INSUBCONTINENT EXCLUSIVE:
One of the bigger trends in enterprise software has been the emergence of startups building tools to make the benefits of artificial
intelligence technology more accessible to non-tech companies
Today, one that has built a platform to apply the power of machine learning and natural language processing to massive documents of
unstructured data has closed a round of funding as it finds strong demand for its approach.Eigen Technologies, a London-based startup whose
machine learning engine helps banks and other businesses that need to extract information and insights from large and complex documents like
million.The round was led by Lakestar and Dawn Capital, with Temasek and Goldman Sachs Growth Equity (which co-led its Series A) also
verticals such as insurance and healthcare, two other big areas that deal in large, wordy documentation that is often inconsistent in how
disaster if it is not.The focus up to now on banks and other financial businesses has had a lot of traction
work closely with them, like Allen - Overy and Deloitte
The whole process is relatively easy to do for a non-technical person: you figure out what you want to look for and analyse, find the
examples using basic search in two or three documents and create the template, which can then be used across hundreds or thousands of the
it can make decisions that you hope will mimic those of a human
at Harvard.A natural computing whiz who found himself building his own games when his parents refused to buy him a games console, he figured
out that the many pages of printouts he was reading and re-entering into a different computing system could be sped up with a computer
program linking up the two
Liu went on to Harvard to study not computer science, but physics and art
people could detect pixelated actual work with that of his program
Distill this, and Liu was still thinking about patterns in analog material that could be re-created using math.Then came years at McKinsey
in London (how he arrived on these shores) during the financial crisis where the results of people either intentionally or mistakenly
overlooking crucial text-based data produced stark and catastrophic results
Liu worked on X-ray lasers that could be used to decrease the complexity and cost of making microchips, cancer treatments and other
specifically around sequential patterns
There are more data scientists and engineers building the engine around the basic idea, and customising it to work with more sectors beyond
what Eigen is doing is robotic process automation, or RPA
The focus of Eigen is more on unstructured data, and the ability to parse it quickly and securely using just a few samples.Liu points to
companies like IBM (with Watson) as general competitors, while startups like Luminance is another taking a similar approach to Eigen by
addressing the issue of parsing unstructured data in a specific sector (in its case, currently, the legal profession).Stephen Nundy, a
partner and the CTO of Lakestar, said that he first came into contact with Eigen when he was at Goldman Sachs, where he was a managing
director overseeing technology, and the bank engaged it for work
financial services use cases, and it stands up against the competition
