WHYSE
Perplexity for data science and analysis.
Powerful proprietary algorithms at scale.
Deep insights on big data in under 7 minutes.
The problem
Analytics stops at the report. The moment a metric moves, teams fall back to guesswork and endless slicing — never quite trusting the number in front of them. That's data paranoia.
Our motivation
Eliminate data paranoia. Every answer is traceable to the exact rows that produced it — no black box, no guesswork. People finally trust what they see.
Every number maps back to the source rows.
The same cut gives the same answer, every time.
No paranoia — the data speaks for itself.
Versus Claude, ChatGPT & text-to-SQL
Text-to-SQL
Generates SQL from natural language. Still bound by what SQL can express — and still needs you to know what to ask.
WHYSE
Converts text to parameters, then calls purpose-built algorithms on the OLAP table. No code generation, no correction loop.
Text-to-SQL
Orchestrators generate, test, and fix SQL in a loop. Expensive, and accuracy still isn't guaranteed.
WHYSE
Known algorithms, known outputs. Cheaper, more accurate, no iteration.
Text-to-SQL
You choose the dimensions to slice by. If you don't ask the right question, you don't get the answer.
WHYSE
Point at a metric. We find which dimensions explain the move — no hypothesis needed.
The case
Not one feature — a system. The engine, the explainable artifacts, the flexibility for your experts, and where it all lands in planning.
What you'd be licensing
Drill-down, slicing, and filtering are built in — all on the same engine.
Explainability layer
WHYSE emits structured, explainable artifacts — the drivers, the evidence, the decomposition. Your LLMs and agents consume them to summarise complex situations quickly, simply, and accurately, without inventing the details.
Machine-readable evidence, not prose.
Human- and agent-legible by design.
The grounding a raw LLM lacks.
Who operates it
WHYSE lets the data talk automatically — and hands the wheel to your subject-matter experts when they want it. Analysts can steer, constrain, and extend the analysis without giving up correctness.
Point it at the data. It surfaces the drivers with zero setup — the data talks on its own.
Analysts steer the search and add domain knowledge — with the same 100% numeric accountability.
Where it lands
Turn a metric movement into a clear, evidence-backed narrative — then set and pressure-test objectives against what the data actually supports.
A metric shift becomes a narrative anyone in the room can follow and trust.
Objectives tested against the evidence, not a hopeful guess.
Integration
Pick whichever fits your architecture. All three are the same engine underneath.
SaaS-based. Your agent calls each capability as a named tool. We host — zero infrastructure on your side.
We run the engine, you call a REST API. Fastest path to production.
Install as a dependency. Runs on your infrastructure — your data never leaves.
Proof of work
Same engine, no retuning between them. Nothing in it is domain-specific.
"How did fare contribution shift between January and February?"
"Which states and fuel types drove the Q3 production variance?"
"Which booking type and zone shows the most anomalous trip outcomes?"
Team