Nanonets is an AI document processing platform that extracts data from invoices, receipts, purchase orders, and IDs, then routes it through approval and export workflows. It learns from corrections and connects to accounting tools like QuickBooks and Xero.
Nanonets is an AI-powered document processing platform aimed at one of the least glamorous problems in business: getting data off invoices, receipts, and forms and into your systems without a human retyping it. It layers machine-learning extraction on top of OCR, then wraps the results in workflows — validation rules, approval steps, and exports to the tools where the data actually needs to live.
What separates it from a bare OCR engine is that it learns. When a reviewer corrects a misread field, the model takes the correction on board, so accuracy on your specific document mix improves over the first few weeks of use. That matters, because every company's invoices look different, and static templates break the moment a supplier redesigns theirs.
You feed documents in by email forwarding, API, drag-and-drop, or connected storage like Google Drive. Nanonets classifies each document, extracts the relevant fields — invoice number, line items, totals, tax, PO references — and checks them against rules you define, flagging anything suspicious for human review. Clean records then flow out to accounting and ERP systems; QuickBooks, Xero, and SAP integrations are the popular routes, with webhooks and a REST API covering everything else.
Typical deployments: accounts-payable teams automating invoice capture and three-way matching, expense teams processing employee receipts, operations teams digitizing purchase orders and delivery notes, and onboarding flows that verify ID documents. It handles both neat digital PDFs and messy scans, and copes reasonably well with documents it has never seen before.
Nanonets has shifted to a usage-based model: a free starter tier comes with a pool of credits, and paid plans (Growth and Enterprise, both custom-quoted) draw down credits as documents move through workflow steps. Be aware that older per-month tiers still circulate on review sites, so quotes you find in comparison articles may describe a pricing generation that no longer exists — get current numbers from Nanonets directly.
Finance and operations teams processing hundreds to many thousands of documents a month, where the payback on automation is measured in staff hours. It's overkill for occasional use — the setup and tuning effort only pays off at volume — but at scale it can genuinely take a full-time data-entry burden off a team.
We rate Nanonets as one of the more complete mid-market options in document AI. The extraction quality is strong, the learn-from-corrections loop visibly works, and the workflow layer means you get an end-to-end process rather than a raw API you must build everything around. The review screen is well designed: a human can clear a queue of flagged documents quickly, which is where these systems live or die.
Our gripes are about predictability. The move to credit-based pricing makes costs harder to forecast than a flat per-page rate — complex workflows with several AI steps consume credits faster than you'd guess — and because Growth and Enterprise plans are custom-quoted, you can't self-serve a real price without a sales conversation. Accuracy on handwriting and very degraded scans also still needs the human review loop. If you want a simpler, cheaper extraction-only tool, look at FormX.ai; if you want everything on your own servers for free, Tesseract plus elbow grease remains the fallback.
