AI pilot
An AI pilot that supports a real decision
We build a bounded use case on your data, measure quality and separate a compelling demonstration from operational readiness.
Discuss your project →What the pilot includes
A pilot tests one defined hypothesis for a specific user or process. Before development, we fix the inputs, reference or validation method, permitted actions and stop conditions. The solution runs in a bounded environment and does not replace the existing process without separate approval.
- Agreed use case and dataset
- Prototype with necessary integrations
- A set of control examples
- Quality review and error log
- Recommendation for the next step
How results are evaluated
Criteria follow the task: retrieval accuracy, classification completeness, draft quality, processing stability or user effort. A technical metric does not replace a business criterion. Error types and situations requiring human review are recorded separately.
The decision after the pilot
The team receives documentation of the architecture, sources, tests, constraints and observed results. It can then scale, prepare data further, change the approach or stop the initiative. A pilot is not presented as a production system; operational security, reliability and support requirements are planned separately.
Fit before scope
Is this the right starting point?
Use these criteria for an initial orientation. The final recommendation follows a review of your context, data and constraints.
A good fit when
- A process or decision that needs improvement is identified
- An accountable sponsor and result user are available
- A measurable outcome or quality criterion can be defined
Resolve this first when
- The goal is simply to buy unspecified AI without a defined challenge
- A guaranteed impact is expected before data are assessed
- No one is accountable for accepting the result
A verifiable first stage
What you can use to make the next decision
The exact scope is agreed before work begins. These are typical decision artefacts, not promised business results.
AI use-case matrix
Challenges compared by value, data availability, risk and ease of verification.
Pilot boundary
One process with defined users, sources, human oversight and stop conditions.
Decision criteria
Quality measures and a process to scale, refine or stop the initiative.
Five questions · no datasets or credentials
Frequently asked questions
Frequently asked questions
How is a pilot different from a demo?
A demo shows a capability on a prepared example. A pilot uses approved real data, control cases and a predefined evaluation method.
Does the pilot integrate with our systems?
Only as far as necessary to test the hypothesis and permitted by access rules. A controlled copy or temporary import may be safer than full integration at this stage.
Can the whole team use it immediately?
We normally start with a limited user group. Access expands after quality, roles and logging have been validated.
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