Eigen raises $37M to help banks and others parse massive documents

Paperwork
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A big trend in enterprise software is the arrival of startups that build new technology to provide non-tech companies with artificial intelligence. Today, a startup, Eigen Technologies, has developed a platform that uses machine learning to process massive documents. Now, the startup has closed a new funding round.   

Eigen Technologies is a London-based startup which uses machine learning to help banks and other businesses to sort out massive documents like contracts. Today, the company has banked $37 million in Series B round. This makes the total valuation of the company to $180 million.   

Dawn Capital and Lakestar are leading the current round. Other backers in the round include Goldman Sachs Growth Equity and Temasek. In total, the startup has raised around $55 million. However, it is way less than the amount raised by other startups, like Coveo. 

Eigen's investor Goldman Sachs
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Eigen is working in the financial sector, with offices in the middle of The City. However, the company plans to use the funding to expand its platform scope to cover other areas, like healthcare and insurance. These are two significant areas that deal with considerable documentation that is mostly inconsistent.  

The startup’s focus till now has been on banks and other financial businesses. Moreover, Eigen Technologies now has a customer base, which includes 25% of the world’s largest banks and others that operate closely with them, like Deloitte and Allen & Overy. Moreover, Eigen has seen revenues grow sixfold 

The co-founder and CEO of Eigen, Lewis Liu, says that the company focuses on “small data.” The company has formed a new way to teach an AI to read documents; for example, it can read a loan contract by looking at some examples of these. Moreover, this is a natural process, even for a non-technical person. You find a document to analyze, then find some examples using the basic search method and create the template. Eigen’s AI uses this template for thousands of documents of the same kind.  

There are two reasons as to why Eigen’s work is groundbreaking. First, machine learning and AI need hundreds and thousands of examples to teach itself to mimic these examples. However, Eigen only needs a couple of examples. Secondly, an industry like finance contains many sensitive documents. Thus, there is an issue with the AI companies that want to anonymize the data. However, Eigen’s system only works with what it gets from the company, and the data stays with the company.  

Dr. Lewis Z. Liu and Jonathan Feuer founded the company in 2014. However, its origins go several years back when Liu studied art and History at Harvard University. It was there that he got the idea of developing a pattern in analog material that could be recreated using maths. Liu went on to do a Ph.D. at Oxford, where he worked on X-ray lasers that could decrease the cost of cancer treatments, microchips, and other applications.  

Liu said, “The whole idea of my Ph.D. was how to make this scalable and cheaper. We made an X-ray laser apparatus and found that the same equations could be used around sequential patterns. This concept, coupled with my corporate relationships, was what led to Eigen.” 

Several AI companies in the market are building tools for non-technical businesses. One of the areas that is close to what Eigen is doing is robotic process automation. However, the main focus of Eigen is to use unstructured data and parse it quickly with only a few samples.   

Liu says that companies like IBM are their general competitors. Meanwhile, startups, like Luminance, are developing a similar approach to parse unstructured data in a specific area.   

Stephen Nundy, a partner and CTO of Lakestar, said in a statement, “It’s quite admirable what Liu and his team are doing. They don’t just pick the names and addresses. They can sort out deep data with semantic understanding. Eigen is delivering, and it is a great signal for the future.”  

 

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