Data, Cloud & AI
Cloud and data platforms built to be operated, not just demonstrated.
There is a wide gap between a system that demonstrates well and a system that survives contact with real users, real data volumes, and a real on-call rotation. We work on the second kind.
Cloud that stays affordable
Cloud costs rarely spiral because of one bad decision. They spiral because nobody owns the total. We review the estate, identify what is actually driving spend, and separate the savings that are safe to take from the ones that trade resilience for a smaller bill. Right-sizing, storage lifecycle policies, and removing genuinely idle resources usually account for most of the gain.
Data platforms
A data platform earns its keep when people trust the numbers enough to act on them. That is a question of lineage, validation, and clear ownership far more than it is a question of tooling. We design pipelines that fail loudly rather than quietly producing wrong answers.
AI, assessed honestly
We assess where AI genuinely fits your operations and, just as importantly, where it does not. Many problems presented as AI problems are better solved by fixing a data-entry process or an integration. When a machine learning approach is warranted, we are specific about the training data required, how performance will be measured, and what happens when the model is wrong.
Other practices
Let us look at the problem properly.
Tell us what you are dealing with. If we are not the right fit, we will say so and point you somewhere better.