Our approach
Every other tool asks you to climb up to it.
Ask a data question today and you're expected to arrive already knowing the shape of the answer — the right metric, the right table, the right framing. But real questions don't start there. They start as a hunch, or a worry, in plain words. BumbleB Crunch™ is built to start where you're standing.
The cost of curiosity
Every product team has questions it isn't asking. Not because nobody cares — because asking has a price. Configure the tracking. Build the dashboard. Queue the analyst. Wait for the report. By the time the answer lands, the meeting is over and the call was made on instinct.
So teams ration questions. And the ones that get cut are always the same ones — the vague, early, half-formed ones. The is something wrong here? questions. They're the hardest to justify taking to an analyst, and they're the ones most worth asking.
Because the two things that actually make someone open an analytics tool are curiosity and worry. Neither one arrives as a specification. You can't spec a hunch — and you shouldn't have to know the shape of the answer to be allowed to ask the question.
We make the cost of curiosity zero.
There are three ways to answer a product question
BI tells you what happened. Product analytics tells you what users did. Only one approach gets you to what to do next.
The old way
Dashboards and queries
Configure tracking. Build the chart. Queue the analyst. Wait for the report. By the time the answer arrives, the meeting is over and the decision is made on gut feel.
The translation way
An AI that turns your question into a query
Much faster than building a dashboard, and genuinely good when you already know what to ask for. But it needs a question that's already analyzable — and when the question gets hard, the reliable fix is having someone write the query in advance.
Our way
An agent that builds up to the answer
Ask Crunch the way you'd ask a colleague — half-formed, a little worried, in plain words. It works out the right question, then builds toward the answer one layer of reasoning at a time. From "what happened" all the way to "what should I do next." Grounded in your data. Nothing written in advance.
The intelligence is already there. Modern language models have absorbed an enormous amount of how good analysts think — millions of teardowns, retention analyses, business reviews. The hard part isn't teaching them to do analytics. It's building a product that lets them actually do it on your data, your questions, your way.
That's what we built.
How it works
Crunch builds the ladder as it climbs
Each rung is a layer of analyst reasoning — and each one holds the next.
The distance between a raw event table and why did retention drop after the pricing change is enormous. Most of that distance isn't querying. It's the concepts in between — the ones nobody wrote down.
So Crunch builds them, one level at a time. Raw events become sessions. Sessions become journeys. Journeys become cohort behavior. Cohort behavior becomes the retention story. Each level is a vocabulary the next one gets to reason in — so no single move ever has to span the whole gap.
The first rung is the biggest, and it happens before a single row is read: turning "is onboarding broken?" into the question that can actually be investigated. Every tool that starts at the data assumes you've already climbed that one yourself.
And the route isn't fixed in advance. Each rung gets chosen after the one below it came back, because what you just learned is what tells you where to look next. A path decided up front is a guess. A path built as you climb is a finding.
Every layer is checkable
You can verify one level on its own terms without holding the whole analysis in your head.
The reasoning stays simple
Each step operates on the right abstraction instead of wrestling raw rows, so no single move is a leap.
Errors surface where they happen
A layer that doesn't hold gets caught at that layer — not compounded quietly into a final number.
The answer is assembled
What you get is built from verified parts, not extracted from one opaque computation you have to trust whole.
What you can count on
An analyst that's available when you need it, shows its work, and gets better over time.
Takes the question you actually have
Vague, early, half-formed — that's a valid starting point, not a failure to specify. Working out the real question is the first rung, not a prerequisite.
Shows its work
Because the answer is built in layers, you can follow it layer by layer — and check any one of them without taking the rest on faith.
Won't make things up
When a layer doesn't hold, Crunch tells you instead of building on it anyway. A number it can't stand behind is a number it won't hand you.
Gets better as models do
The analytical thinking lives in the model, not in queries we hand-wrote. Every leap in capability arrives as better reasoning — with nothing to rebuild.
Won't make things up
The most valuable thing it does is say no
Push Crunch for a number the data can't support — a 12-month LTV, a CAC payback, a revenue reconciliation — and it declines, instead of inventing something plausible. That restraint is why you can put its answers in front of a board.
See it in action →Why this took until now
For most of the past decade, the only way to make a data system answer well was to teach it the answers in advance. Curate the metrics. Write the queries someone might need. Maintain the layer that maps business words to database columns. That was the right design for its time — the technology underneath couldn't carry the reasoning, so people had to.
But quality bought that way has to be bought again for every new question. It's labor, and it recurs. That's the real reason questions get rationed — and why the vague, early ones never make the cut. The cost of asking and the permission to ask turn out to be the same gate.
What changed is that the reasoning can now live in the model. Build for that from the ground up — rather than bolting it onto a system designed for the old constraint — and quality stops being something you hand-build one question at a time. It starts compounding.
That's the bet we made, and it's the part that doesn't ship in a weekend: knowing which layers to build, in what order, and what a good answer actually looks like when you get there.
See it for yourself
Ask Crunch a real question on real data — and watch it think.
See a demo Start free trial