Combine application maintenance, performance support, and AI evaluation with a clear operating model. Give the product a path to reliable operation, ongoing improvement, and team ownership.
Product and operations teams taking an AI-enabled service into sustained use.
AI products continue to change as usage, data, and model behavior evolve. The operating plan needs to cover the application, its integrations, and the quality of its AI-driven behavior.
Identify dependencies, operating responsibilities, support routes, and the checks needed for a release. Agree how issues will be assessed and escalated.
Review application health, performance, and agreed AI behavior measures. Connect observations to a maintenance backlog and named decision makers.
Exercise representative cases when the application, model, retrieval sources, or workflow changes. Use results to inform release decisions and follow-up work.
Deliver agreed fixes and improvements, update documentation, and support knowledge transfer. Help the internal team develop the capability to own and evolve the product.
We agree the scope and outputs around your starting point. A typical engagement can include:
New documents and changing user questions alter what an assistant needs to do. A representative evaluation set helps the team review changes, investigate failures, and decide which improvements should be released next.
Bring a priority workflow and the people who own it. We will review the intended outcome, dependencies, and constraints, then define an initial scope and acceptance criteria.
Support coverage, response expectations, and service responsibilities are agreed for each engagement.
Plan this engagementBring product design, architecture, and full-stack engineering together. Build custom AI applications, retrieval-augmented generation, machine learning solutions, and intelligent workflows around a defined business outcome.
Explore this serviceApply Safe AI practices across the development lifecycle: specification, implementation, review, testing, and release preparation. Combine automation with clear acceptance criteria and accountable decisions.
Explore this serviceModernize legacy applications, connect enterprise systems, and develop cloud and data foundations that support your AI priorities. Sequence the work around business continuity and a practical migration path.
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