Reading the retail web at scale
DataWeave describes what it sells in plain terms: competitive intelligence as a service. In practice, that means the platform aggregates and analyzes billions of unstructured data points scattered across the web - prices, product descriptions, stock levels, search rankings, reviews - and packages them into signals a brand manager or a category buyer can use before a competitor gets ahead. On a typical day, the company processes data on roughly 120 million products.
The scale of the problem is easy to underestimate. Karthik likes to cite one number to make it concrete: in a span of just two to three days, Amazon and Walmart together made around 120 million price changes. No human team can watch that. The pitch behind DataWeave is that automated, AI-powered collection and normalization can - and that clean data, not clever dashboards, is the hard part.
"Brands today have very little control over their performance on online marketplaces. Data is either limited, incomplete, or not actionable." - Karthik Bettadapura
His current thinking has moved toward the AI shopping wave and its unglamorous bottleneck. In recent writing he argues that AI-driven commerce cannot scale until product data stops being a mess - reliable price, availability, and attribute intelligence has to come first, before any assistant can reliably recommend or buy on a shopper's behalf. It is a familiar theme from someone whose graduate research was in information retrieval: the value is in making messy data trustworthy.
A company built on a research question
Karthik holds an MTech from IIIT-Bangalore, where his research focused on information retrieval, advanced databases, and distributed systems - close to a spec sheet for the company he would later build. Before DataWeave, he was part of the team that built Web18, CNBC's online property. The through-line across both chapters is the same: taking large volumes of messy web data and turning it into something people can use.
In 2011 he co-founded DataWeave in Bengaluru alongside Vikranth Ramanolla, who leads the technology side as CTO. The early years were research-heavy and product-heavy, and firmly aimed at the Indian market. That focus eventually ran into a ceiling.
"We built great technology, but we were not able to scale beyond a certain point in India." - Karthik Bettadapura
After about five years, the founders concluded that the technology was strong but the domestic ticket sizes were too small to build the business they wanted. They looked outward, to the far larger US retail market. The move was not a soft landing. As an unknown startup from another country, DataWeave faced the classic cold-start problem - why would a US retailer trust a vendor with almost no local customers? The company started with two US customers and worked its way up, eventually landing a large big-box retailer as an early anchor client. Today Karthik runs the business from the Austin, Texas area, while DataWeave keeps its engineering hub in Bangalore and its sales and marketing split across both countries.
Learning over knowing
Ask Karthik about hiring and you get a compact philosophy rather than a checklist. He puts adaptability above credentials - in a field where the underlying platforms rewrite their own rules constantly, the ability to pick things up quickly beats a fixed body of knowledge.
"Learning is more important than knowing. We give a lot of importance to people who can learn and pick up things quickly." - Karthik Bettadapura
That instinct shows up in the product too. DataWeave's clients have included names like Adidas, Dorel, and Timex - brands that live or die by their position on the digital shelf, where a single unmonitored price drop or a rival climbing the search results can quietly erode a quarter. The company's remit has widened over the years from pricing optimization and assortment analytics into share-of-search, content audits, and brand protection, always circling the same core competency: collect the web reliably, normalize it, and hand back an answer.
"We provide Competitive Intelligence to retailers and consumer brands."
"Amazon and Walmart made 120 million price changes in just 2-3 days."
"You are an unknown entity. Why should they be trusting someone who does not have enough customers here?"
"AI shopping can't scale without clean, normalized product data."
A decade and a half in, the shape of Karthik's career is consistent. He picked a hard, unfashionable problem - the reliability of data underneath online commerce - and stayed with it while the labels around it changed from web analytics to big data to AI. The retail web keeps getting noisier. That, more than anything, is what keeps DataWeave in business.
- His graduate research at IIIT-Bangalore centered on information retrieval - essentially the academic root of what DataWeave now does at web scale.
- Before founding a company that reads the retail web, he helped build CNBC's Web18 online property.
- DataWeave keeps its engineering hub in Bangalore while running sales and marketing across both the US and India.
- His long-running LinkedIn handle, "brkarthik," has followed him for years as DataWeave's public face.
Who is Karthik Bettadapura?
He is the co-founder and CEO of DataWeave, an AI-powered SaaS platform providing competitive intelligence to retailers and consumer brands. He co-founded the company in 2011 with Vikranth Ramanolla.
What does DataWeave do?
DataWeave aggregates and analyzes billions of unstructured web data points to give retailers and brands competitive intelligence - pricing optimization, assortment analytics, digital-shelf and brand-protection insights - processing data on around 120 million products daily.
What is his background?
He holds an MTech from IIIT-Bangalore with research in information retrieval, advanced databases, and distributed systems, and previously helped build CNBC's online property Web18. He has over 15 years in technology leadership.
Where is he based?
He is based in the Austin, Texas metropolitan area, while DataWeave maintains its engineering hub in Bangalore, India.
Why did DataWeave expand to the US?
After about five years focused on India, he found the company had strong technology but couldn't scale beyond a point domestically, so it expanded into the larger US retail market.