
AfterQuery provides expert-curated datasets and reinforcement learning environments for frontier AI models.
AfterQuery sells expert-curated datasets and reinforcement learning environments for training and evaluating AI models. Its products page describes supervised fine-tuning data, rubric and verifier-based RL, tool-calling RL environments, computer-use and browser-use environments, code generation data, multimodal data, off-the-shelf datasets, and custom evaluation and training datasets.
For enterprises, AfterQuery offers custom datasets, vertical-specific AI consulting, end-to-end agent deployment, and simulation environments. The offerings draw on a network of professionals across domains such as software engineering, finance, medicine, and law.
AfterQuery operates in a market where frontier AI labs need training data and evaluation environments that capture professional workflows not available in public internet corpora. Its own research materials argue that compute and model architectures are commoditizing while high-quality professional data becomes a key source of differentiation.
The broader AI data market is competitive, with Forbes naming Mercor, Scale AI, micro1, and other human-data providers as peers. Demand remains tied to AI labs and enterprises seeking better data for coding, finance, medicine, law, computer use, and agent workflows.
AfterQuery differentiates itself by combining expert contributors with custom software systems for creating, checking, and evaluating training data. Its official Series A post says the company works with nearly 100,000 verified practicing professionals and builds workflows in-house rather than outsourcing data collection.
The company also publishes research showing performance gains from its datasets and environments, including improvements on Terminal-Bench 2.0 and tau2-bench. That research-led sales motion gives model labs and enterprises concrete evidence that curated professional-domain data can improve agent behavior.