Nanonets

What it claims to do

• Form Extraction
• Invoice Processing
• OCR
• API Integration

Claimed benefit. Automated data entry, reduced manual processing, improved accuracy

Reported use case. Finance teams automating invoice processing and expense management

Source: the vendor and the capability mapping work behind this map. The Revenue AI Report has not independently verified these figures. Verified findings live in Research, with method, sample, and field date attached. Unfamiliar terms are defined in the AI and Revenue Dictionary.

Published case studies

• Asian Paints

  The company reduced vendor invoice processing time to approximately 30 seconds, saving 192 hours per month.

  Read the Nanonets case study ↗

• Roche

  The company reduced processing time from 10 minutes per order to 30 seconds, saving 40 hours per month.

  Read the Nanonets case study ↗

• Schneider Electric

  The company automated PR-to-quote matching, processing 500,000 pages in 4 months with a 90%+ match rate.

  Read the Nanonets case study ↗

We found a minimum of 3 published case studies here. There may be more we have not found. Case studies are published by the vendor. Customer names, figures, and outcomes are the vendor's claims. The Revenue AI Report has not audited them. Independently checked findings live in Research, with method, sample, and field date attached.

How to read these claims

The sample is chosen by the seller

A case study is the best result the vendor is allowed to publish. It is not a sample of all customers. The accounts that churned do not get a page.

There is no control group

Almost no vendor study compares the team using the tool against a matched team that did not. Without that comparison, the lift reported cannot be separated from headcount changes, pricing changes, seasonality, or a good quarter.

The baseline is usually missing

A percentage gain means nothing without the starting number. A 300% increase in meetings from two meetings a week is eight. Ask for the absolute figures and the time window.

Activity is not revenue

Most published gains are activity metrics: emails sent, replies, hours saved, meetings booked. Closed revenue, win rate, and retention are the metrics that survive a board meeting. Ask which one the study actually measured.

Named logos are not always customers

Logos and case studies have been published for accounts that had already churned or had only run a pilot. Ask the reference directly, by name, and ask how long they have been live.

Field notes: what users say in public

Independent feedback from review sites and practitioner forums, not vendor marketing. This is what a buyer would hear from a peer who has already run the tool.

Good starting point for low-volume invoice extraction; get a volume-based cost model in writing before scaling up.

Ask these on the call

1. 01What is the fully loaded cost per document at our actual monthly volume, not the marketed rate?
2. 02What uptime and reliability guarantees exist for high-volume processing?
3. 03How does accuracy hold up on our specific document types versus simple invoices?

How to read review evidence

Review sites are a biased sample

Most reviews are collected by the vendor, often with an incentive attached. Scores cluster high across the whole category, so a 4.6 average is closer to par than to proof. Read the one and two star reviews first, and read the most recent ones, because product and pricing change faster than the average score does.

Forums show the failure modes, not the base rate

Reddit and Hacker News threads surface what breaks, which is exactly what a business case needs. They do not tell you how common the problem is. Treat a repeated complaint as a question for the vendor, not as a verdict.

Complaints about price are usually complaints about structure

Seat minimums, credit packs that expire, annual lock-in, and per-action pricing produce most of the pricing anger in public reviews. Get the structure in writing, not the headline number.

Ratings are a snapshot

Every score here is dated. Check the live page before you cite it in a board deck.

The friction above is the tool level version of a pattern the Report has already measured. See Agent reliability for the method, sample, and field date behind it.

Claims versus the record

No citable discrepancy between this vendor's public claims and independent reporting was found at the time of the last check. That is not verification. It means nothing has been published either way, so the claims above still rest on the vendor's own account.

What has to be true before you buy

The Report does not review tools in isolation. Every tool on this map is connected to three things we publish elsewhere on the site: a decision framework that tells you how to evaluate it, a research theme that shows what we have measured in the market around it, and an essay that applies both to a real case. Those links appear at the bottom of this section so you can verify our reasoning instead of taking this page at face value.

Ambition level: Optimize

Same process, less time. Judge it on hours returned and data hygiene, not revenue.

Audit the data model before the forecast model. A clean sandbox is not live CRM data.

Friction at this stage

• Forecast inaccuracy
• Risk found late
• Deal reviews rebuilt by hand every week

Common questions about Nanonets

What does Nanonets do?

AI-powered document processing platform that automates data extraction from invoices, receipts, forms, and more. It sits in the Document Intelligence & Analysis category and maps to the Deal management stage of the revenue journey.

Where does Nanonets fit in a revenue team?

Nanonets maps to the Deal management stage at the Optimize level of ambition, and is usually owned by the RevOps and GTM engineering seat. Reported use: Finance teams automating invoice processing and expense management

Does Nanonets publish customer case studies?

Yes. 3 named customer stories are published, including Asian Paints, Roche, Schneider Electric. These are vendor claims, not figures verified by The Revenue AI Report. A case study is the best result a vendor is allowed to publish, not a sample of all customers.

What do buyers say about Nanonets?

Public score 4.7 on G2 from 94 reviews, observed May 2025. Praised for: Advanced OCR technology with strong customization for specific document types Accuracy of roughly 92-93% reduced manual effort by an estimated 9.2 FTEs for one enterprise user Good starting point for low-volume invoice extraction; get a volume-based cost model in writing before scaling up.

What should we ask Nanonets before buying?

What is the fully loaded cost per document at our actual monthly volume, not the marketed rate? What uptime and reliability guarantees exist for high-volume processing? How does accuracy hold up on our specific document types versus simple invoices?

Answers are assembled from the vendor material, published case studies, and independent evidence shown on this page. Terms are defined in the AI and Revenue Dictionary.