We teach machines how experts think.
Backed by angels from
Powering every frontier AI research lab
Problem
AI researchers and enterprises are
hitting walls with suboptimal data solutions.
Today’s models can generate answers. But they struggle with real work. Because real work isn’t just outputs. It’s decisions, tradeoffs, and context. That knowledge doesn’t live on the internet. It lives inside experts.
Expertise has never been captured. Until now.
The most valuable knowledge isn’t written down. It exists in how professionals think. Not just answers, but reasoning. Decisions. Tradeoffs. Context. We work with domain experts to capture that thinking—then structure it into training data models can learn from.
Our solution
We turn real-world work into training data.
AfterQuery is an applied research lab curating data solutions for frontier foundation model development.
Models trained on outputs plateau. Models trained on reasoning improve.
We build datasets that reflect how experts actually solve problems— step by step, decision by decision.
Our data includes:
High-quality prompt–response pairs and chain-of-thought reasoning traces. Teaching models how to behave across complex tasks.
Expert-designed prompts with grading frameworks for reasoning and code generation.
Turning subjective judgment into scalable reward signals.
Custom environments across APIs, tools, and services. Enabling training and evaluation of agents in real workflows.
Human-demonstrated interactions across browser and desktop environments. Teaching models to navigate and operate software end-to-end.
Research
Our approach starts with research: where exactly do models break down in real professional contexts? Why do these failure modes exist? We take a proactive stance and every domain has its own failure patterns.
