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Gurgaon Startup Veratech Is Disrupting Data Intelligence Market With ML Solutions

Gurgaon Startup Veratech Is Disrupting Data Intelligence Market With ML Solutions

Data Intelligence solutions startup Veratech claims to have found the answer to the long and arduous process of conducting due diligence. Using the power of ML and data gathering algorithms, Veratech aims to provide their clients with 100% accurate data with no room for error.

The Seed Problem

This Gurgaon-based startup was founded by Mohit Yadav and Mayank Sharma, a lawyer, and an engineer respectively, in December 2017. It was during his job that Yadav realized that due diligence is a “very painful process”. He said, “There’s a lot of drudgeries involved. And it’s something that has to be done in every corporate transaction that takes place. I saw that from my end that the entire process could be automated using technology. I had dabbled in technology before that so I reached out to a good friend, he is now my co-founder Mayank Sharma.”

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The Birth of Veratech

Mayank and Mohit began working on the idea in gradually and after working for the better part of 2016 and 2017, they found that the market is bigger, believing they could make the startup work on on a full-time basis. This led to starting their company in the latter half of 2017.

Yadav revealed that there was no external funding to the company, and they bootstrapped it. This includes the technology as well, which is developed completely in-house. In fact, the co-founder stated that he gathered the training data for various models and algorithms used in the product himself.

The project was also helped off the ground by a few of Yadav’s acquaintances from his alma mater, Jindal Global University. By tapping into the network that the alumni of this institution have access to, Mohit was able to find individuals who could contribute to the project. This includes one of his professors who had a decade of experience in corporate law, thus enabling the use of his feedback into the earliest iteration of the product.

Inside Veratech

Veratech provides a service for multiple data-based tasks and also provides customisation on their packages. Currently, they offer services relating to counterparty diligence, risk advisory, transactional advisory, due diligence, vendor or client onboarding. This comes in the form of two products known as ‘Semantic’ and ‘Explore’.

Veratech’s Semantic product is built on a proprietary rule engine. This utilises their in-house built training models and is calibrated for absolute accuracy. The accuracy is achieved by using several techniques such as utilising several sources to harvest data points, meta, and contextual analysis, in addition to relation rules and an indigenous rule engine.

This makes extensive use of Machine Learning, as the algorithms are used to find patterns in the data delivered. ML is “one cog in the entire cycle”, with the system as a whole geared towards getting accurate data. Mohit says, “The problem with AI or ML is that they are inherently predictive, so we need to set a certain standard of accuracy. Now the person whom I have to give the output to needs 100% accuracy. So wherever Machine Learning could make sense for us, in terms of accuracy, it is being used. Elsewhere, a different mechanism is being implemented.”

Explore, on the other hand, is a product that is still in development stage. It is being constructed as a research tool to help discover market trends and find relevant businesses.It also offers features such as data visualization and comprehensive search functionality.

Why Veratech is different

As they worked on their product, they realized that there was no similar solution in the legal sector. Mohit says, “There’s no solution even right now. The way we handle this information, we are not simply getting a chunk of data and going to let you decide what to do with our data. Our core competency is finding fault lines in that data.”

The second advantage offered by Veratech’s solutions is how they gather the data. This is as close a replication to the human process involved in collecting it. “Even though we do it using technology, the end result is the closest thing that can be achieved by technology if a human were to gather the same information”, says Mohit.

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He also pointed out the distinction between other sectors, where data is essentially what is required. However, in the case of some sectors such as BFSI, the legal sector, and the VC sector, there is a need for accurate, binary information. Such information is either right or not right, with no scope for inaccuracy. Mohit says, “That is the biggest advantage we have over other players in the market. We don’t say 99.8% or 96.5% accurate. Because the idea that our clients would use this service is contingent upon the fact that it needs to be absolutely accurate. There is no 0.00001% scope of inaccuracy.”

Who does Veratech target?

Veratech started off with law firms as customers, owing to Mohit’s existing connections in the legal sector. Apart from Tier 1 law firms, the larger market for Veratech varies. There is a focus on the banking and financial services sector, which demonstrates a requirement during credit disbursement. There are also other professionals such as company secretaries, and risk professionals. Veratech’s products can also work on any scale, whether it is a small Chartered Accountant or a Big 4 consultancy.

Mohit also demonstrated how the product can be used for other applications as well. This also includes media as a field, as Mohit said:

“The way we see it is Indian promoters have a large web of companies that you need to go through to see what’s going on. It’s humanly not possible for a single reporter to track down a conglomerate, it might take a couple of months. We can help you do away with that, so you can really focus on the stuff that matters.”

With a nimble team of 6, Veratech demonstrates the power of cloud-based analytics services. 4 of the 6 are satellite workers, who can access the product and provide the service from anywhere. This effectively turns the startup formula on its head and is powered by ML models and a need to find a solution for a genuine problem.

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