Every company is making decisions off dashboards. Very few have checked whether the numbers underneath them are actually right. We build the pipelines and the warehouse, and then we do the part almost everyone skips: we audit the data itself, so the reports your leadership steers by are reports you can actually trust.
Bad data doesn't look bad. It looks like a clean dashboard with a number on it. So teams present it, budget against it, and make real calls on it, never knowing that a campaign got double-counted, a month of revenue came through null, a platform quietly stopped reporting, or half the real spend lives in a spreadsheet nobody mentioned.
We've walked into warehouses with years of quiet integrity problems that nobody had caught, the kind that make a marketing ROAS number confidently wrong. The pipeline was "working." The dashboard rendered. The numbers were still lies.
We'd rather slow you down than let you walk into a board meeting with a number that turns out to be wrong. That's the whole job.
Two halves of one discipline: build the pipelines and warehouse right, then prove the data flowing through them is actually correct.
Designing and building the data warehouse on BigQuery, the connectors that feed it, and the transformation layer on top, so every source lands cleanly in one place your team can query with confidence.
The part almost everyone skips. Per-source completeness checks, gap and zero-day detection, double-count and duplication hunting, and reconciliation against the source systems, so you know the numbers are right before you bet on them.
Finding and fixing the fragile feeds, brittle dedup keys, timezone and off-by-one date bugs, missing validation, no error handling, that quietly corrupt a warehouse over time, and rebuilding them to run reliably.
Every company has them: the manual spreadsheets and side ledgers built to compensate for a warehouse people stopped trusting. We find them, fold them back into a single source of truth, and retire the duct tape.
Defining the metrics that matter, blended CAC, true revenue, ROAS, whatever the business runs on, so a number means the same thing to everyone who reads it, in dashboards built on data that's been verified.
An AI layer over your own warehouse, ask questions of your data in plain language and get answers, with data-quality checks built in so the model reasons over numbers that are actually correct, not confidently wrong.
Most data work stops at "the data is flowing." Ours starts there. We treat a warehouse the way a security team treats a network, assume something's wrong until we've proven it isn't, and go looking for the failures actively instead of waiting for a wrong number to surface in front of the board.
That auditing instinct is the difference between analytics that feel authoritative and analytics that are actually right. It's not glamorous work. It's the work that keeps a confidently-wrong number from costing you a quarter's budget.
The tooling to put AI on top of a data warehouse now exists and is largely commoditized, an LLM that speaks SQL, connectors that pull every source into one place, a model that can define a metric in seconds. The hard part was never the AI. It's whether the data underneath it can be trusted, because an AI reasoning over corrupt data just produces wrong answers faster and more convincingly.
That's exactly where we sit: the AI-driven analytics layer with data-integrity auditing built in, run by a team that catches the problems so you don't have to. The intelligence is only as good as the data. We own both.
A clean dashboard is not the same as a correct one. We build the pipeline, then we try to prove it wrong, so the numbers you steer by actually hold.
Tell us what your data stack looks like today. We'll tell you, honestly, where we'd look first and what it takes to trust it.
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