Enterprise intelligence, built to evolve.

Recursive self-improvement for enterprise agents.

• Harness, tools, context and model tuned together, and judged on reliability, latency and cost per successful task.
• A gated loop of run, observe, evaluate, improve, verify and deploy, with release controls suited to each workflow.
• Fast fixes in the harness, and deeper changes to the model when needed.
• Our direction: systems that help find weaknesses, propose changes and evaluate the next generation of agents.

Private benchmarks that tell real progress from noise.

• Benchmarks built from your own tasks, measuring what your organization counts as success.
• Held-out tasks that every candidate change must pass before it is promoted.
• Checks for contamination, overlap and saturation, so a higher score reflects a real gain.
• A record that grows with every new failure, so a newer model can be tested against everything the system has learned.

Executable environments where agents learn by doing.

• Reproducible task environments, with setup and state managed for every run.
• Multi-step interaction and tool use, with every trajectory and execution trace recorded.
• Executable test cases and verifiers, with rewards tied to outcomes that can be checked.
• Red-team and blue-team adversarial testing that catches shortcut solutions and reward hacking.

Training tasks built from real enterprise work.

• Tasks built from enterprise workflows and authorized code repositories, each aimed at one capability.
• Expert-designed specifications and difficulty calibration, with synthetic generation under expert supervision.
• Tasks for proprietary languages, internal workflows and multi-agent tool use.
• Task sets that keep pace with stronger models: saturated tasks are retired, and harder ones are generated from observed failures.