Define the working decision
What exactly the system should prepare, who it helps and what it must not do.
MADAI / PILOT METHOD
An AI pilot with a clear decision at the end.
A pilot is not a model demo. It is a bounded experiment with an owner, data boundary, evaluation set, operating controls and explicit criteria for continuation.
In brief
The methodology separates six decisions: purpose, data, baseline, quality, controls and operational readiness. Each decision requires evidence.
01 / FRAMEWORK
Measure the system before you scale it.
Architecture, data, models and automation are designed around specific work scenarios and tests, not around a general impression from a chat interface.
Build the evaluation set
Real tasks, correct sources, expected outputs and safety tests.
Prove operating readiness
Identity, logs, approvals, monitoring, fallback and incident procedure.
02 / IMPLEMENTATION
What has to be ready before production
The outcome must be repeatable, traceable and operable. A useful pilot separates baseline performance, retrieval quality, answer quality, tool actions, safety and human decisions.
- 01Baseline before AI
Time, error rate, quality and cost of the current process.
- 02Data boundary
Approved sources, sensitivity, permissions and retention rules.
- 03Exit criteria
Minimum quality, maximum risk and economic limit of the pilot.
- 04Change control
Who approves changes to model, prompt, tool or data source.
03 / DECISIONS
Questions that directly affect architecture.
The answers determine pilot scope, autonomy, model selection and operating cost.
How long should a pilot run?+
Long enough to cover representative scenarios and real users. The decision evidence matters more than an arbitrary number of weeks.
What is the most common mistake?+
Starting with a model before establishing a baseline, evaluation set and workflow owner.
When should the system move to production?+
When quality, safety, operations and accountability criteria are met and a regression plan exists.
Start with a task that can be tested.
We define the data, expected output, risks and evaluation set before choosing the model and integrations.