AI TRAINING & EVALUATION ENVIRONMENTS
Commission a task environment or evaluation suite in which supported mathematical and decision outcomes are checked by code, not judged only by the fluency of an explanation.
Available products. A customer-specific workflow.
CLI · MCP · Headless
Model post-training teams, agent developers, AI-platform builders and research groups evaluating computational work.
A useful implementation has a specific job. The scope is built around your original requirements, environment and acceptance criteria.
A language-based score can miss whether a plan satisfies constraints or an arithmetic result is actually correct.
Choose a task family with an explicit outcome and an applicable independent check. The score reflects the property being evaluated, rather than presentation alone.
Reused instances, leaked answers or hidden shortcuts can make scores look stronger than real generalization.
Define separate training and held-out cases, control access to reference answers and test the grading path with deliberately incorrect submissions.
A dataset without reset, action and result conventions is difficult to use in an existing training loop.
Deliver a versioned environment contract with task inputs, permitted actions, reset behavior, scoring and replay appropriate to the accepted workflow.
The final order identifies the selected products, integration work and operating responsibilities. Included components and work are not charged again.
Supported logic, exact arithmetic, graph, planning or configuration tasks with clearly stated inputs, objectives and constraints.
Checks for the requested property, with explicit treatment of invalid, incomplete, timed-out and unsupported results.
Agreed generation rules or retained cases, dataset splits, input identities and evaluation conditions.
CLI, MCP or headless product access plus the scoped adapter, reset/replay conventions, documentation and acceptance report.
An evaluation presents a supported planning model and then changes a permitted constraint. The agent must return a valid revised decision, not just explain how it would do so.
Explanation only—not a live product run or a claimed customer deployment.Agree the supported family, agent actions, resource budget, acceptance rule and separation of training and test data.
Agree the supported models, execution environment and acceptance measures before committing to the delivery.
Choose the relevant capability.
Not every workflow needs every product.
Task design, private datasets, grader integration and environment delivery are quoted by scope. Existing product access is priced separately where required and not billed twice when included in the project.
No. This offer supports the customer’s training or evaluation process. The environment, tool access and scoring contract are agreed for that process.
A private delivery can be scoped with explicit data rights, access and retention. Open-source publication is not a default requirement.
No. Each score must match the property checked. A returned feasible solution cannot be labeled a proven optimum unless the applicable independent proof succeeds.
Commission the model, integration and acceptance work needed for your task. Existing products stay independently available.
One capability. A connected workflow.
A team to put it into practice.