Founded in 2017 by Sarthak Jain and Prathamesh Juvatkar, Nanonets is an innovative AI data extraction software designed to help businesses streamline document workflows and eliminate tedious manual tasks. This San Francisco and Bengaluru-based startup has been making significant strides in the AI automation space, recently securing $29 million in a Series B funding round led by Accel and existing investors Elevation Capital and Y Combinator.
Nanonets’ Impact on Workflow Automation
Despite the increasing competition in the workflow automation sector, Nanonets has carved out a niche by automating some of the most mundane and time-consuming tasks for professionals in finance, legal, and procurement sectors. The startup’s ability to simplify complex processes has earned it a strong foothold, particularly in the US market, followed by Europe.
Nanonets’ revenue has been doubling year-on-year, with a significant portion derived from automating finance processes such as accounts payable and reconciliation. While specific revenue figures remain undisclosed, the company’s impact is clear.
The Genesis of Nanonets
Jain and Juvatkar’s journey into the startup world began with Cubeit.io, a content aggregation startup acquired by Myntra in 2012. Their experience at Myntra highlighted the challenges of implementing artificial intelligence, even with a skilled team, prompting them to create Nanonets.
“When we started Nanonets in 2016, the application layer of AI (neural networks) models was just getting started. Early applications like content moderation and image tagging were being discovered, and deploying them at a company was extremely challenging—training, deploying, testing, etc. We knew AI would become a basic requirement for each company adopting technology, hence we started Nanonets,” Jain explains.
Automating Tedious Tasks with AI
Many companies still rely on employees to manually review and input complex documents into systems, a process that is not only time-consuming but also prone to errors. Drawing from their extensive experience in AI and machine learning, Jain and Juvatkar decided to automate these tasks. Nanonets allows businesses to leverage machine learning tools for automating processes such as invoice processing, accounts reconciliation, and expense management.
Efficient Data Extraction
Data is the backbone of many businesses, yet it often remains inaccessible when trapped in unstructured formats like emails, PDFs, and invoices. Nanonets can accurately extract data from various sources, including PDFs, documents, images, emails, and scanned documents, with over 95% precision. This significantly reduces the time required for manual invoice processing from 15 minutes to under a minute.
The startup processes millions of documents monthly, achieving a Straight Through Processing (STP) rate exceeding 90%, which means most transactions are completed without any human intervention. Additionally, their use of Natural Language Processing (NLP) enhances the technology’s capability to understand contextual meanings within documents, going beyond simple word recognition.
Accuracy and Security
Improving the accuracy of AI models is a primary challenge for Nanonets. Unlike generative models, Nanonets uses discriminative models, which do not generate new data but find information based on provided data. “Instead of using generative models, we use discriminative models. These models, though large like generative AI models, don’t make things up. This distinction is crucial, especially in scenarios like a CFO closing monthly books where accuracy is important,” Jain notes.
Nanonets is SOC 2 and GDPR compliant, ensuring that data is used only for intended purposes. The startup has developed its own models to ensure data confidentiality, addressing security concerns by preventing third-party data sharing.
Future Plans and Market Growth
With its latest funding round, Nanonets has raised $40 million to date. The company plans to invest this capital in research and development. Its 110-member team has seen a fourfold increase in its user base over the past 12 months. Nanonets offers a freemium model, allowing customers to evaluate the efficiency of its automation solutions firsthand, with charges based on paperwork volume.
Currently, Nanonets boasts over 10,000 customers, including major enterprises like Swiss pharmaceutical giant Roche and Indian paint major Asian Paints. However, one of the startup’s significant challenges is automating workflows involving unstructured data, given the variety of document formats and types.
“For example, even for a single document type like invoices, there are thousands of possible formats, and your models need to be smart enough to work across all of them,” Jain says.
Jain believes few companies offer a solution that is AI-based without manual intervention, highly accurate, and fully integrated into workflows. “This is our biggest strength on competitive deals today, and this is also what we consider our biggest challenge – to keep improving accuracy and the quality of our workflows,” he adds.
Market Competitors and Growth Projections
The Robotic Process Automation (RPA) market is expected to reach $34.18 billion by 2029, growing at a CAGR of 9.52% from 2024 to 2029, according to Mordor Intelligence. Nanonets faces competition from players like UiPath, Automation Anywhere, Docsumo, HyperVerge, and Amazon Textract, which focus more on workflow automation rather than data extraction.
“We grew 100% last year and are on track to grow again by another 100%. We largely sell into a global market. The majority of our customers are in the US, but we have customers all over Europe, Singapore, and Australia,” Jain concludes.
