Kapnova uses specialized agents to find opportunities across pricing, promotions, marketing, inventory and demand, then quantitative models determine the best decision.
AI finds the opportunities. Math computes the answer.
It is built around one decision universe: revenue, gross profit and contribution margin. Kapnova continuously looks for decisions that can materially move those outcomes and ignores the ones that don't.
Paid media, creators, email and a promotion all ran at the same time and revenue increased. Traditional reporting credits each one. Kapnova separates what each lever actually caused from what would have happened anyway.
Based on reported attribution, you would keep funding display. Kapnova shows the opposite: creators generated $470K through organic conversation that last-touch never credited, while display looked stronger than it really was. See the other decisions we run →
Specialized agents scan your business and outside market data to surface the decisions with the most revenue or profit at stake.
Causal inference, econometrics, forecasting, simulation and optimization determine what each decision is actually worth.
Kapnova recommends experiments, measures what happened and uses the results to improve the next decision.
We recommend. You decide. Nothing is executed on your behalf, and your raw data never leaves your tenant.
Most reporting optimizes toward what correlated hardest. Kapnova separates what actually caused the outcome from what simply moved alongside it, revealing where the real revenue and profit opportunity is.
Everything moved together, so the budget went to whatever correlated hardest.
Price moves revenue through sentiment. Promo caused none of the lift, so it leaves the model.
This is the simulator your team works in. Set the change you are considering and the model returns the effect, the interval around it, and the recommendation. On a decision this size, the gap between the two reads can be worth millions.
On your data, we test this first against a decision you’ve already made, so you can validate the result yourself.
Figures illustrative. See this run on your own decisions →
Four surfaces. Two for when you have a decision in front of you, two for the weeks in between.
Every decision and every outcome makes the next one sharper.
Kapnova’s causal engine grew out of MIT research studying whether treatments actually change health outcomes. We brought that same rigor to business to understand what truly drives an outcome, then model what happens when you change it.
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.