About
AfterQuery is an applied research lab that builds curated datasets, reinforcement-learning environments, and other data solutions for leading AI labs and enterprises. Its differentiation is access to nearly 100,000 verified professionals across domains such as finance, software engineering, medicine, and law, enabling it to encode and evaluate real-world expertise for foundation-model development.
Market
AfterQuery competes in the expert AI training-data, model post-training, data-labeling, and agent-evaluation market. It positions itself as a research-driven, software-first provider for frontier foundation-model developers, differentiating through domain-expert workflows, realistic edge cases and judgment, custom in-house data-creation tools, and end-to-end API/MCP reinforcement-learning environments rather than primarily generic annotation.
AfterQuery primarily serves frontier AI labs and foundation-model developers, especially teams responsible for model training, post-training, evaluation, and agent engineering. Its offerings are aimed at organizations that need expert-generated data and realistic software-workflow environments to improve complex AI systems.
At a Glance
Problem
AI models can generate answers but often fail at real professional work because success depends on decisions, tradeoffs, context, and domain-specific judgment—not merely outputs. AfterQuery addresses the resulting training-data bottleneck: much of the most valuable knowledge is embedded in how experts think and work, rather than documented online. The economic implication is that models trained primarily on conventional outputs plateau, limiting the quality of high-value AI systems and agents.
The central use case is helping frontier AI labs and enterprises train and evaluate models that can reason through complex professional tasks and operate in real workflows, rather than just produce plausible responses.
Product / Service
AfterQuery is an applied research lab and data-solutions provider that works with domain experts to capture their reasoning, decisions, tradeoffs, and context, then structures that work into training and evaluation data. Its offerings include supervised fine-tuning prompt–response pairs and reasoning traces, expert-designed reinforcement-learning rubrics, API/MCP agent environments, and human-demonstrated browser and desktop computer-use trajectories.
The benefit is a more realistic way to teach and test foundation models and agents: models can learn step-by-step problem solving, receive scalable reward signals for judgment and code generation, and practice using tools and software in end-to-end workflows.
Market
AfterQuery competes in the market for AI training data, reinforcement-learning infrastructure, agent environments, and model-evaluation solutions, with a specific focus on expert-generated data for frontier foundation models. The available evidence does not identify named direct competitors; instead, it positions AfterQuery around the specialized end of the market where generic web data and basic annotation are insufficient for teaching professional reasoning.
The company appears to have substantial early traction rather than being pre-revenue. AfterQuery says it closed a $30 million Series A at a $300 million valuation in April 2026 and had surpassed a $100 million revenue run rate; third-party reporting also lists $100 million in 2026 ARR, up from $6.5 million in 2025. The company further claims that leading AI labs use its datasets and reinforcement-learning environments across dozens of domains.
Founders & Leadership
Funding History
Y Combinator
Altos Ventures
Recent News
AfterQuery published a research post describing its work in applied AI training data and model-performance improvement. The post is part of the company’s research and benchmark coverage.
AfterQuery highlighted its partnership with The Raine Group, saying the work turns fragmented knowledge into structured, searchable intelligence across deal teams and improves workflow speed.
Sacra reported that AfterQuery reached $100 million in annualized revenue in April 2026 and described the company’s training-data, reinforcement-learning, and evaluation infrastructure products.
SiliconANGLE reported that AfterQuery raised $30 million at a $300 million valuation, with Altos Ventures leading and Y Combinator, The Raine Group, and BoxGroup participating. The company planned to use the funds to expand its expert network, workforce, and enterprise business, including custom AI agents.
AfterQuery announced a $30 million Series A at a $300 million valuation, led by Altos Ventures with participation from The Raine Group, Y Combinator, BoxGroup, and Latitude Capital.
The Business Wire release said AfterQuery completed its $30 million Series A at a $300 million valuation and had surpassed a $100 million annual revenue run rate. The capital was earmarked for expanding its expert network, domain coverage, team, and enterprise solutions business.
AfterQuery published research on how its expert-curated data supports foundation-model development and model-performance evaluation, using τ²-Bench as the benchmark context.
AfterQuery promoted its Experts program, inviting professionals to share their expertise through contract-based AI training work and earn on their own terms.
AfterQuery outlined its thesis that professional expertise must be extracted, structured, and encoded into formats that AI models can learn from, positioning high-quality data as a source of competitive advantage.
Active Roles
33Business Model
AfterQuery makes money by providing curated datasets, reinforcement-learning environments, and specialized data solutions to leading AI labs and enterprises. The available evidence supports an enterprise data-services model, but does not specify standardized pricing or subscription terms.