Overview
Nanonets is a San Francisco-based company that began in OCR-driven data extraction and now sells AI agents for finance and operations processes such as accounts payable, reconciliation, order management, cash application, supplier onboarding and claims[17][18]. Extraction is performed by its proprietary OCR-3 model, and a component called Trail stores business rules traced to their source documents for the agents to follow[18].
Agents connect to ERP and finance systems including SAP, NetSuite and QuickBooks, to email and storage, and to AI platforms through MCP and REST or GraphQL APIs; confidence scores decide when a human is asked to approve, with routing to Slack, Teams or email[18]. Nanonets states SOC 2 Type II and ISO 27001 certification and names customers including Schneider Electric, Volkswagen, Procter & Gamble and Ryanair[18].
Pricing is per block run rather than per page: each workflow step costs US$0.02, US$0.10 or US$0.30 depending on complexity, and Nanonets estimates a typical invoice workflow at under US$2 per invoice[17]. The model makes cost depend on workflow design, and the platform is aimed at process automation rather than simple scanning.
Nanonets features
- Proprietary OCR-3 extraction model
- Workflow agents for AP and reconciliation
- ERP connectors including SAP and NetSuite
- Human approval routing to Slack, Teams or email
- MCP and REST/GraphQL APIs
- On-premises option on Enterprise
Nanonets pricing
Starter: US$50 free credit, then US$100/month for 100 credits, up to 3 users. Growth: volume pricing via sales, up to 40% discount. Enterprise: custom, with SSO/SCIM, HIPAA, private cloud or on-premises. Block runs cost US$0.02, US$0.10 or US$0.30.
Nanonets strengths and limitations
Strengths
- End-to-end workflow, not just OCR
- Free starting credit
- Named enterprise customers
- Deployment options including on-premises
Limitations
- Per-block pricing is hard to forecast
- Overkill for simple scanning
- Growth pricing not published
- OCR model is proprietary and unbenchmarked independently
Appears in
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