We build our own products, and we build our clients' AI platforms. Same team. Same platform. Same rule: production, or we don't call it shipped.
Lumara
CommandIQ
BrewwMost of the enterprises we meet don't have an AI problem. They have an AI-in-production problem. Models are cheap now. Foundations aren't. Programs stall on data quality, platform tax, and controls nobody wrote down before the first pilot. DataCaffe.ai exists to close that gap — by shipping our own products end-to-end, and by embedding the operators who built them alongside your teams.
Every product and every engagement runs on the Breww core. That means one data model, one guardrail engine, and one lineage graph — not four MLOps stacks reconciling every quarter.
A partner runs every engagement from kickoff through go-live. No pyramid. No juniors hidden behind slides. If we can't staff you with someone who has actually shipped in production, we say so upfront.
Model risk documentation, incident playbooks, retirement plans. Written before day one. Gated on production. This is the piece most vendors leave for later — and it's why most programs stall.
A boutique data-engineering firm serving global insurers, banks, and manufacturers. The foundation everything else stands on.
Sick of running a different MLOps stack per client, we built one. It works well enough on client three that we start productising it.
Lumaara, ESGCaffe, and CommandIQ come out on the Breww engine. Same guardrails. Same lineage. Same operator on the other end of the ticket.
The AI-native brand launches: four products, one platform, and a senior operator bench for the engagements that don't fit a product yet.
Tell us the problem you're trying to solve. A named senior partner replies within one business day — usually within four hours during working time. No sales team in between.
