Context Warehouse

The problem

Your clients' data is scattered across ten platforms. One person runs Google, another runs Meta, the analytics guy never sends the report on time, and the answer to “how much did we spend on this client across all channels last quarter” takes half a day to assemble. Looker Studio connectors, Google Sheets, screenshots in slides.

And the agencies that do have a warehouse? We've never seen one that actually works. Naming conventions nobody respects, pipelines that broke two account migrations ago, historical data gone because the old ad account was disconnected.

Meanwhile the knowledge lives in your media buyers' heads: what was changed, what the client asked, why the strategy shifted. When one leaves, 75% of the account walks out the door with them.

Why your AI pilot failed

You tried AI. It gave you dumb recommendations. It wasn't the AI. It was the foundation: your data is scattered and your context is missing.

Here's the difference. CPA tripled last week. AI without context says: “lower your bids, broaden your targeting.” AI with context says: “conversion tracking was broken for three days. The spike is missing data, not performance. Ignore it.”

Same model. Same question. The second one is useful because it knows what happened on the account. That is what a context warehouse is for.

What we build

Valuable before the AI ever reads it

A context warehouse pays for itself with zero AI involved. A media buyer leaves: the account history stays. A new hire onboards onto an account in a day instead of a month. A client asks why you made that call last year: there's an answer. Your agency stops depending on what individual people happen to remember.

AI-readiness is the offense. Institutional memory is the defense. Same foundation.

What it doesn't do

The AI reads. It doesn't touch your accounts. No robot spending your clients' money, no automated campaign changes. It analyzes the data and the context, tells you what works, what dropped, what changed and why. Your team decides. Activation can come later, when you're ready. The foundation is the same either way.

Your data, your warehouse, your LLM

“Are the AI companies training on our data?” is the first question legal asks, and it's the right question. To be clear: we are not Anthropic or OpenAI. We don't train on your data and we don't store it. Everything lives in your warehouse, in your own cloud project. If you use a frontier API, you do it knowing that provider's terms.

And the model itself is your call. Copilot under the Microsoft license you already have, an open-source model on a private cloud, or a frontier API your legal team has cleared: all fine. The LLM is a commodity, swappable at any time. What makes any decent model do great work is the foundation underneath it: your data, your context, your operations registry. That part is yours too.

Engagement

This is an infrastructure build, not a SaaS subscription. We build it, your agency owns it: the warehouse, the data, the dashboard. Fixed-scope build, then an optional maintenance retainer to keep pipelines healthy and the context layer growing.

The build is scoped on a call: get in touch. How an engagement runs is on the process page.