so you know what to check.
Read more: bit.ly/4rQppOj
ALT Dark grid-background landing page graphic with large orange text reading “OCR API” across the center, surrounded by small image tiles of bees, orange mushrooms, an orange flower, and a honeycomb. Small labels read “FEATURES,” “PRICING,” and “USE CASES.”
You don't hand an AI agent autonomy. It earns it.
SpaceXAI Engineer Lauren Tan (@poteto), who ships thousands of code changes a month with AI agents, puts it simply: "Make loops autonomous only after it earns your trust." The agents have to show their work, and any step can stop the work when something looks off.
The same is true when the agent works on your documents instead of your code. Before an AI agent fills a form or redacts a clause in a real contract, someone has to sign their name off.
The trust comes down to three answers about your docs: who set it up, what permissions the agent has, and has anything changed?
Nutrient AI Assistant 2.4.0 keeps those answers on file:
• Every agent's setup is saved and versioned, so you know exactly which version ran on any given day.
• Edits can wait for a person's approval, so someone can stop the line before anything changes.
• Each session reaches only the documents and agents it was issued for, and narrow jobs can go to helpers that can only read.
Start with relatively strict permissions. Loosen them as the agent proves itself, and keep records of every change.
How it works: bit.ly/4rN1O0M
ALT Two white document panels on a brown background are labeled DOCX on the left and AI on the right, with a pink square of dots between them suggesting conversion or processing. Small decorative dot clusters are scattered around the background.
In AI-powered workflows, document infrastructure isn't just a supporting feature. It's fundamental.
When AI prepares a tax return, accountants still need to check it. That means tracing every number back to its source, checking the calculation, and marking up what needs attention, all inside the platform instead of across a patchwork of tools.
That's the approach Current took when building their AI-native tax platform on Nutrient.
"This product wouldn't work without document editing," says @tuckerhaas, CTO at Current.
Read the story 👇
bit.ly/3VYoY8u
ALT “This product wouldn’t work without document editing. Our entire tax automation product, we need some way to display documents. That’s a very core part of it.” Tucker Haas Chief Technology Officer
AI answers are useful. But answers your software can actually act on exponentially more powerful.
Meet DocSignals: Nutrient document extraction + Jev, TypeSafe’s new AI model.
Ask a question about a document. Get a structured signal and a citation you can inspect. Then define what happens next: route the document, trigger an automation, or bring in a person to review.
Probabilistic signals. Deterministic workflows.
Get started for free.