AI startup Atlan bags $105 million from investors at a valuation of $750 mn

MUMBAI: Artificial intelligence (AI) is perhaps the only new age tech space which has continued to evince investor interest even amid a global slowdown in tech funding. AI startup Atlan led by Indian founders Prukalpa Sankar and Varun Banka has bagged a fresh $105 million in funding led by Singapore’s sovereign wealth fund GIC along with co-investor Meritech Capital at a valuation of $750 million.Existing investors Peak XV Partners and Salesforce Ventures also participated in the round taking the five-year old company’s total fund count to over $200 million. Sankar and Banka started up with their data intelligence venture SocialCops in New Delhi whose tech tools powered projects including India’s national data platform DISHA. In fact, Atlan was incubated while working on the project.
Now based in the US, Atlan is building a platform for AI and data governance and counts corporate biggies like Unilever, Cisco, News Corp and Ralph Lauren among its clients. The fresh funding comes amid rising demand for AI-data readiness and governance framework among companies. “Over the past year, boards have consistently asked their CIOs and CDOs about their AI roadmaps, who have realised that the main hurdle isn’t AI models but the lack of AI-ready data—data enriched with business context, trust and security. Atlan is addressing this by building the control plane for the data and AI stack integrating trust and context into the digital fabric,” co-founder Sankar said in a statement on Tuesday. Atlan claimed that its revenues have grown multi-fold in the past two years.
Data practitioners spend 30%-50% of their time finding and understanding data; the startup claims that its platform cuts that time by 95%. The company centralises data management. “…we built a solution that unifies data across warehouses, lakehouses, BI tools, and AI agents. In doing so, Atlan empowers data teams to leverage the entirety of their data at high velocity and scale by ensuring its quality, accuracy, and governance. This means data teams can efficiently and effectively collaborate on data that would be otherwise siloed for various use cases, including populating AI models with trustworthy data,” added Banka.