Auto Research Data
01End-to-end research tasks and trajectories covering source discovery, evidence synthesis, citation validation, and long-form analysis across open-web and professional knowledge workflows.
Frontier Data Products
Intelligence is built through more than reading. Models need practice, feedback, evaluation, and experience inside environments that behave like the real world.
We build the data systems behind that learning: trajectories, RL environments, verifiers, expert judgment, and multimodal tasks across coding, research, and computer use.
As model capabilities expand, our products expand with them — from focused benchmarks to persistent, open-ended workflows.
End-to-end research tasks and trajectories covering source discovery, evidence synthesis, citation validation, and long-form analysis across open-web and professional knowledge workflows.
Rich, stateful environments for coding, computer use, research, and professional workflows — paired with tasks, trajectories, and reward signals for long-horizon training.
Evaluation systems that turn nuanced outcomes into reliable learning signals, combining executable checks with carefully designed criteria for quality, correctness, and process.
High-signal demonstrations and multi-step traces that show models how experts code, use tools, recover from errors, and complete complex workflows from start to finish.
Pairwise arena evaluations where trained human judges compare model behavior at scale, revealing differences in correctness, usefulness, reasoning quality, and preference that static scores miss.
Domain experts create and judge work in science, finance, law, engineering, and other high-skill fields where surface-level fluency is not enough.
Data that teaches models to understand and act across screenshots, documents, interfaces, images, audio, and video — grounded in realistic tool-use tasks.
Production-ready datasets, benchmarks, and environments available without a custom build, spanning coding, research, agent workflows, and core model capabilities.
As AI systems evolve from chatbots to persistent agents, the data infrastructure they need changes fundamentally.
Instruction-tuning pairs and RLHF preference data. Static (prompt, response) examples curated by human annotators.
Agent trajectories in sandboxed environments. RL rollouts, tool-use traces, and reward signals within a single bounded session.
Continuous, multi-day interaction streams with evolving environments, accumulated context, and self-improving agent behavior.
Join leading AI teams partnering with Evolvent AI on agent data, benchmarks, and long-horizon environments. Book a 1:1 demo to get started.