When if-else falls short: classification, forecasts, recommendations, CV. We ship from data and metrics to a live API — integration and drift monitoring, no AI-for-AI theater.
A model without production is an R&D cost, not an asset. Skip metrics, data pipelines, and monitoring — and you pay for an experiment you can’t scale or trust in decisions. The risk: solid test accuracy and a quiet failure in the wild.
In this engagement:
We start with the problem and the data: is there signal, what’s the metric, where does it plug in. Build a simple baseline. If it clears the bar — strengthen the model and ship an API with monitoring. If not — we say so and don’t burn budget on complexity. Scope and timeline after a short review of inputs and the goal.
Depends on the data and how clear the metric is. A narrow task with a decent dataset moves faster; raw data and a fuzzy goal need more prep. We lock timeline and stages after reviewing inputs — not guesswork.
Data volume and quality, labeling needs, model complexity, integrations, and monitoring. We first check whether a baseline can move the needle; budget and scope come after that review, stage by stage.