Build agents that understand the work.
Source company data, evaluation tasks and interactive environments around the capabilities you need to measure or improve.
Begin with a capability brief.
Tell us which workflow your agent needs to perform, where it currently fails and what convincing evidence of success would look like. We use that brief to assess sourcing, task design and environment feasibility.
Projects are scoped individually. A data package, an evaluation suite and an RL environment are different deliverables, with different requirements for access, verification and usage rights.
Source relevant business context
Request selected, connected operational records with provenance, a field dictionary, quality notes and a defined license.
Evaluate a real workflow
Scope reviewed tasks, explicit policies, a repeatable setup and checks on actual outcomes. Record the agent setup, model versions and evaluation limits.
Develop an interactive environment
Define the tools, state transitions, reset behavior, observations and scoring needed for training or repeated evaluation. Validate the simulator before scaling.
A few useful answers.
Can we request a custom workflow?
Yes. Share the desired capability, required tools, current failure modes, rights requirements and acceptance criteria. Availability and feasibility are assessed before a project is agreed.
Do you provide ready-made datasets?
This site introduces our sourcing and development approach. We confirm availability, scope and permissions for each inquiry rather than presenting a public inventory of private company data.
Can an environment run in our infrastructure?
Deployment requirements are part of scoping. The package, integration, execution limits and security review must be agreed for the selected infrastructure.
There’s intelligence in the work you already do.
Let’s find out what it could become.