AfterQuery reportedly valued at $3.2 billion five months after Series A

AI training-data startup AfterQuery has reportedly raised a round at a $3.2 billion valuation, five months after announcing a $30 million Series A at a $300 million valuation. The reported increase is more than tenfold in less than half a year.

Y Combinator partner Gustaf Alströmer said the company is the accelerator's fastest startup to progress from launch to unicorn status. AfterQuery's founders, now aged 22 and 23, participated in Y Combinator's Winter 2025 cohort 18 months ago.

A rapid valuation increase

Forbes first reported the financing. AfterQuery could not immediately be reached for comment, so the company has not publicly confirmed the reported round or disclosed its size.

In April, the San Francisco-based company said it had reached an annualized revenue run rate of $100 million. At that time, it said it was working with many of the largest AI labs and named Nvidia, Legora and the Korean AI lab Motif Technologies as customers.

Training agents for professional work

AfterQuery belongs to a group of AI training-data companies that employ knowledge professionals, including doctors, lawyers and other specialists, for model training. The approach follows a market shaped by companies such as Mercor and Scale.

Its stated focus differs from training models simply to produce accurate answers. AfterQuery says it trains models and agents to work as professionals do when completing tasks, describing the process as encoding the patterns, decisions and reasoning of leading practitioners.

What businesses should examine

The reported valuation and revenue figures underline the commercial attention on specialised human input for AI systems. Businesses evaluating AI agents should distinguish between systems that can answer questions and those trained on the decisions and workflows needed to carry out professional tasks.

That distinction makes the quality, relevance and task-specific nature of training data a practical consideration when organisations assess whether an AI agent can support work that requires expert judgement.