Bring AI into the environment you are responsible for.
The IT team rarely has the luxury of evaluating a model in isolation. It is responsible for identity, data, contracts, systems, security and what happens when a vendor or API changes.
Talk through your requirementsCan this fit the environment you own?
Make the use case, information boundary and operating responsibility explicit before standardizing a platform or commissioning a new application.
- Custom work earns its place when
- An approved platform leaves a material gap in access, integration, deployment or the way people need to work.
- Change course when
- Existing entitlements already solve the need, or identity, source access and lifecycle ownership cannot be established.
Ask to see the evidence.
- An end-to-end data and dependency map.
- Permission changes and forbidden requests tested with real roles.
- Responsibility for model changes, incidents and ongoing spend.
A constrained architecture comparison.
Test one valuable workflow against the approved platform and a custom option, with clear criteria for choosing between them.
The situation behind the request
You may have multiple AI pilots, inconsistent data sources and pressure from business units to deploy faster. Some employees want to use consumer tools; others need specialized knowledge or workflow applications.
Where the obvious solution falls short
A one-off chatbot can create more shadow IT. A blanket platform strategy may fail on unusual workflows. A self-hosted model may impose costs and maintenance your team has not planned.
How we would approach the work
Define the use case and required controls, compare commercial and bespoke options, validate source access and integration and establish a delivery architecture with real evaluation.
What the engagement may include
- Private/managed model choice and data-flow options.
- Permission-aware enterprise retrieval and knowledge applications.
- AI agents with bounded tools and approval states.
- Integration, quality, cost monitoring and client handover.
The choices worth making explicitly
We do not recommend custom RAG when Microsoft, AWS, Glean or another approved tool solves the problem. Custom engineering needs a specific justification.
What we would test before calling it ready
Ask for a permissions test, full dependency register, lifecycle plan and negative cases. The system must be maintainable after the first demonstration.
A sensible first step
Start with the most valuable constrained workflow, not a company-wide claim that every employee needs a new AI assistant.
Bring us the
difficult part.
A product idea, a broken workflow, scattered company knowledge or an integration others keep working around. Tell us what needs to change and what has already been tried.
Discuss a project