A bank. A financial institution in six countries. The broker insuring Mexico's airports. A university. A delivery platform. None of them bought the same thing. Every one builds on the same control plane, under routes they configured themselves.
A vendor ships a product and leaves. A control plane is what is still underneath in eighteen months, when what you built on it no longer resembles anything anyone sold you.
Bought for adoption.
Proven with the strictest buyers.
Some customers are named. Others are not. We treat their operational detail as theirs to disclose, not ours. When a case study is anonymized, the architecture, the configuration, and the outcome are fully described. The identity is what's withheld. Sophisticated enterprises recognize this as the same standard they hold their own vendors to.
Nobody audits marketing AI. They control it anyway.
Rappi automates marketing workflows on Saptiva AI, live in Mexico today. No regulator required this. No auditor set a deadline. Nobody was forcing the question at all.
Marketing runs on customer data, and Rappi's customers are in nine countries under nine privacy regimes. They chose the layer before the border problem arrived.
Six countries. One configuration.
MultiMoney operates across six countries. Six privacy regimes, and a book that has to move between them without crossing a line. KYC document readers, conversational agents, and credit origination, running under one configuration the group set once.
Put the group's stalled pilots into production.
3 weeks to 5 minutes.
Savant in production. Sector data, regulatory context, and the bank's own book, in one question. Financial data across industries, resolved before the meeting ends.
Mexico's largest private AI lab in higher education.
Selected over global solution partners on architecture and execution speed. A multi-year deployment building IBERO's institutional AI capability.
The broker insuring Mexico's operated airports.
Document processing and customer operations AI running in production. Saptiva Studio applications orchestrated through frIdA, deployed by an embedded Forward Deployed Engineer under strict residency and confidentiality constraints.
Every foundation above started as a first conversation. If you're accountable for an AI outcome at an enterprise, the next deployment that earns a page here is yours.