You're flying blind between board meetings. Let's fix this.

Not a team — a person. Your most technical team member — an analyst, a marketing-ops lead, even a technically confident marketing manager — connects the warehouse (BigQuery has a generous free tier), picks tables, one-click populates the Data Mart library, and publishes it, usually in an afternoon. From that point on, everyone in the company self-serves: they ask their business a question in their own AI chat — Claude, ChatGPT, or Slack — or pull the numbers in Google Sheets. No new tool to learn. If you have zero technical people, we'll walk you through setup in a 30-minute demo — but if your company uses Google Sheets and runs ad campaigns, someone on your team can handle this.

This is the core difference between OWOX and every "AI analytics" tool your team has been pasting into Slack. When someone asks ChatGPT "what's our ROAS?", the LLM writes a query on the fly — different every time, potentially wrong, impossible to verify. OWOX works the opposite way: your analyst defines the SQL, approves the logic, and publishes it as a Data Mart. When you ask a question in your own AI chat, every number in the answer is the result of that pre-approved SQL running against your real warehouse data — the AI narrates, but it never writes the query or invents a join. The numbers are deterministic: same question, same result, every time. Patented technology. Every number traces back to SQL your team approved.

OWOX starts at $65/mo . That includes connectors (pull ad-platform data into your warehouse), Data Mart management (govern and join your data), and the Google Sheets Extension. For context: a freelance analyst charges $75–150/hour; a full-time analyst is $80–120K/year plus three months to ramp. OWOX scales to every team member for less than a business lunch. Higher tiers add MCP — ask your business in Claude, ChatGPT, or Slack — plus multi-destination delivery and SLA. Enterprise is custom.

Because ChatGPT makes up numbers and your marketing lead can't tell the difference. It writes SQL on the fly, joins tables it shouldn't, hallucinates columns that don't exist, and gives a different answer every time. One wrong number in a board deck is all it takes. OWOX gives your marketing lead the same ask-in-your-AI-chat experience — that's exactly what MCP is — but the answer comes from governed Data Marts with analyst-approved SQL. Every cell is deterministic, every join follows pre-defined keys, no hallucinations, same answer every time. Your marketing lead gets speed; you get trust.

Your first Data Mart takes minutes. A useful library covering your core metrics — revenue, CAC, pipeline, campaign performance — can be built in a day or two. Your team sees the library in Google Sheets immediately after publishing, and you can ask your first question in your own AI chat the same day. This isn't a six-month project — no implementation phase, no professional-services engagement, no migration. Connect the warehouse, build the library, publish it. Done.

You need a warehouse, but getting one is easier than you think. Google BigQuery has a free tier that covers most small-company volumes — 10GB storage, 1TB queries/month, no credit card. Your marketing-ops person or a freelance analyst can set it up in an afternoon, and OWOX connectors pull your ad-platform data in automatically. Already on Snowflake, Databricks, Redshift, or Athena? OWOX connects to all of them. Your data stays in your warehouse — OWOX reads from it, never copies it — so nothing you ask ever leaves your control.