AfterQuery

What

AfterQuery is an applied research lab focused on training data for foundation model development. It serves AI research labs and enterprises that need higher-quality data than synthetic, public, or web-scraped sources typically provide.

Its core workflow appears to combine research, domain-expert partnership, and dataset production to create curated training data and custom data solutions. Based on the page content, it is positioned as a specialist data partner for teams working on model training, fine-tuning, reinforcement learning, and agent evaluation.

Features

• Curated training data library: Provides a browsable set of data solutions intended to improve model performance through more carefully constructed datasets.
• Custom dataset development: Supports requests for datasets built around a customer’s specific needs, which is useful for domain-specific model training programs.
• SFT pairs: Offers supervised fine-tuning examples with prompt-response and chain-of-thought style reasoning data to help models learn response behavior.
• Rubric-based reinforcement learning data: Supplies expert-designed prompts, grading rubrics, and test cases for reasoning and code generation tasks, supporting structured RL training and evaluation.
• API and MCP-based RL environments: Builds custom tool-use environments that enable systematic agent training and automated grading across apps, services, and workflows.
• Computer use environments: Provides expert-demonstrated trajectories in browser and desktop settings to train agents on navigating and operating software interfaces.

Helpful Tips

• Validate data fit against your target model objective: For this type of vendor, the most important buying criterion is whether the data supports your exact use case, such as instruction following, code generation, multimodal reasoning, or tool-using agents.
• Ask how research translates into deliverables: The page emphasizes research leadership, so procurement teams should confirm how those insights appear in dataset design, labeling standards, and evaluation methodology.
• Separate training data needs by stage: Teams often benefit from using different assets for SFT, reinforcement learning, and agent environment evaluation rather than treating all training data as interchangeable.
• Review expert involvement carefully: Since the company highlights partnerships with domain experts, buyers should examine what level of expert contribution exists for their domain and whether it is sufficient for specialized tasks.
• Clarify environment scope for agent training: For API, MCP, browser, and desktop environments, define the target tools and workflows early so the resulting environment is usable for measurable evaluation.