Let the data talk.
Point at a metric. WHYSE searches the whole dimensional space and ranks what actually explains the move — no hypothesis, no SQL, no waiting on the queue.
The whole argument, in three minutes.
Why dashboards stop at what, why SQL cannot get you to why, and what the engine does instead.
NO SQL · NO HYPOTHESIS · REAL PRODUCT FOOTAGE
Dashboards tell you what happened. Nobody tells you why.
And when someone finally does, the room argues about whether the number is right.
Curiosity has a price tag
Every new question is another full scan and more analyst hours. The more you want to know, the more it costs to ask.
Dashboards show the move, not the cause
A chart tells you revenue fell. It will not tell you which slice took it down.
Nobody trusts the answer
Three analysts produce three numbers and the percentages add up to 107%. That is data paranoia, and more analysts make it worse, not better.
SQL is a retrieval language. It was never going to discover anything.
Answer the question you already thought to ask.
Searches every segment and finds the one you didn't.
The last frontier of data science.
From raw tables to ranked answers.
Build the cube once
One pre-aggregation pass turns your warehouse into a cube of mergeable sketches. Every segment's stats become a lookup, not a scan.
Ask in plain English
The model reads your schema, not your rows, and turns your sentence into a structured request. That request is shown back to you before anything runs.
Get a ranked, traceable answer
A deterministic engine ranks every segment by how much it explains. Contributions sum to 100%, and the result is an if-then rule you can take into the room.
Same question, same data, same answer — every time.
Six questions the engine answers without a query.
What moved the number
Ranks which segments drive a KPI and its change, in order of how much they explain.
Where it's breaking
Finds the slices performing below their own baseline, not just below average.
What's missing or over-supplied
Separates a shortfall from a surplus and attributes each to the segments responsible.
What travels together
Surfaces feature combinations that co-occur — and the ones conspicuously missing.
What changed shape
Flags segments whose distribution shifted, even when the average held steady.
What predicts the outcome
Learns which attributes predict churn, default or conversion, as rules you can read.
Drill-down, slicing and filtering are built in — same engine, no extra setup.
The answer arrives while the decision is still open.
The engine found the COVID cliff in NYC taxi data without being told to look
“Observed 1,428 against a forecast of 2,114 — well outside the confidence interval, on a day nobody had flagged.”
Read the investigationProfiled on 100M+ records; formal benchmark in progress. Other figures are representative of internal runs, not customer results.
Two more investigations, including a fully public dataset: EIA Crude Production · DriveU
Three ways in. Zero rewrites.
MCP server
Your agent calls each capability as a named tool. We host; no infrastructure on your side.
Hosted service
We run the engine, you call a REST API. Fastest path to production.
Embedded library
Install as a dependency. Runs on your infrastructure; your data never leaves it.
Every answer ships as structured, explainable evidence your own agents can ground on — not just prose.
Auditable by design.
The engine computes over pre-aggregated cube cells — ranked segments and schema reach the model, not raw fact rows.
The same question returns the same answer, and the path to it is auditable.
Runs on commodity hardware. Embedded deployment keeps everything in your environment.
The questions your data scientist will ask.
Ask your first why.
Bring one question you've been putting off. We'll build the cube and answer it on the call.