For financial services, quick and efficient decision making is often the difference between a successful customer acquisition vs a failed one. To make fast & accurate decisions, enterprises need to capture & analyze unstructured data from documents such as tax returns, financial statements & invoices. In January 2019, when Bikram & I started speaking with lenders, insurers & other fintech companies, one thing was clear - access to accurate data & analytics was only possible with a large operations team manually reading customer documents.
We realized that complexity of data capture was an intense pain & decided to build an API platform for enterprises to easily automate manual error-prone processes. We are now at the cusp of a technological shift where machines are more powerful at reading & analyzing documents than humans and the enterprise stack is incomplete without a powerful Document AI engine.
As another milestone in this journey, we are glad to announce that Docsumo raised $3.5million in Seed capital. The round was led by Common Ocean with participation from Fifth Wall, Arbor Realty Trust & existing investor Better Capital. The amount will be used to further improve & expand our API products and expand our team.
Document understanding is difficult - unintelligent & template-based OCR solutions just don’t cut it. My co-founder Bikram used to be a data entry agent in Nepal to earn side income and knew how tedious & boring data entry is. However, the problem is a lot more complicated to automate since we as humans can make sense out of unstructured images whereas machines need structured data for decisions.
Before writing the first lines of code, we spoke to dozens of CTOs, heads of operations & underwriters to help us understand their day-to-day challenges. Additionally, it was important to understand how simplifying document processing would help businesses. We knew the challenges and we started small with just one use-case - invoices and launched the product in mid 2019.
The response was very positive.
It led to us converting our first enterprise customer PayU in October 2019. Since then, we have grown 6x in just the last year and expanded our offerings to multiple use cases in the US commercial lending & insurance space. Here is a short demo of the product:-
We came across a few recurring themes while working with initial customers:-
To this end, we have build Docsumo such that:
The advantage of this approach is that Docsumo can now process millions of documents per year for large enterprises such while being flexible enough for developers to easily automate processes.
It’s not very common to hear from customers that they like your product so much that they’re willing to invest in you. We witnessed two of our customers invest in this round, Arbor and National Debt Relief. It is a testament to the product that we are building in the cognitive automation space. We witnessed additional participation from Fifth Wall & our previous investor Better Capital.
In the spirit of sharing our learnings, here is the pitch deck we used for this round:-
The trust shown in us by our clients and investors is overwhelming, and gives us the confidence to pursue our mission of enabling automated enterprises. We take this opportunity to celebrate this milestone together and are grateful for the support. Amongst countless milestones we want to achieve, the least distant ones are expanding our client base, building more use-case APIs, and hiring a bunch of rockstars to help us in our mission.
In today’s dynamic business world, filing and archiving official documents in the digital form makes it handy, and works wonders in the future or in unforeseen circumstances.
The traditional supply chain management approach relies heavily on manual work and is time-consuming, error-prone, and expensive. As documentation is an important part of the supply chain that consumes considerable efforts of enterprises in the supply chain workflow, it makes sense to automate the process with the help of intelligent document AI software.
Optical Character Recognition (OCR) is the technology to convert an image of text into machine-readable text. It is the underlying technology for various data extraction solutions including Intelligent Document Processing. However, OCR is not smart enough to figure out the context in a document - it works simply by distinguishing text pixels from the background and finding a pattern. This limitation could cause inaccuracy in captured data that could directly impact the output of your data extraction model.
Accounts payable is a key financial function for any business. Corporations can have thousands of suppliers; even for relatively smaller businesses, the number of suppliers could be in hundreds. All the invoices they receive from these suppliers come in multiple formats, layouts, and templates - some semi-structured, some unstructured. Therefore, firms expend time and resources to capture invoice information through manual data entry and verification of accounts payable. Manual data entry is not feasible in the long run, definitely not on a large scale. Before we talk about how intelligent invoicing solves the problems associated with manual invoicing, let’s discuss the challenges in much detail.