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An AI assistant is planned. Until it is genuinely useful we would rather point you at the page that actually answers your question.
Most commercial AI work today is integration rather than training: taking a capable general model, connecting it to your data and systems, constraining what it can do, and measuring whether the output is reliable enough to depend on.
The categories that recur are retrieval over your own documents, classification and extraction from unstructured text, and agents carrying out multi-step tasks against real systems.
Suitability
And when it is not. Choosing the wrong tool costs more than any implementation detail.
In practice
Answering from your documents with citations, so claims can be verified at source.
Structured extraction with confidence scoring and a review queue for uncertain cases.
Scored test sets running in CI so quality changes are visible before release.
Considerations
Practical notes from running this in production.
FAQ
Tell us what you are trying to build or fix. We will come back with scope, approach and an honest view on cost and timeline.