AI creates value when its role, context and limits are explicit.
RAG, LLMs, MCP and assisted automation: I build bounded, supervised use cases tied to a concrete measure of productivity or quality.
The problem
- AI experiments multiply without a shared business problem or value measure.
- A generic assistant loses context, produces variable answers and weakens trust.
- Useful knowledge is scattered, poorly structured or difficult to keep current.
- Security, confidentiality and human-validation risks are addressed too late.
What I look at
- The precise business task to accelerate, make safer or enrich.
- Knowledge sources, access rights, quality and freshness.
- The context given to the LLM, tools exposed through MCP and the limits of each action.
- Human oversight points, traceability and genuinely observable value indicators.
What it unlocks
- An understandable, focused AI use case integrated into the existing product.
- A RAG and LLM loop that enriches knowledge without losing its provenance.
- Measurable productivity or quality gains, with a human retaining the decision.
How I help
01
Bound the use case
I start from a real task, define the useful context and set the situations where the system must stop or request validation.
02
Design the knowledge loop
I connect RAG, LLMs and business sources to produce better-grounded answers and recommendations that improve over time.
03
Secure and measure
I limit access, log actions, maintain human oversight and measure value before expanding the scope.
Signs it is time
- The team discusses models before naming the problem to solve.
- The system receives more data or permissions than its task requires.
- No human is clearly responsible for validating a sensitive output.
- Success is described as an impression instead of a before-and-after measure.
Related examples
FAQ
Do existing tools need to be replaced to integrate AI?
No. A surgical use case can often integrate into the existing product and workflows. The right choice depends on the expected gain, the data and the risk level.
How do you keep control over an AI agent?
By bounding its context and tools, limiting permissions, logging actions and requiring human validation at sensitive steps.
Clarify whether this topic is a priority, and how to tackle it.
If these signals resonate, the next step does not have to be a long engagement: we can first make risks, dependencies and decisions explicit.