Mate Labs: Rahul Vishwakarma on Revolutionizing Enterprise Forecasting with the World's Fastest AI
For global FMCG giants, predicting what consumers will buy next week is a billion-dollar problem. Traditionally, this was done with spreadsheets and gut instinct, often leading to massive inefficiencies. Rahul Vishwakarma, a Chemical Engineer turned AI entrepreneur, is changing that with Mate Labs. His startup has built an automated machine learning platform that is reportedly 100x faster than existing solutions, helping enterprises forecast demand with unprecedented accuracy. This is the story of how two college friends from India built a technology that even Google recognized as one of the best out of Asia.
Rahul's journey into Artificial Intelligence began in 2010, long before 'AI' was a buzzword. Along with his co-founder Kailash, he started exploring machine learning to solve complex problems during his engineering days. After a stint designing automation for a $1.3 billion oil and gas project in Seoul, Rahul returned to his passion: making machine learning accessible and impactful for businesses. Today, Mate Labs is solving the "Demand Forecasting" puzzle for some of the world's largest companies, proving that complex supply chain problems can be solved in minutes, not months.
The 100x Speed Advantage
Mate Labs claims a significant technological edge. "We spent three years building our automotive technology, which I can proudly say is currently the world's fastest by a margin of 100x," Rahul asserts. "We have benchmarked our technology against Google, AWS, H2O, and DataRobot. What takes a company nine months to do, takes us ten minutes."
The Problem: The Billion-Dollar Guessing Game
Imagine a large FMCG company selling thousands of products across 50 locations. That creates 50,000 to 100,000 unique combinations of product and location that need a sales forecast every single week. This data dictates everything: procurement, manufacturing, logistics, and staffing.
"Companies have been struggling with this for centuries," Rahul explains. "The industry standard accuracy for demand forecasting is often below 50%. It's incredibly difficult to predict the future. But if you get it wrong, you either have excess inventory rotting in warehouses or stockouts that lose you customers."
The Solution: Automated AI for Supply Chain
Mate Labs introduced a proprietary Automated Machine Learning (AutoML) platform specifically tailored for supply chains. By automating the entire data science lifecycle—from data cleaning to model selection and training—they enable demand planners to generate highly accurate forecasts without writing a single line of code.
The impact was dramatic. "We help companies move from sub-40% accuracy to 80-90% accuracy," says Rahul. During the COVID-19 pandemic, this capability became a lifeline. Mate Labs helped a major brand predict the unprecedented surge in demand for hand sanitizers—a product that went from obscurity to essential overnight—allowing them to ramp up production before the lockdown hit.
The Mate Labs Evolution
- The Genesis: Started in 2016 with a mission to enable non-developers to use machine learning.
- The Pivot: Realized that a general "AI tool" was a solution looking for a problem. Pivoted to solving a specific, high-value pain point: Demand Forecasting.
- The First Win: Convinced a pharmaceutical head of supply chain to let them solve a specific challenge, proving the tech could outperform manual methods.
- The Scale-Up: Automated the entire pipeline, reducing the time-to-insight from months to minutes.
The Future of AI: Beyond the Hype
Rahul is a realist when it comes to AI. He prefers the term "Machine Learning" because it accurately describes training a system for a specific task in a controlled environment. While he acknowledges the rapid progress—predicting that AI will surpass human intelligence in specific tasks—he believes "General Artificial Intelligence" (AGI) that mimics unconstrained human cognition is still years away.
However, he warns about the ethical implications of current AI, particularly in social media algorithms that can manipulate behavior and influence elections. His advice for the AI-enabled future? "Embrace it. See it as a companion. Let it handle the automation so you can focus on the creative and human aspects of life."
Mate Labs Impact
- 100x Faster: Forecasting models built in minutes vs. months.
- 90% Accuracy: Improved forecast accuracy from industry average of ~40%.
- Crisis Response: Successfully predicted pandemic-driven demand spikes for critical hygiene products.
- Global Recognition: Chosen by Google as one of the best AI startups in Asia.
Advice for Tech Entrepreneurs
Rahul's journey from a "starry-eyed kid" to a seasoned CEO offers valuable lessons. He advises founders to avoid scaling prematurely. "Don't think about how you will scale before you have solved the first problem," he cautions. "Solve for one customer deeply. If you can't quantify the problem statement, it's too vague. Start with the customer, not the technology."
About the Guest
Rahul Vishwakarma is the Co-Founder and CEO of Mate Labs. A Chemical Engineering graduate from NIT, Rahul has a deep background in process control and automation, having worked on massive oil and gas projects before turning to entrepreneurship. He started his journey in machine learning in 2010 and has since been a vocal advocate for democratizing AI. Under his leadership, Mate Labs has emerged as a pioneer in AI-driven supply chain planning, helping enterprises navigate volatility with data-driven precision.
Mate Labs is an AI/ML startup that provides next-generation demand forecasting and supply chain planning solutions for large enterprises. Their flagship platform automates complex data science tasks, enabling companies to predict demand with high accuracy and speed, reducing inventory costs and lost sales.