Frontier Data Products

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.

Auto Research Data

01

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.

RL Environments and Agents

02

Rich, stateful environments for coding, computer use, research, and professional workflows — paired with tasks, trajectories, and reward signals for long-horizon training.

Rubrics and Verifiers

03

Evaluation systems that turn nuanced outcomes into reliable learning signals, combining executable checks with carefully designed criteria for quality, correctness, and process.

SFT and Trajectory Data

04

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.

Arena-Based Human Evaluation

05

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.

Expert Professional Domains

06

Domain experts create and judge work in science, finance, law, engineering, and other high-skill fields where surface-level fluency is not enough.

Multimodal and Computer Use

07

Data that teaches models to understand and act across screenshots, documents, interfaces, images, audio, and video — grounded in realistic tool-use tasks.

Off-the-Shelf Data

08

Production-ready datasets, benchmarks, and environments available without a custom build, spanning coding, research, agent workflows, and core model capabilities.

The evolution of agent data

As AI systems evolve from chatbots to persistent agents, the data infrastructure they need changes fundamentally.

1Chatbot Era

Single-shot prompt → response

Data paradigm

Instruction-tuning pairs and RLHF preference data. Static (prompt, response) examples curated by human annotators.

2Single-Session Agent

One query, one environment, one session

Data paradigm

Agent trajectories in sandboxed environments. RL rollouts, tool-use traces, and reward signals within a single bounded session.

3Persistent Agent

Always-on agents that learn and evolve

Data paradigm

Continuous, multi-day interaction streams with evolving environments, accumulated context, and self-improving agent behavior.

  • Long-horizon tasks spanning hundreds of steps
  • Multi-day, multi-stage workflows
  • Perceiving and adapting to living environment
  • User modeling for proactive action
  • Continuous self-evolution from experience
Evolvent's Focus

Let's build what's next, together.

Join leading AI teams partnering with Evolvent AI on agent data, benchmarks, and long-horizon environments. Book a 1:1 demo to get started.