Services / 01
Introduce AI into the systems your people already use, or move an existing AI application to a more suitable model, provider or platform. Start with a business task, validate the approach and make the transition in controlled stages.
Typical starting points
What we can scope
Choose a useful task, review data access and readiness, then integrate knowledge search, generation or assisted workflows where the evidence supports them. Agree how quality and operational impact will be measured before expanding the scope.
Assess models, prompts, retrieval, tool integrations, data boundaries, infrastructure and operating costs. Define the target and test a transition plan against representative tasks before switching production traffic.
Where suitable, use AI assistance to understand code, propose changes and support test creation. Engineers review the work, preserve essential behaviour and use regression tests before release.
Prompt and tool compatibility, model behaviour, retrieval quality, access control, latency and cost all need review. New embedding models may require rebuilding indexes; the actual system determines the migration work.
Internal knowledge search across approved documents. Document processing with human review. Assisted operations within agreed permissions. These examples describe possible scopes, rather than delivered customer stories.
Concrete outputs
Depending on the task: Python, application APIs, retrieval-augmented generation (RAG), semantic search and Azure or AWS services. Model and provider selection follows data, quality, deployment and commercial requirements.
Proposed delivery sequence
A shared delivery plan · 1 / 5
Map the task, current system, data boundaries and dependencies.
A shared delivery plan · 2 / 5
Agree architecture, evaluation criteria and the migration backlog.
A shared delivery plan · 3 / 5
Test representative tasks, failure cases and human review.
A shared delivery plan · 4 / 5
Introduce changes with checkpoints and a rollback route.
A shared delivery plan · 5 / 5
Monitor quality, cost and failures; transfer operational ownership.
Common questions
Usually there is more to review. Prompts, tool calls, model behaviour, retrieval, access controls, latency and cost can all change. We evaluate the target against representative tasks rather than assuming that a compatible endpoint produces equivalent behaviour.
It may. Different embedding models can produce incompatible vector spaces or dimensions. We inspect the actual retrieval system, determine whether indexes must be rebuilt, and plan validation and transition accordingly.
We define permitted data sources, access controls and provider settings with you. Training use, retention, hosting regions and contractual terms must be checked for the selected service. Consequential actions need appropriate human oversight.
Yes. An assessment can compare suitable approaches using your tasks, data requirements, deployment constraints and operating costs. It should also establish whether AI is appropriate for the problem.
A clear next step
Discuss introducing AI into an existing workflow or moving an AI application to a different model or platform.