AI and machine learning
Use advanced methods where they clarify a real decision.
AI is not a substitute for a useful question or dependable context. The right starting point is a bounded use case the team can evaluate honestly.
The problem
Interest in AI is high, but the decision it should improve is still unclear.
A model can produce an answer without making that answer useful, explainable, or appropriate for the work. Weak definitions, incomplete context, and unclear ownership make experiments difficult to judge.
Berthside starts with the decision and the information around it, then tests whether an AI or machine-learning approach earns a place in the workflow.
What the work can include
- Preparing and documenting databases, source data, definitions, and retrieval boundaries.
- Exploring data agents and natural-language search for bounded, reviewable internal questions.
- Evaluating retrieval-augmented generation (RAG) patterns that return source-grounded context the team can review.
- Assessing regression or classification when a measurable prediction or category is genuinely useful.
- Testing forecasting or anomaly detection where the history, signal, and response process support it.
- Documenting what the method can and cannot support before any wider adoption.
A responsible experiment makes the uncertainty visible alongside the output. Security, privacy, client-data handling, and review requirements remain part of the decision.
A practical next step
Start with the decision, not the model.
Bring the workflow, the signal you wish were easier to see, and the constraints the work must respect.
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