Kapnova combines your data, outside signals, business rules, economics and operating constraints to find the decisions that matter, then uses purpose-built quantitative models to estimate what each one is worth.
One engine. Every decision makes the next one better.
A pricing, promotion, loyalty, or media question, answered with the drivers, the confidence, and every number traceable to a source.
The action, the dollar impact against the correlational read, the confidence, the one thing that would change the answer, and how much each driver contributed.
Every call logged with its prediction and, when the outcome lands, captured. A running total of dollars gained or protected against the correlational read, your number to carry into the budget conversation.
| Decision | Call | Outcome | vs correlation |
|---|---|---|---|
| Hydra-Glow +8% | Hold | Held | +$1.6M |
| Q3 creator push | Scale | Scaled | +$0.9M |
| Loyalty tier expansion | Narrow | Narrowed | +$0.2M |
| Spring promo cadence | Pull back | Pending | tracking |
It tracks the drivers and the sentiment signal and flags when the picture shifts, sentiment souring ahead of transactions, so you can revisit a call before it reaches the P&L.
Every driver quantified. Every answer traceable to the data, method and assumptions behind it. And when the evidence isn’t strong enough, Kapnova tells you what is missing instead of pretending to know.
Kapnova transforms your existing data so it can separate what actually drove an outcome from what simply moved with it. That foundation makes every decision, scenario and recommendation more defensible.
Kapnova continuously brings in sentiment, reviews, search, competitors and market signals, then models how they influence your outcomes and when.
You don’t have to provide it. We bring the outside world into the model.
Sentiment is not a static input. Your decisions move it and it moves revenue back, so which way the arrow runs is itself a causal question. The same variable is a mediator in one decision, a confounder in another, a leading indicator in a third. Collapsing that into one score is the mistake we do not make.
Souring sentiment is driving a real decline. This is a signal to act on.
A price or product change caused both the decline and the souring. The sentiment is an echo.
Seven steps run on every question, and they run without anyone starting them. The second one is the one that matters, and it is the step a dashboard, an attribution tool and a language model all skip.
Working out what caused what used to take a scarce senior specialist weeks per question, built from scratch every time. Agents pre-trained by our MIT team now do that work in hours, at a scale that used to need a room of PhDs.
It reads the signal and takes your question. It never invents the answer.
It runs the identified methods and produces the effect, every step auditable.
Which assumption to lean on, and where it breaks. This stays with you.
The optimization engine searches your real decision space, budget, margin and inventory included, for the move that maximizes the goal you set, not the metric that looks good this week. As outcomes come back it learns which actions actually caused them, and updates the policy across the whole organization. It starts from calibrated category priors and sharpens into your own numbers with every decision logged.
When a machine makes the call, what sits beneath it decides whether the call can be trusted. A probabilistic language model is the wrong thing to put there: it reasons from correlation and repeats it confidently, without sources, on exactly the highest-stakes calls agents get handed. Those decisions need a causal layer underneath.
Start with your URL and Kapnova will surface what it can find from public data alone. Then connect your own data to measure what past decisions actually caused and where the next revenue or profit opportunity may be.