Nanonets

What is Nanonets?

Nanonets offers AI-powered intelligent document processing and no-code workflow automation to extract data from unstructured inputs and automate document-heavy processes. It’s designed to help teams turn documents and other semi-structured information into structured data they can act on.

The platform uses AI to extract information without relying on predefined templates and provides decision engines to flag, route, and validate items as part of end-to-end workflows. The stated outcome is reduced manual effort in document-heavy processes such as accounts payable, order processing, and insurance underwriting.

Key Features

• AI-powered data extraction for unstructured inputs: Extracts meaningful information from documents and other sources such as emails, tickets, or databases.
• Template-light extractors: Data extraction is described as not relying on predefined templates.
• No-code workflow automation: Automates complex manual workflows through a no-code platform.
• Learnable decision engines: Supports rules/decision logic to flag, review, validate files, and enhance extracted or missing data.
• Centralized structured output: Consolidates extracted data “in one place,” then exports to business systems or file formats (XLS, CSV, XML).

How to Use Nanonets

1. Start with an automation workflow by ingesting files or data from sources like email, cloud storage, support tickets, or databases.
2. Configure the AI extraction step so the system extracts the needed fields from the input documents.
3. Add decision steps to flag items for review, validate extracted data, or enrich missing fields using decision engines.
4. Export the structured results to your target system (for example, a CRM or database) or to common file formats such as XLS, CSV, or XML.

Use Cases

• Accounts payable (invoice processing): Ingest invoice documents, extract data from invoices/receipts/POs, route items for review, and reconcile transactions by syncing with an ERP.
• Order processing and supply chain: Extract order-related information and automate steps for order matching (including 2-way/3-way matching against purchase requests) and document handling to speed processing.
• Insurance underwriting: Automatically classify and organize application documents, consolidate data across multiple documents into a single view, and trigger customer-facing communications via automated emails.
• Document intake from multiple channels: Import documents from email, Dropbox, Drive, or Microsoft Dynamics and standardize extracted data into a single structured output for further processing.

FAQ

Does Nanonets require predefined templates for extraction?

The page states that the AI extractors “don’t rely on predefined templates.”

What kinds of inputs can be processed?

The platform is described as extracting from documents and also from sources including emails, tickets, and databases.

Where can extracted data be sent?

The page mentions exporting structured data into systems such as a CRM, WMS, or database, and also exporting as XLS, CSV, or XML.

Is workflow setup code-based?

The platform is described as having a “no-code platform” for automating complex business processes.

How does Nanonets handle validation or review steps?

It uses decision engines to flag, review, validate files, and enhance extracted or missing data.

Alternatives

• Low-code/no-code workflow automation platforms with document ingestion: These can orchestrate intake and routing, but may require additional tooling or custom configuration to achieve high-quality extraction from unstructured documents.
• General OCR and form extraction tools: Useful for turning scanned documents into text/fields, but they may offer less end-to-end workflow automation and decisioning compared with a combined extraction + workflow platform.
• Custom ML pipelines or in-house document processing systems: Offer maximum flexibility, but typically require more engineering effort to build, maintain, and evolve extraction and workflow logic.
• Enterprise RPA focused on back-office tasks: Can automate repetitive actions after data is available, but may not address the document-to-structured-data extraction workflow as directly as an intelligent document processing platform.