You didn't get here because your analytics are broken.
You got here because your analytics look fine — and the problems keep happening anyway. The reporting disagreements. The attribution gaps. The ad spend that isn't producing. Those symptoms live in a layer below the platforms — in the events, identity logic, and consent state that everything else reads from.
These aren't tool problems. They're signal problems.
They live in the events, identity logic, and consent state that feed every platform you run. Buy a better tool and the problem moves with you. Fix the signal layer and every platform above it starts working the way it was designed to. That's the layer we work in.
Named by what you're up against — not by what we sell.
When your platforms tell different stories
Your analytics platform says one thing. Your ad platform says another. Finance has a third number. Every reporting cycle, someone has to explain the gap — and nobody can, because the disagreement isn't in the dashboards. It's in the events, identity logic, and consent state that feed all of them. When that layer is inconsistent, every platform inherits the inconsistency.
We design and build the signal layer your platforms read from — the event taxonomy, data layer specification, identity resolution logic, and consent state architecture. When this is right, the disagreements stop. Not because the dashboards got better. Because the underlying signals finally agree.
When your ad platforms are optimizing blind
Conversion API match rates eroding. iOS signal loss cutting into attribution. Ad blockers taking another slice. Your platforms are bidding on an increasingly incomplete picture of who's converting — and the algorithms learn from whatever signal they receive, accurate or not. The match rate improvement alone pays for the engagement within a quarter.
We architect and govern the server-side signal routing that gets your conversion data to ad platforms accurately — without exposing user data to third-party scripts, without consent compliance risk, and without the fragility of client-side tagging.
When your warehouse has data but nobody trusts it
You have Snowflake. You have dbt. You might have a data team. And when finance and marketing are in the same room, they're still working from different numbers. The problem is almost always upstream — the signals feeding the warehouse were never governed. Events named inconsistently. Revenue defined differently across systems. CRM data that doesn't reconcile with what product analytics records.
We build the warehouse truth layer: the data architecture and dbt models that reconcile your marketing data against your revenue systems. One set of numbers — governed, documented, and defensible to any stakeholder in the room, including the CFO.
When your stack has a compliance dimension
Healthcare organizations running analytics on patient-facing properties. Financial organizations operating under OSFI or cross-border US/Canada frameworks. Organizations where the compliance team has started asking questions about what the analytics stack is collecting — and nobody has a clean answer.
We design measurement architectures that are BAA-compatible, PHI-safe, and consent-aware from the start — not retrofitted after the fact. A stack that can survive an audit, doesn't create OCR or OSFI exposure, and doesn't collapse your signal quality the moment a consent banner is properly implemented.
When the architecture needs to stay accurate as you grow
An architecture that isn't actively governed drifts. New campaigns add new events. New platforms get connected. New team members make decisions without documentation. Within eighteen months, the architecture you built becomes the problem you're trying to solve again. The cost of that is usually invisible until it shows up in a reporting cycle where nothing agrees.
Your signal layer stays accurate as your stack and business evolve — monthly signal quality reviews, schema governance as new use cases are added, and a senior counterpart your data and marketing teams can reach when something needs an architecture decision, not a support ticket.
It starts with understanding your environment — and the same principal sees it through.
Every engagement begins with a Measurement Architecture Assessment: a priced, scoped diagnostic, not a discovery call. You get a detailed architecture document at the end regardless of what comes next — a clear picture of your signal architecture, where it's failing, and what it would take to fix it.
Everything that follows is scoped from what the assessment finds. The sequence, the priorities, the build — all of it comes from a documented understanding of your actual environment, not from a standard engagement template.
The principal who scopes the assessment delivers the work. No handoffs after the discovery call. The person with the context is the person doing the work.
Start with a Measurement Architecture Assessment →