BESPOKE LABS
Data Research Lab — SF Bay Area
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Launching Nimble models: #1 decision models outperforming Jev. Read more.

Data Research Lab SF Bay Area

Build environments that enable agents in the real world.

Environment research & infrastructure for the agent revolution.

[ Frontier Labs ] [ Neolabs ] [ Enterprises ]
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What We Do

As AI agents move from demos to production, the bottleneck for reliability is no longer the model, it’s the environment.

We are an applied AI research lab building the environment infrastructure for the agent revolution.

01

RL environments & infrastructure

Company-scale simulations with real codebases and microservices, allowing agents to master the complex, long-horizon workflows required for production.

02

Agent evaluation & optimization

Automated prompt and policy searches via the GEPA optimizer achieve superior accuracy significantly faster than traditional, manual prompt engineering.

03

Production-grade RL & benchmarking

Collaborative research in RL, curation, and benchmarking ensures that training environments keep pace with the rapidly advancing frontier of model capabilities.

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Who We Serve

Two audiences. One foundation: real environments.

[ For Frontier & Neo Labs ]

Train in environments that match production.

Use our environments as training grounds to teach new tasks and improve reliability.

[ For Enterprises ]

Build AI that doesn’t break in production.

Enterprises rely on us to make AI agents dependable. With Bespoke, you can:

  • Evaluate agents in environments that mirror your systems and processes.
  • Optimize prompts and policies automatically with GEPA and RL.
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Our Research Track Record

We ship products. And we advance the field.

This combination of open research, benchmarks, and deployed optimization infrastructure makes Bespoke the environment partner of choice for the agentic era.

01

OpenThoughts

One of the best open reasoning datasets.

ICLR 2026Dataset10k+ Monthly Downloads
Openthoughts.ai ↗
02

Terminal-Bench

First environment-based benchmark for agentic systems.

ICLR 2026Benchmark
Cited by Anthropic · Cited by Open AI · Cited by Google DeepMind Read the paper ↗
03

GEPA

Evolutionary algorithms for prompt optimization.

ICLR 2026Algorithm200+ Teams
Read the paper ↗
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Get in Touch

Join us in building the foundation for the agentic world.

For Talent

Join the Team

Full-time researchers and engineers who want to do the most important work in AI, not just the most visible.

See open roles →
For Experts

Build with us

Domain experts interested in building RL environments and data pipelines.

Apply as expert →
For Labs & Enterprises

Work with us

Frontier labs or enterprises looking to improve agent reliability.

Start a conversation →