The AI that does
the work.

Ask a question and it builds the dashboard. Describe a problem and it runs the analysis. Tell it your weekly process and it handles that too. Work that used to take days happens in a single conversation.

app.switchboard.build
Documents
Marketing
Channel Attribution
Weekly Performance
CORE
Executive Dashboard
CAC Analysis
▾ Q1 2026 ▾ All Channels
CAC by Channel Q1 2026
Meta Google Email Organic
Operator
What's our CAC by channel for this quarter?
Ran Execute Sql
Here's your acquisition cost breakdown for Q1:

Meta: $48  |  Google: $52  |  Email: $31  |  Organic: $19

Email and organic are your most efficient channels. Meta and Google are similar cost but serve different audiences.
That Meta number feels high. What's driving it?
Ran Execute Sql
Breaking it down by campaign type:

Broad awareness: $72 CAC  |  Retargeting: $31 CAC

Your broad prospecting campaigns are pulling the average up. The retargeting is actually performing well.
How does that compare to Q4?
Ask Operator anything...

The analysis that takes a team days. Done in a thread.

Ask a question. Get a real answer, not a chart you have to interpret yourself. Operator breaks down the numbers, isolates what changed, and connects data across every source you have. One conversation. No waiting on a ticket. No repeating yourself next time.

Operator
That Meta number feels high. What's driving it?
Ran Execute Sql
Breaking it down by campaign type:

Broad awareness: $72 CAC  |  Retargeting: $31 CAC

Your broad prospecting campaigns are pulling the average up. The retargeting is actually performing well.
How does that compare to Q4?
Ran Execute Sql
Meta was $41 in Q4. The $7 increase is almost entirely from the new prospecting campaigns you launched in January. If you strip those out, your core Meta CAC is flat quarter-over-quarter.
app.switchboard.build
Acquisition Performance
▾ Rolling 4 weeks ▾ All channels
CAC — Meta
$48 +17%
First-Order AOV
$287 +4%
6-Month LTV
$412 +12%
CAC Trend by Channel Last 4 weeks
W1 W2 W3 W4
Operator
Can you build me a dashboard with CAC by channel, first-order AOV, and 6-month LTV — updated every Monday?
Created Dashboard
Done. "Acquisition Performance" is in your Marketing folder.

It includes: CAC trend by channel (rolling 4 weeks), first-order AOV by source, 6-month LTV by acquisition cohort.

Refreshes every Monday at 6am. I'll flag you if CAC on any channel spikes more than 20% week-over-week.
Ask Operator anything...

Describe a dashboard. Watch it get built.

Operator creates dashboards, scorecards, and reports mid-conversation. They live in your workspace, refresh on a schedule, and alert you when something changes. Need to tweak it? Just say so.

Runs your weekly work.

Operator doesn't just answer questions. It runs the processes you repeat every week. Fulfillment plans. Inventory allocations. Order prioritization. Ask for it once, get back a spreadsheet you can act on.

Operator
What does the fulfillment schedule look like for next week?
Ran Execute Sql
Checked Inventory
120 orders pending. 89 can ship same-day. 31 need restock — I've flagged those and built a prioritized fulfillment plan.

Want me to export it as a spreadsheet?
Yes, export it.

Gets smarter about your business.

Most AI tools forget everything between sessions. Operator learns how your business works, how you like your data, and what your weekly process looks like. Every conversation makes the next one faster.

Business context
"LSA spend is determined by campaign name in the ad spend tables."

Operator remembers how your business works — the definitions, the edge cases, the things you'd normally have to re-explain every time.

Preferences
"I prefer bar charts for channel comparisons."

How you like your data presented, which metrics you care about, which breakdowns matter most. Ask once and it sticks.

Processes
"Every Monday, pull the weekly shipping plan in this format."

Teach Operator a workflow once and it runs it the same way next time. No SOPs to write, no tickets to file.

ChatGPT can help you think through a data problem in the abstract. Operator knows your actual numbers, your actual definitions, and your actual processes — and it remembers them next week.

The answers are right because the data is right.

Point an AI at raw data and it guesses. Sometimes it's right. Often it's not. Our team builds the data models underneath Operator — how your metrics are defined, how your tables connect, what your business terms mean. These models are built by experienced data engineers who've done this across dozens of companies, not auto-generated from your raw tables. Operator queries that, not a pile of raw data.

The difference isn't the AI. It's what's underneath it.

Every step is visible. Operator shows what it queried and how it got to its answer. If something looks off, you fix it in the conversation.

Human-built models
Transparent reasoning
Approval workflows
We asked how the AI knows where our ad spend data comes from. The team had already stored that context, so every time we ask about spend, it pulls from the right source automatically.
Context stored, answers accurate
Marketing Director, Home Services

Built for the people running the business.

Marketing Director
Which campaigns are driving the most efficient new customer growth?
Ran Execute Sql
Email nurture sequences: $31 CAC, highest 30-day LTV. Meta prospecting: 3x more expensive but acquires higher-AOV customers. The trade-off depends on your LTV target.
Build me a weekly attribution report.
CAC diagnostics, channel performance, attribution analysis.
Ops Manager
What does the fulfillment schedule look like for next week?
Ran Execute Sql
Checked Inventory
120 orders pending. 89 can ship same-day. 31 need restock — I've flagged those and built a prioritized fulfillment plan. Want me to export it as a spreadsheet?
Yes, export it.
Fulfillment planning, inventory optimization, weekly schedules.
Finance Lead
Walk me through gross margin by product line this quarter.
Ran Execute Sql
Supplements: 68%. Apparel: 44%. Accessories: 52%. Supplements are up 4 points from Q3 — lower COGS from your new contract manufacturer.
Show me the COGS breakdown for supplements.
Gross margin modeling, COGS visualization, revenue breakdowns.
E-commerce Manager
Why did subscription churn spike in February?
Ran Cohort Query
February cohorts have 23% higher 90-day churn than January. The spike is concentrated in customers who received your free-gift-with-purchase promotion — they're churning after gift fulfillment.
How much revenue was at risk from that promo?
Subscription churn, discount lift analysis, LTV modeling.

How accurate are Operator's answers?

Accuracy depends on the quality of the data model underneath. Ours is built and maintained by our team — not auto-generated. Operator queries that model, not raw tables. If an answer looks wrong, you can ask Operator to show its work and trace it back.

What data sources does Operator work with?

We connect 200+ sources — Shopify, ad platforms, subscription tools, CRMs, and more. Our team handles the connection and keeps it running.

Can Operator change my data or data models?

Yes, with your approval. When Operator proposes a model change, it shows you the plan first and waits for your sign-off before anything happens. Nothing changes without your sign-off.

Is my data shared with other customers?

No. Operator learns about your business from your data. Each customer's context is completely separate. We don't use your conversations to train or improve responses for anyone else.

How long before Operator is actually useful?

Days, not months. Our team handles onboarding — we connect your sources, build the initial data models, and set up your first dashboards. You're asking real questions about your business within the first week.

How is this different from asking ChatGPT about my data?

ChatGPT doesn't have your data. It can help you think through problems in the abstract. Operator is connected to your actual Shopify store, your actual ad spend, your actual subscription metrics — right now. The answers are about your business, not a hypothetical one.

See Operator in action.

Book a 15-minute demo on your actual data, not a slide deck.