AI answers your business can stand behind.

Vuon checks AI-written queries against your business definitions and access policies before they run, and preserves the data and calculations behind each answer.

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A new standard for working with AI + data. Every answer verifiable, traceable, and trusted.

Built on approved logicVuon turns your context (data warehouse, docs, metric definitions) into trusted, reusable queries, so anyone can ask questions and get answers built on logic your team has already approved.
Traceable and reproducibleTrace any answer back to the data, definitions, SQL, and snapshots behind it, then rerun it anytime, so everyone can see exactly where a number came from and trust it.
Self-documentingVuon documents your warehouse as it works, so the context behind every answer stays current without anyone maintaining it.

Every query is checked before it runs.

People and agents can ask anything. Before a query reaches your warehouse or business tools, Vuon checks it against your access rules, your semantic layer, and your metric definitions. If it doesn’t match, Vuon sends it back with the reason, and the agent fixes it.

SALESIn SlackAGENTSOver MCPRevisingDATAIn VuonSNOWFLAKESNOWFLAKEHUBSPOTHUBSPOTBIGQUERYBIGQUERYHEXHEXVuonChecks every queryREADYCHECKINGSENT BACKWAITING ON AGENTAPPROVEDSecurity access checksSecurity access checksAdheres to your semantic layerAdheres to your semantic layerComplies with metric definitionsComplies with metric definitionsComplies with metric definitions-- waiting for the next querySELECT region, SUM(gross_amount)FROM fct_ordersWHERE quarter = 'Q3' GROUP BY 1SELECT region, SUM(net_revenue)FROM fct_ordersWHERE quarter = 'Q3' GROUP BY 1SELECT stage, COUNT(*) AS dealsFROM hubspot.dealsWHERE is_open GROUP BY stageSELECT plan, AVG(churned) AS churnFROM fct_subscriptionsWHERE day >= current_date - 90SELECT week, COUNT(DISTINCT user_id)FROM events.sessionsGROUP BY week ORDER BY weekWaiting for a queryFrom agents · Q3 revenueRevenue must use net_revenueReturned to the agent with the fixFrom agents · revised query→ Routing to SnowflakeFrom Head of Sales→ Routing to HubSpotFrom the data team→ Routing to BigQueryFrom agents · weekly actives→ Routing to HexSQLSQL v2SQLSQLSQLSQLSQL v2SQLSQLSQL

Right where general agents guess.

We asked the same analytics questions of an agent wired straight to the warehouse and the same agent working through Vuon. Vuon gets the answer right in the places agents most often go wrong.

Claude + warehouse MCPVuon
Semantic layer adherenceUses the dimensions, joins, and grain your layer defines
41%
100%
Metric populationApplies the right filters, exclusions, and denominators
38%
67%
Selecting the correct metricPicks the approved metric, not a lookalike column
52%
76%
Consistent across rerunsReturns the same answer when asked the same question
46%
80%

Same frontier model, warehouse, and questions in both setups. Figures are placeholders until the evaluation run is final.

Everything between your agents and your data.

One governed layer in front of your warehouse. It enforces your definitions, inherits your permissions, and records every query, no matter which agent is asking.

See how Vuon secures your data

Fits the stack you already run

Vuon sits between the agents your team already uses and the warehouse you trust. It reads definitions from your semantic layer, pulls context from your docs, and posts alerts where your team works.

VuonApplies your definitions

Works with the semantic layer you have

Vuon starts from the definitions your team already maintains, so there’s no second model to build or keep in sync.

One SQL layer for every agent

Claude, ChatGPT, your own agents, and the people on your team all reach the warehouse through the same checks. Connect over MCP or the CLI and switch models whenever you like.

VuonSame checks, any agent
agent:claudeSNOWFLAKE
Identityanalyst@acme.com
RoleFINANCE_READ
Row policyregion = 'EMEA'
Same access as in your warehouse

Your permissions, inherited

Every query runs with the identity and permissions set on your warehouse connection, including row-level policies where supported.

A record of every query

See who or what asked, which data it touched, and what Vuon approved, sent back, or denied.

Recording querieslive
  • 14:09:12agent:claudefct_ordersapproved
  • 14:09:07sarah@acme.comhubspot.dealsapproved
  • 14:08:55agent:internalfinance.payrolldenied · no access
  • 14:08:41agent:claudefct_orderssent back · wrong metric
  • 14:08:30data-teamfct_subscriptionsapproved
  • 14:08:12agent:chatgptevents.sessionsapproved
  • 14:07:58agent:claudedim_usersapproved
  • 14:07:40sarah@acme.comfct_ordersapproved
Queries & visualizationsRerun
  • Orders 29D12s
  • Refunds 29D3s
  • Net Revenue By Region<1s
  • Region Totals<1s
  • Net revenue by region
5 steps rerun on fresh data0 LLM tokens

Traceable and reproducible

See exactly how each calculation feeds a chart, then rerun the whole chain on fresh data without calling the model again.

Prove it on your own questions

Run your team’s real questions through Vuon and through a direct warehouse connection, side by side, before you commit.

YOUR QUESTIONSMCPVUON
Q3 net revenue by region
Churn by plan, last 90 days
Weekly active users
Open pipeline by stage
Trial-to-paid, September
Refund rate, EU only
ARR by segment
Dashboards created per user
Direct MCP 3/8Vuon 7/8
SOC 2 Type IIHIPAAGDPR

Enterprise-grade security

SOC 2 Type II certified, HIPAA and GDPR compliant. Queries run in your warehouse, and raw data stays in your environment.

fct_orders.net_revenueUpdated by Vuon 2h ago
owner: financeexcludes refunds

Self-documenting

Vuon documents your warehouse as it works, so the context behind every answer stays current without anyone maintaining it.

@Vuon why did pipeline drop?
Vuon
Open pipeline down 12% this week

Or use Vuon’s own agent

Ask in Slack and get alerts when the metrics you care about shift, built on the same checked queries.

    Our Story

    When ChatGPT was first released, our CEO Will built Stripe’s internal ChatGPT. But hooking it up to the data exposed a fundamental problem: AI can give confident answers about your data that are quietly wrong.

    Our CTO Forest has spent years building production software and doing hands-on data science work, including as an engineer on Stripe’s data science team. That experience drives his passion for data tools that are reliable, transparent, and easy to use.

    They built Vuon with a core belief: an AI answer is only useful if you can trust it.

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