FOR TEAMS ALREADY USING AI
Your team is already making decisions on unvalidated AI reports.
We handle the guardrails. One metric glossary, validated reports, and a home where they live and refresh.
You keep the speed your team just discovered.
Months → Minutes
That's what happened to report turnaround when AI showed up. Nobody is checking the answers. That's the gap we close.
Free call. You'll leave with a read on your reporting gaps either way.
What actually changes
Your reporting today
- Everyone self-serves reports in Claude, and everyone defines revenue a little differently
- Finished reports are orphaned HTML files with nowhere to live, refresh, or be shared
- Nobody can say which numbers are validated and which are vibes
- Your actual data team is underwater, and the request backlog is measured in months
Your reporting after
- One glossary. When anyone reports AOV, it means AOV
- Every AI answer runs through your strategy docs and definitions first
- Reports live in one place, refreshable and shared, not in a downloads folder
- A publishing tier system, so speed stays and the wrong number never reaches the board
AI reporting isn't the problem. Ungoverned AI reporting is.
You can't take self-serve away from your team now. There would be a revolt. The move is guardrails, not rollback.
Free call. You'll leave with a read on your reporting gaps either way.
What we keep hearing from operators
- A new report used to mean a multi-month backlog for the data team. Now anyone builds one in an afternoon. Nobody knows which ones to trust.
- The AI creates an incredible report, then hands you an HTML file. Go nuts. It has nowhere to live, no way to refresh, no way to share.
- Ask five people what revenue means and you get five answers. Decisions are being made on all five.
Everything inside the System
Six pieces. One outcome. Self-serve reporting your whole company can actually trust.
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01
The Metric Glossary
Every metric your business runs on, defined once, org-wide. Does revenue include cancellations? Does AOV include sales tax? Answered one time, inherited by every report and every AI answer after that.
The single piece operators tell us would fix the most
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02
Docs-First AI Access
Connect Claude, Cursor, or whatever your team already uses through our MCP. Every question routes through your strategy docs and definitions before it touches the data.
Context your team stops re-explaining in every chat
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03
A Home for Reports
Living, refreshable, shareable reports in one UI, with AI chat that is contextual to the page you're looking at.
Replaces the report graveyard on everyone's desktop
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04
Publishing Guardrails
A free-publish space so nobody loses speed, and a validated tier our team stamps before a report rolls out to everybody.
The stamping system every ops lead wishes they had
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05
Your Warehouse, Not Ours
We sync your sources into BigQuery inside YOUR account. The system sits on top of your infrastructure, and usually replaces what you pay for pipelines today.
Your data stays yours. No new silo
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06
A Human on Call
This is not "here's your SaaS tool, good luck." You get a point of contact who hooks up new sources, troubleshoots, and sanity-checks outputs when something looks off.
The outsourced data team behind the software
How it works
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01
Map and sync
We audit your sources, sync everything into BigQuery in your account, and sit with your team to write the metric glossary. The definitions arguments happen once, here.
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02
Wire the guardrails
Strategy docs, definitions, and the MCP go live. Your team connects the AI tools they already use, and every answer starts flowing through your context first.
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03
Your team self-serves
Anyone builds reports. The ones that matter get validated, stamped, and live where the whole company can see and refresh them.
Questions
What does it cost?
A flat onboarding fee plus a flat monthly. No usage-based pricing. We're not going to make you monitor which employees are asking too many questions. If you'd rather carry the AI usage on your own bill, bring your own Anthropic key and we'll wire it in.
We already have a data team and a warehouse. Does this still fit?
Yes, and honestly that's where the pain is loudest. Your data team keeps the core architecture and the board-level reporting. This system handles the long tail of self-serve requests that were sitting in their backlog for months.
We're on Snowflake, not BigQuery.
We build on Google Cloud, so BigQuery is our home turf. If you're committed to Snowflake, book the call anyway and we'll give you an honest read on whether the fit is there. We'd rather tell you no than sell you a bad setup.
What's an MCP, in plain English?
A secure connection that lets the AI tools your team already uses (Claude, Cursor) read your actual data, your metric definitions, and your existing reports. Instead of a chatbot that guesses, you get answers built from your source of truth, with your rules applied first.
Do we have to use your chat interface?
No. Most teams keep using Claude and just connect through us. Some teams want a scoped chat inside the reporting UI so people know exactly what it can see. We can do either. Same experience, your call.
What happens after I book?
A 30-minute call with the Vision Labs team. We'll map your current reporting reality, your stack, and where the trust gaps are. If it's a fit, we'll scope the build. If not, you leave with a plan anyway.
See what guardrails look like on your stack.
Free call. You'll leave with a read on your reporting gaps either way.