AI&MachineLearning

What we do

AI & Machine Learning, end to end.

AI engagements fail more often from unclear evaluation than from model choice, so that's where we start: defining what 'working' means for your use case, with a test set and success metric, before picking an architecture. For most product features, that means starting with a well-chosen off-the-shelf or lightly fine-tuned model behind a retrieval layer rather than jumping to custom training — training a model from scratch is rarely the right first move, and we'll say so even when it's the more impressive-sounding pitch. The early decision that matters most is scoping the failure modes: what the system should do when it's uncertain, how hallucination or misclassification gets caught before it reaches a user, and what human-in-the-loop review looks like at launch versus six months in. A typical build runs six to twelve weeks to a production pilot behind a versioned API, with an evaluation harness that runs alongside development rather than being an afterthought — regressions in prompt or model behavior should show up in CI, not in a customer complaint. Computer vision and time-series work follow the same discipline, just with labeled-data quality as the earlier gate. What separates a competent AI team from a demo shop: monitoring for drift and regression after launch, a clear cost model per inference before scaling traffic, and UX design that accounts for the model being wrong sometimes, because it will be.