What Is Nanonets?

Last updated July 2026.

If you have looked into automating document data entry, Nanonets is one of the first names you run into. It shows up in accounts-payable guides, invoice automation comparisons, and lists of OCR tools. The part worth getting clear on is what it actually is and where it fits, because Nanonets is more than an extraction tool: it has grown into a broad workflow and accounts-payable automation platform. This article explains what Nanonets is, what it is used for, whether it uses OCR, what it costs, where its limits show up, and when teams pick a more focused alternative.

What is Nanonets?

Nanonets is an AI-powered intelligent document processing platform that extracts structured data from unstructured documents and automates the workflows around them. It reads documents such as invoices, receipts, purchase orders, contracts, bank statements, and forms, turns them into structured fields, and can route that data through approval and posting steps into your accounting or ERP system. You can use it through a drag-and-drop dashboard or through an API. Underneath, it pairs optical character recognition with machine-learning models that learn to pull the right fields from each document type.

What is Nanonets used for?

Nanonets is used to take data off documents and feed it into downstream systems without manual keying. The most common use is accounts payable: capturing invoice fields, matching them to purchase orders, routing them for approval, and posting them to the books. Beyond AP, teams use it to process receipts for expense reports, read order forms and purchase orders, pull fields off contracts, and digitize forms. The pitch is a single platform that handles capture, extraction, validation, approval, and integration, rather than a tool that only returns raw text.

Does Nanonets use OCR?

Yes. Nanonets is built on optical character recognition combined with machine learning. OCR converts the image of a document into text, and trained models identify which pieces of that text are the fields you care about, such as the vendor, the invoice number, the date, and the total. OCR on its own returns characters and their positions on the page. A full extraction workflow goes further and returns labeled fields, validated and ready for your systems, which is the layer that makes the output usable without someone re-reading the document.

What workflow and automation features does Nanonets have?

This is where Nanonets is broad. On top of extraction it offers 2-way and 3-way purchase-order matching, approval routing where reviewers can approve from email, Slack, or Teams, and a set of pre-built integrations with accounting and ERP systems including QuickBooks, Xero, NetSuite, Sage Intacct, SAP, Microsoft Dynamics, and Oracle, plus Zapier for everything else. That breadth is the product's main strength for teams that want a full accounts-payable automation suite rather than just data extraction. If your goal is end-to-end AP, those features matter. If your goal is clean fields out of a document, they are surface area you may not need.

How much does Nanonets cost?

Nanonets bills per block run, not per page. A block is one step in a workflow: extract a field, classify the document, format a value, post to an integration. Run five steps over one document and you have five billable runs. These figures were read from Nanonets' own pricing page in July 2026.

By Nanonets' own example, a typical invoice workflow runs 4 to 6 blocks and lands under $2 per invoice end to end. Because the unit is a workflow step rather than a page, that number does not convert into the per-1,000-pages rate every cloud OCR API quotes, and comparing the two without saying so produces a difference of roughly fifty times that means nothing. Two teams processing identical page counts can get very different bills depending on how many steps their workflows run. Note also that two of the four tiers publish no price, and the dollars-per-credit ratio is not published anywhere. We work through the full arithmetic on our Nanonets pricing breakdown. Confirm current numbers on the Nanonets pricing page before you budget.

What are the limitations of Nanonets?

Nanonets is a mature, capable platform, and most limitations teams cite are about fit rather than quality. The block-based pricing with add-on charges can be hard to forecast. The breadth of the workflow and automation features means more to configure and maintain than a team that only needs extraction wants to take on. And as with any extraction tool, getting unusual or low-quality layouts to extract cleanly can take some configuration and tuning. None of these make it a bad tool. They are the trade-offs of a broad platform, and they push teams with a narrower need toward a more focused product.

What is the difference between Nanonets and a focused extraction tool?

A focused extraction tool does one job well: it classifies a document, reads it, extracts the fields you define, validates them, sends low-confidence values to a reviewer, and exports clean data, then leaves the downstream automation to you. A broad platform like Nanonets bundles that extraction with matching, approval routing, and a deep set of integrations. Neither approach is wrong. The right choice depends on whether you want a full automation suite or accurate fields you can drop into the systems you already run. The honest test is to run your real documents through both and compare accuracy and all-in cost per page for your volume.

When should you use a Nanonets alternative?

Consider an alternative when you mainly need accurate extraction, want pricing you can predict, or do not want to stand up and maintain a broad automation platform for a single use case. DocuOCR is a focused Nanonets alternative: it classifies a mixed file, reads any layout, extracts the fields you define, validates them, routes uncertain reads to review, and exports clean data through a dashboard and one REST API, with a single inclusive per-page price and no add-on charges. It is built on intelligent document processing, uses document classification to sort a mixed batch automatically, and exposes the same workflow through an OCR API for developers. If you understand the difference between recognizing text and returning structured fields, the guide on OCR versus data extraction is a good next read.

One more note on scope: if your real goal is full accounts-payable automation, with invoice capture flowing straight into approvals and posting, a dedicated accounts payable automation workflow may be the better next step for that specific job, while you keep extraction itself simple and predictable.

Nanonets is a strong, established platform, and for teams that want a full AP-automation suite it earns its place. For teams that want focused, accurate document data extraction with a price they can plan around, a more focused product is often the better fit. The way to decide is to test both on the documents you actually process and compare the accuracy and the all-in cost.