Workflow: Scoring
Placeholder — risk / ops model
A production score with monitoring, not a notebook on a laptop.
Shadow IT models
No monitoring
Unclear owners
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Lift vs baseline (placeholder)
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Time-to-score (placeholder)
From classical ML to applied LLM systems. Grounded in your data, your rules, and an audit trail — not a demo that dies in a notebook.
Start an AI project →Click any stage to see how DatabyPassion delivers that part of the work.
We will not start with a model. We start with a decision, a baseline, and a stop rule.
What happens today, in hours and error.
When we kill the experiment.
Placeholder workflows — replace with named work when you are ready.
A production score with monitoring, not a notebook on a laptop.
Answers with citations over your corpus. We do not train on your data.
Placeholder metrics until you confirm real numbers.
Start a project and we will map the first 90 days.