AI implementation

Not always. We can often use an existing model and give it access to the information it needs from your business documents or systems. We consider training a custom model when test results and requirements justify the extra cost.

We agree which data may be processed, where it may be sent and who may access the results. Provider selection, infrastructure and retention follow these requirements.

It can, but we set the level of autonomy according to the consequences of an error. For important operations, we design human approval steps, permission limits and a record of actions taken.

We create representative examples and evaluation criteria such as accuracy, completeness and consistency with sources. We compare results with the expected outcome and the current way of working. Failures help identify limitations and where human review is needed.

Yes. We select one task, available data and a group of people to assess the result. The pilot answers specific questions about quality, usefulness and cost. Its findings can support a rollout, a different approach or a decision to stop that use case.

We estimate costs from task volumes, data size and model selection. We can implement usage tracking, limits and alerts. We also assess which steps need a model and which are better handled by conventional code or a previously computed result.

These changes can affect answer quality. Before rollout, we rerun evaluation examples and check integration behaviour. We agree how to version configurations, monitor results and return to the previous solution if needed.