Supervised fine-tuning datasets
Produces high-quality prompt-response pairs and chain-of-thought demonstrations for supervised fine-tuning, helping models learn task behavior before reinforcement learning.
Applied research lab creating training data and evaluation environments for foundation models.
AfterQuery is an applied research lab focused on curating data solutions for foundation model development. Its public positioning centers on capturing how experts think, then turning that reasoning, judgment, and workflow behavior into training data that models can learn from.
The company’s offerings span supervised fine-tuning data, reinforcement learning signals, agent environments, computer-use trajectories, and custom datasets for enterprises. The site also frames the work around research into where models fail in real professional contexts, so datasets and environments are designed around those failure modes rather than generic outputs.
Produces high-quality prompt-response pairs and chain-of-thought demonstrations for supervised fine-tuning, helping models learn task behavior before reinforcement learning.
Combines expert-written rubrics with automated verifiers to score outputs in reasoning, code generation, and instruction-following tasks.
Builds custom RL environments on top of APIs, MCP servers, and developer tools so models can practice tool use and recover from errors in workflow-like settings.
Pairs browser and desktop environments with human-demonstrated trajectories to train agents on multi-step software tasks.
Creates custom datasets and consulting engagements for enterprises, including vertical-specific AI work and end-to-end agent deployment.
Covers professional domains, deep research, multimodal tasks, code generation, and loss analysis to target specific model gaps.
Teams building foundation models can use expert demonstrations, preference signals, and verifier-backed training data to improve reasoning and instruction following.
Enterprises can commission custom datasets and vertical-specific consulting to address domain-specific model gaps and implementation challenges.
Product teams working on agents can train and evaluate tool use, error recovery, and workflow execution in API, MCP, browser, or desktop environments.
Research teams can study model failure modes through loss analyses and curated datasets that target where models break down in professional settings.
Engineering teams can use code-generation datasets and debugging traces to improve production-oriented coding behavior and architectural reasoning.
AfterQuery is positioned as an applied research lab that curates data solutions for foundation model development. Its public materials emphasize expert demonstrations, preference signals, environments, and evaluation data rather than a single software interface.
The site describes products for supervised fine-tuning, rubric- and verifier-based RL, tool-calling RL environments, computer-use and browser-use environments, RLHF, code generation, multimodal training, and custom datasets for enterprises.
The enterprise page says AfterQuery partners with organizations on custom datasets, vertical-specific AI consulting, end-to-end agent deployment, and simulation or RL environments.
The public site does not list a live pricing table on the pricing URL; that page currently shows a not-found message instead of plan details.
Traffic data is for reference only.
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