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.