Nanonets

Nanonets ships ready-made agents for invoice processing, contract review, freight audit, KYC review, and other recurring processes. You start from a working template instead of a blank canvas, then adjust the fields and rules to match how your team actually handles each document.

Every step in a workflow is a block, and each block has a price based on complexity. Simple operations run at $0.02 per run, standard AI classification and validation at $0.10, and complex extraction or generative AI at $0.30. That structure lets a team with a simple routing job pay far less than one running heavy extraction across thousands of pages.

When a person corrects a wrong field, the correction feeds back into the model so the share of documents the agent closes on its own keeps rising. This matters for teams processing the same document type repeatedly, because accuracy compounds instead of staying flat.

Trail converts policies, procedures, and internal knowledge into a governed graph the agents can reference. Each rule is tied to the document it came from, so an agent deciding whether to accept a late return can cite the policy, the order record, and the prior promise, not just a hunch.

Nanonets built OCR-3 as its own extraction model, and the company says it leads on complex, constraint-heavy documents ahead of general models. The practical point is that a cleaner first pass on hard documents keeps bad data from breaking every step that follows.

The platform covers SAML SSO and SCIM provisioning, role-based access, audit logs that stream to a SIEM, and encryption at rest and in transit. Larger buyers can also pin data to US, EU, or APAC regions, or run the whole thing in a private cloud or on-prem.

Nanonets connects to email, cloud storage, databases, and business systems, with ERP connectors like Salesforce, SAP, and Oracle on higher plans. Custom Python blocks let a team insert its own logic into a workflow when the built-in blocks don't cover a step.