Nanonets
AI-powered document processing and AP automation.
About Nanonets
Nanonets extracts structured data from invoices, purchase orders, receipts, and other financial documents using AI models you train on your own document samples. Unlike generic OCR tools that struggle with non-standard layouts, Nanonets lets you upload examples of your specific forms and build a custom extraction model around them. That makes it practical for manufacturers, distributors, or any enterprise dealing with supplier invoices that don't follow a clean template. The AP automation layer handles three-way PO matching, routes exceptions to the right approvers, and syncs confirmed data to QuickBooks, NetSuite, SAP, or Xero via API. This tool fits mid-to-large AP teams processing high document volumes with inconsistent formats. A ten-person accounting firm running standard vendor invoices through QuickBooks will get less from Nanonets than from a simpler, cheaper tool. The value shows up when your team manually re-keys data from PDFs with unique field layouts, or when your current OCR solution throws too many exceptions back to staff. The main friction points: custom model training has a real learning curve and requires a sufficient volume of labeled document samples before accuracy stabilizes. The API-first design means non-technical teams depend on IT or a developer to stand up integrations properly. And the pricing structure is not self-serve transparent, so budget conversations require a vendor call.
Best for
Enterprises with complex or unique invoice formats wanting customizable AI document extraction
Key Features
- Custom AI model training for unique document formats
- Automated invoice data extraction and processing
- Purchase order matching and three-way matching
- Configurable approval workflows for AP automation
- API-first architecture for enterprise integrations
Pros & Cons
Pros
- Custom model training handles non-standard invoice formats that break generic OCR tools, including handwritten fields and industry-specific document types
- Three-way PO matching flags quantity and price discrepancies before invoices reach the payment queue, reducing manual exception handling
- Configurable approval workflows route invoices by amount, vendor, department, or exception type without requiring IT involvement once set up
- API-first architecture integrates directly into existing ERP and accounting stacks including SAP, NetSuite, QuickBooks, and Xero
- Extraction accuracy improves over time as the model sees more of your specific documents, unlike fixed-template OCR that degrades with format changes
- Supports bulk document processing, useful for month-end close or high-volume AP runs without manual batching
Cons
- Custom model training requires a meaningful sample set of labeled documents before accuracy is reliable — early-stage use produces more exceptions, not fewer
- API-first architecture is a dependency risk for non-technical AP teams; integrations need developer involvement to configure and maintain
- Pricing is not published; enterprise quotes mean you cannot evaluate cost without a sales conversation, and contracts tend toward annual commitments
- Free plan exists but is restricted in volume and features — not sufficient to pilot the tool against a real AP workload before purchase
- Overkill and overpriced for small firms or those processing standard, consistent invoice formats already handled well by QuickBooks or Xero native tools
- Switching cost is high once you have trained custom models on your document library; migrating to a competing tool means rebuilding from scratch
Ledger Brief Take
Stands out from generic OCR tools by letting you train custom models on your specific document formats — a genuine differentiator for companies dealing with non-standard invoices or industry-specific paperwork. The API-first architecture suggests this is built for integration into existing enterprise workflows rather than standalone use by individual practitioners.
Frequently Asked Questions
Common questions accountants ask about Nanonets.
What does Nanonets cost, and is there a free trial?
Nanonets offers a limited free plan, but volume and feature caps make it inadequate for evaluating real AP workloads. Paid plans require a sales conversation; pricing is not listed publicly. Expect annual contract terms for enterprise tiers. Budget discovery requires direct contact with the vendor.
How does Nanonets integrate with QuickBooks or Xero?
Nanonets connects to QuickBooks Online and Xero via API and pre-built connectors, pushing extracted and approved invoice data directly to those ledgers. The sync is configurable by field mapping. Small teams without a developer on staff may need outside help for initial setup, particularly if field names differ between Nanonets and their accounting system.
How does Nanonets compare to Rossum or ABBYY for invoice extraction?
All three use AI extraction, but Nanonets emphasizes user-trainable custom models more aggressively than Rossum, which leans on pre-trained finance models. ABBYY FlexiCapture is stronger on on-premises deployment and document classification at scale. Nanonets suits enterprises with unusual invoice formats who need model control; Rossum suits teams wanting faster out-of-the-box accuracy on standard invoices.
Is Nanonets secure enough for handling financial documents?
Nanonets is SOC 2 Type II certified and supports data encryption in transit and at rest. For enterprises with strict data residency requirements, confirm regional hosting options directly with the vendor before committing, as cloud storage jurisdiction matters for some regulated industries.
Which firm size gets the most value from Nanonets?
AP teams at mid-to-large enterprises processing 500 or more invoices per month with inconsistent supplier formats. Smaller firms or those with a narrow, consistent vendor base will not recoup the setup cost or justify the pricing compared to native AP features inside QuickBooks, Xero, or a cheaper tool like Dext.
How long does it take to train a custom model on your own invoice formats?
Nanonets recommends at least 50 to 100 labeled document samples per model for reliable extraction accuracy. Initial training typically takes a few days, but accuracy continues improving with additional samples. Teams with fewer documents or highly variable formats should expect more manual review exceptions during the first several weeks of live processing.