The moat is the engine, not the output

Singh's claim is that a data business has a deeper moat than a software business, and Tracxn is the case she wants to argue. Public markets have Bloomberg, FactSet and Capital IQ because roughly 50,000 listed companies arrive pre-structured; private markets have millions of companies and almost no structure, so the answer a user sees — every generative-AI company mapped across the value chain — sits on an iceberg: an engine tracking more than 700 million entities that picks up a domain registered yesterday, and a 90-person sector-analyst team doing final curation inside a 750-strong org. The commercial lesson came early and counterintuitively. At Stanford in 2013 the founders tested a $100 self-serve product against a $5,000-a-month curated one, and the expensive, human-touched version won, because the VC and PE professionals buying it are paid too well to scrape the web themselves. Everything downstream follows: inbound content instead of cold calls, a one-and-a-half-month sales cycle at roughly $10,000 realised pricing, data produced in India and sold to the world, corporates who scan markets for vendors rather than acquisitions, and AI deployed behind the interface rather than as the chat box customers keep saying they don't want. The listing, she argues, is not the exit but day zero — a twenty-year commitment with an exam every quarter and more than 70,000 shareholders now watching.