AfterQuery closed a $30 million Series A at a $300 million valuation, led by Altos Ventures with participation from The Raine Group, Y Combinator, and BoxGroup. The San Francisco startup, founded just three months ago by Spencer Mateega and Carlos Georgescu, claims it's already hit a $100 million annual revenue run rate selling training datasets to AI labs.
The company's pitch addresses a real problem: frontier models trained on web scraping and static outputs are plateauing. AfterQuery works with domain experts to capture their reasoning processes—the decision trees, tradeoffs, and contextual thinking that don't exist in public data. They're selling the invisible knowledge that separates competent professionals from algorithmic pattern matching.
That $100 million revenue claim deserves scrutiny. Three months from founding to nine-figure revenue suggests either they're counting contracted future revenue very generously, or they've tapped into genuinely desperate demand from AI labs hitting data quality walls. The timing aligns with reports that companies like OpenAI and Anthropic are struggling to find high-quality training data beyond internet scraping.
For developers, this signals where the puck is heading: synthetic data and expert-labeled datasets are becoming infrastructure, not nice-to-haves. If AfterQuery's revenue numbers hold up, expect more startups chasing the same opportunity—and higher costs for quality training data across the board." "tags": ["training-data", "funding", "expert-datasets", "ai-labs