AI Solutions

Good candidates are usually repeatable, reviewable tasks with clear inputs, outputs, data permissions, and a way to measure usefulness.

It can use approved sources through a controlled retrieval approach. Access, data handling, quality, and permissions must be designed for the specific use case.

We define representative tasks, expected qualities, known risks, review criteria, and human escalation paths, then test the system against those expectations.

We identify approved data, access boundaries, retention needs, provider settings, and sensitive information before implementation. The controls depend on the use case, systems, and applicable obligations.

Yes, when approved APIs, permissions, and data flows are available. We define stable integration boundaries, validation, error handling, and monitoring for the systems in scope.

Models can produce incorrect, incomplete, or inconsistent output and may behave differently as inputs change. We document important limitations, add guardrails where appropriate, and avoid presenting model output as certain.

Review and escalation are designed around the consequence of an error. Higher-impact decisions need stronger human control, clear permissions, and a way to stop or correct the workflow.

Cost depends on discovery, data preparation, integrations, model or infrastructure usage, evaluation, security, interface work, and ongoing monitoring. Assumptions and expected operating costs are discussed during scoping.

Usually. Data, models, provider behavior, user needs, and connected systems can change. Maintenance may include evaluation, prompt or retrieval updates, dependency work, monitoring, and review of safeguards.