Treasure AI Implementation Partner

Treasure AI, the platform most teams still know as Treasure Data, is the most engineering-led CDP we work with. It gives you real control over storage, workflows and query logic, and it expects you to use it. That makes it powerful in the right hands and expensive in the wrong ones, which is largely why implementation partners exist for it.

What we run today

We lead Treasure AI implementation and support for Condé Nast across a multi-year engagement. That is the deepest platform bench we have, and it covers the full lifecycle rather than a launch: initial architecture, ongoing use case development, and the unglamorous run work of keeping pipelines healthy as upstream systems change around them.

The work is SQL, workflows and governance

A Treasure AI project is mostly three things, and none of them are configuration screens:

• Ingestion and workflows: scheduled jobs that land, transform and reconcile data from web, app, backend systems and partner feeds, with proper failure handling rather than silent gaps.
• The customer model: parent segments and unification logic that decide who counts as one customer. Get this wrong and every downstream audience inherits the error.
• Activation: pushing segments to ad platforms, email and internal systems, then proving that what arrived matches what was sent.

Cost is a design decision

Because you control how queries and workflows run, you also control what they cost. Inherited instances often have workflows running hourly that only need to run daily, or segments recomputed in full when an incremental refresh would do. We treat run cost as part of the architecture rather than something to discover on the invoice.

Where recovering conversions lost to ad blockers matters, we pair Treasure AI with Datafly Signal for first-party collection. For a broader view of how this fits a wider data programme, see our CDI/CDP implementation practice.