It depends on the use case, but generally we need representative historical data related to the outcome you want to predict or automate, along with access to whoever understands how it was produced. We start every engagement with a data readiness review, so you don't need perfect data upfront. You need enough to evaluate honestly whether the model is feasible.

Dashboards tell you what happened; AI earns its keep when you need to predict what will happen or automate decisions at scale. The threshold is practical: you have a repeatable decision with measurable value, enough historical data to learn from, and a team ready to act on the output. If a specific decision improved by even a few percent is worth real money, it's time.

AI boutiques often build impressive models that never survive contact with production; large consultancies bring process and overhead but hand off to delivery teams far from the original thinking. We combine data engineering, machine learning, and cloud engineering in one team, so models get built on solid pipelines, deployed properly, and maintained after launch.

We monitor for it rather than discover it. Deployments include drift and accuracy monitoring from day one, and retraining and tuning provisions are defined in the engagement, not negotiated after something breaks. Just as important, we gate deployment on honest pilot evaluation, so a model that doesn't meet its success criteria doesn't ship in the first place.

It varies with data readiness and use-case complexity, so we scope it per engagement rather than quoting a universal number. The path is phased: a data readiness assessment, then a working pilot model against defined success criteria, then production hardening. You'll have a concrete timeline after scoping and a working signal of feasibility early, not at the end.

A one-time project ends with a validated model, documentation, and handoff to your team. Ongoing AI managed services add continuous monitoring, drift detection, scheduled retraining, performance reporting, and incremental improvements. Models degrade as the world changes, so if the decision it supports matters month after month, plan for the managed option.

We review training data for representativeness, test model performance across relevant segments rather than only in aggregate, and document known limitations and appropriate uses. For consequential decisions we recommend human review checkpoints in the workflow. You get transparency about what the model should and shouldn't be trusted to do, in writing.