AfterQuery reportedly valued at $3.2B, YC's fastest-ever path to unicorn status

AfterQuery, a San Francisco startup that builds training data for AI models, has reportedly raised a round valuing it at $3.2 billion — a milestone that would make it the fastest launch-to-unicorn run in Y Combinator's history. TechCrunch reported the figure, crediting Forbes with the original story, and noted that AfterQuery could not immediately be reached for comment.

A 10x jump in under six months

The new valuation comes just five months after AfterQuery announced a $30 million Series A at a $300 million valuation in April, according to TechCrunch. That works out to more than a tenfold increase in under half a year. Y Combinator partner Gustaf Alströmer says no startup from the accelerator has ever reached unicorn status faster.

The company itself is barely older than its latest fundraise. Its two founders, now 22 and 23 years old, took part in Y Combinator's Winter 2025 batch about 18 months ago, TechCrunch reports.

Revenue and named customers

The valuation is not built purely on momentum. In April, AfterQuery said it had reached a $100 million annualized revenue run rate and was working with many of the largest AI labs, TechCrunch reports. Its named customers include Nvidia, Legora, and the Korean AI lab Motif Technologies.

From correct answers to professional workflows

AfterQuery belongs to a wave of companies, alongside Mercor and Scale, that hire knowledge professionals — doctors, lawyers, and other specialists — to produce training data for AI developers. Its twist is in what that data teaches. Rather than checking whether a model answers questions accurately, AfterQuery trains models and agents to complete tasks the way a professional would. The company describes this approach as "encoding the patterns, decisions, and reasoning of the world's best practitioners," a formulation TechCrunch quotes in its report.

That distinction matters for the kind of AI these startups help build. Data aimed at answer accuracy improves how models respond to prompts; data capturing how practitioners actually work is meant to help agents carry out multi-step tasks inside real jobs.

Why it matters

The reported round is a data point in two overlapping stories. The first is the scramble among AI labs for high-quality, expertise-heavy training data, which has turned specialist labor into a scarce input and produced a new crop of intermediaries to supply it. AfterQuery's pitch — teaching agents to work like professionals rather than merely answer questions — signals where labs and investors appear to believe the next gains lie: in agentic systems that execute whole workflows, not just chat.

The second is the pace itself. A tenfold valuation jump in five months, at a company run by founders in their early twenties, shows how quickly investors are compressing their timelines when a startup can point to a nine-figure revenue run rate. That cuts both ways: it rewards demonstrated traction, and it invites questions about what these marks are worth if demand for model training cools.

For now, the $3.2 billion figure is unconfirmed. Neither AfterQuery nor its investors have publicly detailed the round, and TechCrunch reports the company did not respond to a request for comment. If the numbers hold up, AfterQuery becomes the clearest example yet of how fast the AI data layer can move from idea to unicorn.