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Meet Mate Labs, A Startup That Builds Easy-To-Use ML Platforms For Non-Developers With 100% Success Rate

Meet Mate Labs, A Startup That Builds Easy-To-Use ML Platforms For Non-Developers With 100% Success Rate

Srishti Deoras

While the adoption of artificial intelligence and machine learning is accelerating among industries such as retail, consumer goods, financial services and more, most companies are building solutions that can be used by experienced developers and coders. But there are very few ML-enabled platforms that can suit the needs of non-developers. Bengaluru-based Mate Labs, a horizontal startup, is enabling just that while working towards their vision of bringing machine learning to the masses.

Analytics India Magazine got in touch with Rahul Vishwakarma, CEO and co-founder of Mate Labs to know how the startup has fared since its inception in 2016 and how far have they been able to accomplish their vision.



The Journey So Far

Explaining what a horizontal startup means, Vishwakarma says that it is those startups that cater to the individual needs of clients and customers for the same purpose of use, such as providing a platform for AI in their case, but across different industries.

“Being technophiles, we were well aware of the fact that AI and ML are the future. We have seen small organisations/individuals lose power and control of their data to medium and large businesses. Using our flagship product – MateVerse, CTOs, data analytics firms, analysts and data scientists from across sectors have been able to increase their bottom line by reducing man-hour costs and empower the top line with Rapid-prototyping framework,” he said.


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How It All Started

As Vishwakarma shares, before starting Mate Labs, they helped companies with individual AI and ML-related projects. They soon realised that many large enterprises were building platforms for developers, and no one considered building something for the non-developers, to easily integrate into their business models.

“So, after fine-tuning several ideas, Kailash Ahirwar and I finally landed on the idea of setting up Mate Labs. Even while AI is becoming centre-stage, most companies are unsure of how to go about adapting to it, or they find the process complicated and time-consuming. This is especially true for non-technology-based companies or organisations without the resources to transform quickly. Therefore, we were confident that our venture would be able to cater to the larger industry needs for quick, cost-effective and easily implementable AI/ML application,” he shares.

They officially started Mate Labs in 2016 and their flagship product, MateVerse has been created with a sole purpose of simplifying the entire process of building and training ML models, simple enough to be used by someone who doesn’t have to write a single line of code.

“While I am a chemical engineer from Maulana Azad National Institute of Technology, Kailash completed his B. Tech in Energy Engineering. Our passion for AI and ML brought us together to start the company,” shares Vishwakarma.

Understanding Mateverse

Mateverse, their Machine Learning platform can be used by tech-enthusiasts to build and train ML models without writing a single line of code while saving a significant amount of time. Its versatility lies in the fact that Mateverse can be used both beginners and evolved users. For data analysts and scientists who spend several weeks re-processing data, it serves as a one-stop-solution to many of their problems.

Their product, Mateverse, is used by CXOs and VPs, data analytics firms, analysts and data scientists from across sectors including Fortune 500 companies. “Our one-of-a-kind offering gives us the competitive advantage of being able to target customers who weren’t until now from the incumbents of this space. We built this platform through their proprietary technology which makes the automation possible, he said.

Some of the capabilities that Mateverse offers are:

  • Automated Missing Value Imputation
  • Automated Outlier Detection
  • Automated Feature Selection
  • Automated Machine Learning, which includes Classification, Regression, and Time-series Forecasting
  • Real-time Dashboard
  • Over 70 Visualisations
  • Programmatic Analytics
  • Support for Private and On-Premise Deployment

Mate Labs Enables DIY Machine Learning

When using MateVerse, users are guided through a step-by-step process enabling them to easily train and deploy a model, allowing them to experiment and build newer solutions. The success rate of their model has been 100%, and Vishwakarma proudly shares that they have never failed.

Challenges

Vishwakarma says that as for talent, there is a relatively huge shortage. “And, that is where platforms like Mate Labs, plays a role in alleviating much of the challenges due to the high-level of talent required in ML,” he said.

He further adds that lack of educational Institutes that train talent specific to the industry, and the negative hype around AI, is one of the major challenges. “When it comes to the companies that are falling behind in adopting AI, the lack of information/proper advice and lack of adequate resources are the major challenges,” he said.

Growth Story So Far

In the last three years, they have built a strong global and organic user base of 10,000+ including some of the top Fortune 500 companies as well as data analytics firms. They started Mate Labs as a bootstrapped company and raised $550,000 seed funding from lead investor Omphalos Ventures India LLP in September 2018. “Our sole objective to secure the funding is to build and grow the business,” he said.

Since then, a significant portion of the funding has gone into hiring the right talent in terms of engineers, marketers, and business developers to accelerate our business growth. We are also investing in R&D to ensure our customers get the best in the business in terms of technology innovation,” he added.

The Way Ahead

The Mate Labs team is currently working on an advanced enterprise product for the BFSI and Retail markets. Using this, they will be able to augment businesses, and help organisations train a model, as well as securely handle their data.

Vishwakarma hopes that within business functions across the segments, they expect large organisations to deploy AI at a deeper level. “This, in turn, will lead these large companies to train employees on AI skills and also hire those with specialised skill sets. This will also mean that 2019 might see the start of a trend of re-deployment of people based on AI, ML deployment and skill set mapping,” he says on a concluding note.

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