AI / ML
Applied machine learning — forecasting, classification, and automation — scoped to a measurable business outcome.
The AI projects that deliver real, durable value share a pattern: they start from a specific, measurable business outcome — reduce churn by X%, cut manual review time by Y hours a week — and work backward to the right technical approach, rather than starting from “we should have an AI feature” and searching for a use case afterward.
What’s included
An honest scoping phase that starts with a data audit — where the relevant data lives, how consistent it is across systems, and whether there’s enough labeled history to actually learn from — because that audit, not model architecture, is usually the deciding factor in whether a project is ready to build. A recommendation for the right level of sophistication for the actual problem, which is often a simpler model, a rules-based system, or a fine-tuned off-the-shelf model rather than a custom architecture built from scratch. Clear evaluation metrics agreed before development starts, so success is measurable rather than a subjective impression. Production deployment with monitoring for model drift, since a model’s accuracy at launch doesn’t guarantee its accuracy six months later as real-world data shifts. And documentation clear enough that your team understands what the model is actually doing, not just that it works.
When to bring us in
Before committing to “we need a custom model” — a short scoping engagement upfront, focused on your data and the specific outcome you’re after, is the cheapest way to find out what’s actually achievable and what it will really take.