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.
AfterQuery
RivendicaAfterQuery creates training data and evaluation environments for foundation model development, capturing expert reasoning, tool use, and workflow behavior.
Overview
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.
Core capabilities
Rubric and verifier-based RL
Combines expert-written rubrics with automated verifiers to score outputs in reasoning, code generation, and instruction-following tasks.
Tool-calling environments
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.
Computer-use and browser-use trajectories
Pairs browser and desktop environments with human-demonstrated trajectories to train agents on multi-step software tasks.
Enterprise data solutions
Creates custom datasets and consulting engagements for enterprises, including vertical-specific AI work and end-to-end agent deployment.
Domain-specific training coverage
Covers professional domains, deep research, multimodal tasks, code generation, and loss analysis to target specific model gaps.
Where it fits
Foundation model training
Teams building foundation models can use expert demonstrations, preference signals, and verifier-backed training data to improve reasoning and instruction following.
Enterprise AI programs
Enterprises can commission custom datasets and vertical-specific consulting to address domain-specific model gaps and implementation challenges.
Agent development and evaluation
Product teams working on agents can train and evaluate tool use, error recovery, and workflow execution in API, MCP, browser, or desktop environments.
Failure-mode research
Research teams can study model failure modes through loss analyses and curated datasets that target where models break down in professional settings.
Code generation improvement
Engineering teams can use code-generation datasets and debugging traces to improve production-oriented coding behavior and architectural reasoning.
Pros and Cons
Pros
- Broad coverage across SFT, RL, RLHF, tool use, browser use, code generation, multimodal work, and enterprise datasets.
- Focuses on expert reasoning, decisions, tradeoffs, and workflow behavior instead of only final outputs.
- Offers both off-the-shelf data and custom engagements for organizations with specific capability gaps.
- Includes environments and trajectories that can reflect real tools, APIs, and software workflows.
- Publishes educational knowledge-base content about training data, SFT, preference training, and evaluation design.
Cons
- The public site does not provide a current pricing page with plan details; the pricing URL currently returns a not-found page.
- Feature depth is described at a high level on the site, but the public pages do not expose technical implementation details or integration documentation.
FAQ
What is AfterQuery?
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.
What kinds of data products does AfterQuery offer?
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.
Does AfterQuery work with 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.
Is pricing publicly listed?
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.
Quick Facts
- Category
- AI training data / applied research
- Primary users
- Foundation model teams and enterprises
- Offerings
- SFT data, RL environments, RLHF, custom datasets, consulting
- Enterprise focus
- Custom datasets, agent deployment, simulation/RL environments
- Source domain
- afterquery.com
- Pricing page
- Currently shows a not-found message