BumbleB Crunch™
The AI analyst that investigates.
Point it at any data — your website, your Shopify store, the files you export, your own database. Crunch works out the real question, plans the analysis, runs it, and shows how it read you and how every number was counted. It goes deep on clickstream — what people actually do on your site and in your product — and joins it with your other datasets.
Running ads to a website? See which spend actually comes back →
Live — “We spent the same on our Google and Meta campaigns. How many sign-ups did each bring in, what was our cost per sign-up on each — and is the cheaper one the one that looked cheaper per click?”
Any data, same reasoning
Three kinds of data. One analyst.
Ad spend joined to website behavior. A product funnel. An orders table with no tracking at all. Every clip is one plain-English question and the investigation Crunch runs to answer it.
The spend that comes back
“We spent the same on our Google and Meta campaigns. How many sign-ups did each bring in, what was our cost per sign-up on each — and is the cheaper one the one that looked cheaper per click?”
Ad spend joined to website behavior: $12,000 each, but Google brought 960 sign-ups to Meta's 480 — $12.50 a sign-up against $25.00. Meta looked cheaper per click ($0.75 vs $2.00); the cheap clicks were the expensive sign-ups.
The leak under the flat line
“Walk me through our funnel, from searches to viewing a listing to contacting an agent. Which step loses the most people?”
Of 20,000 searchers, 16,000 view a listing but only 3,700 contact an agent. The leak isn't at the top — 12,300 are lost between the listing and the contact.
The flat quarter, split
“Revenue's flat quarter over quarter. Overall, what changed underneath — the number of orders or their size — and in which region is the order size moving?”
An orders table, no tracking involved: orders rose from 8,000 to 8,800 while the average order shrank — and it's all the West, down $40 an order while every other region rose.
Why it's different
Analysis isn’t translation. It’s investigation.
Most AI analytics turns your question into a query. That works when you already know exactly what to ask. Real questions rarely arrive that way.
The old way
Dashboards and queries
Build the chart, queue the analyst, wait for the report. By the time the answer lands, the decision was made on gut feel.
The translation way
An AI that turns your question into a query
Fast, and genuinely good when the question is already analyzable. When it gets hard, the reliable fix is someone writing the query in advance.
Crunch
An analyst that builds up to the answer
Ask half-formed, in plain words. Crunch works out the real question, plans the analysis, runs it step by step across whatever datasets it needs — and shows its working.
What you can point it at
Broad enough for any dataset. Deep where behavior lives.
Any dataset
Your own database
Sync it with bb-connector — every field anonymized inside your network before it leaves. Orders, listings, operations — ask about it the way you’d ask a colleague.
Behavior, natively
What people actually do
Crunch reads clickstream the way an analyst does — visits, funnels, sources, pages, repeat behavior — so drop-offs and channel quality come back as findings, not raw counts.
Combine them
Behavior meets the business
Put two datasets side by side and ask across them. Start with ad spend and website behavior: which campaigns bring people who actually sign up, and what each one really costs.
An answer you can check
Every answer shows how it was counted
Crunch says how it read your question — “sign-ups” as form submissions, “all the way down” as a 75% scroll — and spells out the window, the unit and the denominator behind every number. You check the reasoning, not just the result.
Live — “Of the visits that read a page all the way down, how many get in touch — and where do we lose the most?”
Takes the question you have
Vague and half-formed is a valid starting point. Working out the real question is the first step, not your homework.
Says how it read you
Plain words get an explicit reading — “hold attention” as read-through, “sign-ups” as form submissions — so you can correct it in one line.
Shows how it counted
Date window, what counts as a visit or a user, which denominator a rate uses — spelled out under every answer.
Works across datasets
Clickstream next to ad spend, orders or CRM data — ask one question across them instead of stitching reports.
Who it's for
Analysis for teams that can’t staff an analyst
Solo founders to growing teams — anyone with data and a question, and no one to hand it to.
Founder / CEO
“Is our growth real, and where is it leaking?” used to die for lack of time. Now it’s one conversation, answered in minutes — before the board meeting.
Marketing lead
Export last month’s ad spend, ask which campaigns brought people who actually signed up — cost per real sign-up, not cost per click.
VP of Product
Activation moved on Friday, the review is Thursday. Ask, follow the thread, have the answer before standup — no data-team ticket.
Ops & business teams
Orders, listings, operations tables. Ask why the quarter looks flat and get volume, mix and region separated — without writing SQL.
Get started
Connect your data from the hub
Everything lands in one place, so you can ask across all of it.
Google Tag Manager
Your website or web app: one Custom HTML tag in your GTM container.
Shopify pixel
Your store: a custom pixel under Customer events captures views, carts and checkouts.
File upload
Excel, CSV or Parquet: ad-spend exports, Google reviews, order lists, anything with rows.
Your database or data lake
On Team: sync from behind your firewall with bb-connector, or bring your own data lake.
Latest from the Blog
A Question Isn't a Query
Every AI tool takes plain English now. That fixed the wording of a question. It left the harder part, working out what the question actually is, exactly where it was.
October 4, 2026You Do Not Need to Read SQL to Catch a Wrong Number
Checking an AI's analysis was supposed to require an analyst. Most wrong analyses are not wrong analytically — they are wrong about the business, which is the one thing the room already knows.
September 6, 2026The Answer Got Cheap. Checking It Didn't.
The cost of producing an answer collapsed. The cost of checking one didn't. In data analysis that gap is wider than it looks — because code fails loudly and analysis fails quietly.
July 21, 2026Ready to ask your first question?
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