AfterQuery

What AfterQuery does

AfterQuery is an applied research lab that curates data solutions to accelerate foundation model development. They focus on capturing expert knowledge, including reasoning, decisions, tradeoffs, and context, and structuring it into training data for models. Their approach involves deep research into model failure modes in professional contexts to inform dataset and environment design. They offer various data products and services for both frontier AI research labs and enterprises, aiming to improve model performance beyond what can be achieved with models trained solely on outputs.

Products

• Supervised Fine-Tuning (SFT)High-quality prompt–response pairs and chain-of-thought reasoning traces, teaching models how to behave across complex tasks and providing a foundation of skills before RL begins.
• Reinforcement Learning + RubricsExpert-designed prompts with grading frameworks for reasoning and code generation, turning subjective judgment into scalable reward signals.
• Agent Environments (API / MCP)Custom environments across APIs, tools, and services, enabling training and evaluation of agents in real workflows.
• Computer Use TrajectoriesHuman-demonstrated interactions across browser and desktop environments, teaching models to navigate and operate software end-to-end.
• Rubric and Verifier-based RLCombines expert-crafted rubrics with automated verifiers that grade model outputs, rewarding nuance and penalizing shortcuts across reasoning, code generation, and instruction-following tasks.
• Tool-calling RL EnvironmentsProvides custom RL environments built on top of real APIs, MCP servers, and developer tools, enabling models to learn how to call, chain, and recover from errors across complex service workflows with automated evaluation.
• Computer-use and Browser-use EnvironmentsPairs high-fidelity browser and desktop environments with expert-demonstrated trajectories, teaching agents to navigate interfaces, complete multi-step workflows, and operate software.
• RLHFCaptures the subtleties of what makes one response genuinely better than another through RL from human feedback, training models to internalize the taste, judgment, and standards of domain experts across thousands of comparison pairs.
• Code GenerationSpans expert-written code, test cases, and debugging traces that teach models to write production-quality software, handle edge cases, and reason through architectural decisions.
• Professional DomainsDraws on 100,000+ verified practitioners across various fields, capturing tacit knowledge and real-world judgment that textbooks and synthetic data cannot replicate.
• Deep ResearchCovers long-horizon research tasks, teaching models to gather evidence across sources, synthesize findings, and produce thorough analyses.
• Loss AnalysesIdentifies where and why models fail in professional contexts through systematic study, pinpointing precise failure modes and distributional gaps.
• MultimodalTeaches models to see, interpret, and reason across image, audio, video, and text together, closing the gap between human and AI processing.
• Off-the-shelf DataOffers ready-to-deploy datasets across high-demand capability areas, providing immediate access to rigorously validated training data.
• Custom Evals and Training DatasetsTailors evaluation suites and training datasets to specific capability targets, designing prompts, rubrics, and environments to address exact model gaps.
• Custom Datasets (for Enterprises)Proprietary datasets specifically optimized for fine-tuning large language models to exact performance requirements and use cases.
• Vertical-specific AI ConsultingResearchers embed with enterprise teams to understand industry challenges and implement targeted gen AI solutions.
• End-to-End Agent DeploymentAgents integrated with internal firm context, including internal files and data sources, built with out-of-the-box or fine-tuned models.
• Simulation/RL EnvironmentsHigh-fidelity simulation environments that mirror production, allowing AI agents to be trained safely before deployment.

Key capabilities

• Capturing expert reasoning, decisions, tradeoffs, and context
• Structuring expert knowledge into training data
• Applied research approach to identify model failure modes
• Development of high-quality prompt-response pairs and chain-of-thought reasoning traces
• Expert-designed prompts with grading frameworks for RL
• Custom agent environments across APIs, tools, and services
• Human-demonstrated interactions for computer and browser use trajectories
• Automated verifiers for grading model outputs
• RL from human feedback (RLHF) for capturing subjective judgment
• Expert-written code, test cases, and debugging traces for code generation
• Access to 100,000+ verified practitioners for domain-specific knowledge
• Systematic study of model failure modes (Loss Analyses)
• Multimodal training data capabilities (image, audio, video, text)
• Off-the-shelf and custom data solutions
• Vertical-specific AI consulting for enterprises
• End-to-end agent deployment with internal firm context integration
• High-fidelity simulation/RL environments for safe agent training

Use cases

• Accelerating foundation model development
• Training models to behave across complex tasks
• Generating scalable reward signals for model training
• Training and evaluating agents in real workflows
• Teaching models to navigate and operate software end-to-end
• Rewarding nuance and penalizing shortcuts in reasoning, code generation, and instruction-following
• Enabling models to call, chain, and recover from errors across complex service workflows
• Teaching agents to navigate interfaces and complete multi-step workflows
• Training models to internalize taste, judgment, and standards of domain experts
• Teaching models to write production-quality software and handle edge cases
• Encoding domain-specific excellence into forms machines can learn
• Teaching models to gather evidence, synthesize findings, and produce thorough analyses
• Identifying and addressing precise failure modes and distributional gaps in models
• Training models to see, interpret, and reason across multimodal data
• Providing immediate access to validated training data
• Optimizing large language models for specific performance requirements and use cases
• Implementing targeted gen AI solutions for enterprises
• Deploying AI agents integrated with internal firm context
• Safely training AI agents before deployment in production

AI approach

AfterQuery is an applied research lab that curates data solutions to accelerate foundation model development. They focus on capturing expert knowledge, including reasoning, decisions, and context, and structuring it into training data. Their approach involves Supervised Fine-Tuning (SFT), Reinforcement Learning (RL) with rubrics, agent environments (API/MCP), and computer use trajectories to teach models how experts think and operate. They also conduct deep research into model failure modes in professional contexts.

Tech named: Supervised Fine-Tuning (SFT), Reinforcement Learning (RL), Rubric and Verifier-based RL, Tool-calling RL Environments, RLHF, Terminal-Bench 2.0

Industries served

• Medicine
• Law
• Finance
• Engineering
• Software Development

What it says sets it apart

• Focus on capturing how experts think (reasoning, decisions, tradeoffs, context) rather than just outputs
• Applied research lab approach to data curation
• Building datasets that reflect how experts actually solve problems step-by-step
• Utilizing 100,000+ verified practitioners for tacit knowledge capture
• Deep research into model failure modes in real professional contexts
• Custom environments for training and evaluation of agents in real workflows
• Combines expert-crafted rubrics with automated verifiers for nuanced grading
• Offers both off-the-shelf and custom data solutions tailored to specific capability targets
• Provides vertical-specific AI consulting and end-to-end agent deployment for enterprises

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.