Sygnet vs Nanonets

Sygnet and Nanonets both turn documents into structured data, but they aim at different jobs. Nanonets is a broad document automation and workflow platform: it classifies documents, extracts fields, runs approval steps, and pushes results into ERPs and CRMs, which makes it a natural fit for teams who want the whole accounts payable or order-intake process to live in one tool. Sygnet is narrower on purpose: an intelligent document processing platform and API built for zero-shot extraction from any document type, with per-field confidence scores, bounding-box provenance, and EU hosting. This page is written by Sygnet, so read the competitor claims against the sources we link and check anything that matters to your decision directly with the vendor.

At a glance

A note on the pricing row: public pricing figures for Nanonets vary widely across review sites, and at least one reseller page describes a per-block-run model rather than per page. We are not going to pick one of those numbers and present it as fact. Ask Nanonets for a quote based on your document mix.

Where Nanonets is strong

The breadth is real. Nanonets describes itself as handling processes that start with unstructured data end to end, with pre-built connectors for major ERPs and CRMs including SAP and Salesforce, and custom integrations included on enterprise plans. If your bottleneck is not extraction accuracy but the plumbing between a shared mailbox and your finance system, that connector library saves months.

Workflow design is the other strength. Nanonets Flow offers a visual drag-and-drop interface for designing automated processes, where users specify how documents are classified, what data is extracted, where approvals are required, and how data is routed downstream. Non-engineers can own that layer, which matters in finance and operations teams without spare developer time.

Classification is mature. The classification system distinguishes between invoices, purchase orders, bank statements, identity documents, insurance claims and dozens of other categories without the user specifying the type in advance. The compliance posture is well documented too: an ISMS certified to ISO/IEC 27001:2022 with an annual SOC 2 Type II examination. For a large enterprise with a procurement checklist, that paperwork is already done.

Where Sygnet is strong

Sygnet does one thing: get correct, checkable data out of documents it has never seen before. There is no model to train and no template to maintain. You send a document and a schema, or no schema at all, and Sygnet infers the fields. That removes the step where someone has to decide which model handles which incoming file.

Every field comes back with a confidence score and a bounding box pointing at the exact place on the page the value came from. That combination is what makes selective review possible: route only low-confidence fields to a human, let the rest through, and give reviewers the coordinates instead of making them hunt. We wrote about how to set those thresholds in AI confidence thresholds for claims processing.

Validation goes beyond single-field rules. Sygnet runs cross-document checks, so a payslip can be tested against a bank statement, or a line item against a purchase order, before anything reaches your system of record.

Hosting is in the EU on Google Cloud europe-west1, and customer documents are never used to train models. Pricing is per document, which is easy to forecast. Sygnet is in early access, so the connector catalogue and the maturity of a seven-year-old platform are not there yet.

Which one for which team

• Pick Nanonets if you need the whole process in one place: email intake, classification, approvals, and a supported connector into SAP, Salesforce or your accounting tool, built by people who are not developers.
• Pick Sygnet if you already have the workflow and the systems, and what you need is an extraction API that returns confidence and provenance you can audit field by field.
• Pick Sygnet if EU data residency is a hard requirement rather than a preference. Nanonets' published security policy states US storage by default, with private and regional deployment options advertised separately (security policy, nanonets.com), so confirm which applies to your contract.
• Pick Nanonets if you need a signed BAA for HIPAA today; the site states one is available for enterprise plans.
• Pick Sygnet if your document mix is long-tail: due diligence packs, notarial deeds, lease agreements, foreign-language contracts. See contract analysis or M&A due diligence for how that plays out.

Teams processing a steady diet of five known document types at high volume will usually be happiest with whichever vendor gives them the cheapest reliable throughput. Teams facing unpredictable documents, or auditors who want to know where a number came from, should weight provenance more heavily than connector count.

FAQ

Can Sygnet replace Nanonets entirely?

Not in every case. If you rely on Nanonets Flow for approval routing and its native ERP connectors, Sygnet replaces the extraction layer but not the workflow layer. Teams that already own their orchestration (a queue, a backend, an iPaaS) can swap in Sygnet's API and keep everything else. Read our build vs buy guide before deciding which pieces to own.

Does Nanonets store data in the EU?

Nanonets' published security policy states that all customer data is stored in the USA and that customer data sits in multi-tenant datastores. Its homepage separately advertises data residency and private deployment, including VPC, single-tenant cloud and on-prem. Those statements may apply to different plans, so ask Nanonets directly what your contract covers. Sygnet runs in Google Cloud europe-west1 by default.

How should I compare accuracy between the two?

Run both on fifty of your own documents, not on a vendor sample. Score per field, not per document, and count a near-miss as wrong. Then divide total cost by the number of fields you did not have to fix by hand. That number, cost per correct answer, is the one that predicts your bill. We explain the method in accuracy vs cost per correct answer.