AfterQuery
by AfterQuery
We teach machines how experts think — expert-driven reasoning data for frontier AI.
AfterQuery is an applied research lab that builds high-quality, expert-generated training datasets, evaluation suites, and agent environments for frontier AI models. It captures how professionals in finance, law, medicine, and software actually reason so labs and enterprises can train models on real expert judgment rather than raw internet data.
AfterQuery is a San Francisco applied research lab that curates expert-generated data solutions for frontier foundation-model development, built on the belief that AI progress now depends on quality reasoning data rather than raw internet text. Working with a network of roughly 100,000 verified professionals across medicine, law, finance, and software engineering, AfterQuery captures not just answers but the underlying reasoning, decisions, and tradeoffs experts apply — then structures that into training data. Its offerings span supervised fine-tuning datasets (prompt-response pairs with step-by-step chain-of-thought), reinforcement-learning data and expert-designed rubrics for evaluating reasoning and code, custom agent environments delivered as API/MCP servers and virtual sandboxes that simulate organizational workstations for business-process-automation agents, computer-use trajectories capturing human browser and desktop demonstrations, and evaluation suites that measure training effectiveness. Founded roughly in early 2025 and backed by Y Combinator, AfterQuery raised a $30M Series A in April 2026 at a $300M valuation led by Altos Ventures (with The Raine Group and BoxGroup), having already surpassed a $100M annual revenue run rate serving leading AI labs.
At a Glance
- Category
- Data & Analytics
- Pricing
- Contact for pricing
- Target Market
- AI Research Labs, ML Engineers, Foundation Model Teams, Enterprise AI Teams
- Founded
- 2025
- Headquarters
- San Francisco, USA
Key Features
- ✓Expert SFT datasets
Supervised fine-tuning prompt-response pairs that include step-by-step chain-of-thought reasoning from domain experts.
- ✓RL data and rubrics
Reinforcement-learning datasets and expert-designed rubrics for evaluating reasoning and code generation.
- ✓Agent environments
Custom API/MCP environments and virtual sandboxes that simulate real organizational workstations for training business-process agents.
- ✓Computer-use trajectories
Human demonstrations of browser and desktop interactions for training computer-use agents.
- ✓Evaluation suites
Purpose-built evals that measure how effectively a model has learned from training data.
Use Cases
- •Improve reasoning in frontier models
AI labs train models on expert chain-of-thought data to generalize better across complex professional tasks.
- •Train and evaluate agents
Teams use AfterQuery's MCP environments, sandboxes, and rubrics to train and stress-test business-process and computer-use agents.
- •Structure firm-specific expertise
Enterprises convert scarce professional judgment in law, finance, or medicine into structured training data.
Ideal For
Best For
- ✓Expert reasoning datasets for supervised fine-tuning
- ✓RL rubrics and evaluation suites for model training
- ✓Custom agent environments and computer-use trajectories
Market Analysis
Pros
- ✓Targets the data-quality bottleneck that increasingly limits model gains
- ✓Strong revenue traction ($100M+ run rate) for a young company
- ✓Broad, verified expert network across high-value domains
Cons
- ✗Services/data model rather than self-serve software
- ✗Depends on demand from a small set of frontier AI labs
Pricing
Custom
Contact for pricing
- ✓Bespoke SFT, RL, and evaluation datasets
- ✓Custom agent environments and sandboxes
- ✓Computer-use trajectory data
B2B data services engaged on a custom basis with AI labs and enterprises.
Sources
This page was written from 2 sources, 1 on domains other than afterquery.com.
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