We are building the trust layer for AI data work.
Vuon exists because the bottleneck in analytics is no longer writing SQL. It is knowing whether the answer is grounded enough to act on.
Start from the decision
The product begins with the business question someone needs to answer.
Use trusted context
The agent works against warehouse data, definitions, docs, and code.
Ship a verifiable output
The result is an answer, report, or artifact that a team can act on.
Company belief
Trust precedes cleverness
Why Vuon
Legacy BI assumed analysts author and everyone else consumes. AI changes that boundary, but only if the generated work can be trusted.
For operators
Ask the business question directly and receive an answer that carries its source context.
For analysts
Define the data foundation, review high-stakes work, and scale expertise through agents.
For teams
Share published analysis that remains legible after it leaves the author.
What we refuse
Vuon is not a prettier dashboard grid and not a chat box that hides the work. The interface is built around deliverables and verification.
No black-box answers
Reasoning, SQL, definitions, and snapshots should be visible when they matter.
No dashboard as default
Answers, reports, and artifacts each fit different questions.
No analyst replacement theater
The system should amplify judgment, not pretend judgment is unnecessary.
How it works
A clear path from context to action.
The same operating rhythm runs through the product: gather trusted context, show the work, then ship an output that can be reviewed.
Start from the decision
The product begins with the business question someone needs to answer.
Use trusted context
The agent works against warehouse data, definitions, docs, and code.
Ship a verifiable output
The result is an answer, report, or artifact that a team can act on.
Next step