Always-On Marketing Science.
Calibrated continuously.

Your MMM is one method in a continuously running loop. Kuwalyst runs the loop (lift calibration, decision steering, ongoing recalibration) without replacing your model. If your MMM ships quarterly and your decisions ship monthly, that gap is what Kuwalyst closes.

MMM is only one brick in your marketing strategy, aimed to measure marketing channel performance

In order to make the right decisions for your marketing strategy, you need a unified measurement approach, where models need to be adjusted continuously to reflect the reality of your marketing performance.

What if a certain event becomes a confounder for your models? What if the customer profile you are targeting changes?

Kuwalyst is designed to capture this.

Watch the video below to see how Kuwalyst uses lift experiments to calibrate an MMM.

Kuwalyst is for teams that have scientists. Your scientists keep ownership of methodology, priors, and final calls. Kuwalyst handles the operating layer (continuous calibration, experiment design, scenario steering) that most in-house teams don't have the cycles to build themselves.

When working on a hypothesis or a model, marketing scientists come with different startegies and ideas they want to test, all of them needing to be validated by offline and online experiments.

Kuwalyst LLMs have been trained to think like a marketing scientist and explore all the most promising strategies simultaneously.

Consulting projects deliver a slide deck. Kuwalyst delivers a system that keeps running after the engagement. If you've ever watched a six-figure audit gather dust, you know the difference matters.

You can. Claude Code can scaffold an MMM in PyMC-Marketing/Meridian/Robyn in a weekend. What it can't do, yet, is run the closed loop continuously: calibration against new lift tests, automated experiment design, scenario forecasting against the live model, all on a recurring cadence. The loop is what Kuwalyst is.

We benchmarked the creation of a Marketing Mix Model with synthetic data reflective of a mid-sized European retail brand advertising across 7 countries.

Using Claude Opus 4.6 extended thinking mode, ROAS estimations were off by a magnitude of 10x

With the same data, Kuwalyst was able to get very close to ground truth ROAS, except for TikTok channel where it was off by a factor of 2x

This was made possible thanks to LLMs trained to iterate over models and improving them incrementally

Kickoff call. Connector setup for sales, advertising, and customer data. A first-pass review of what you have and what's underused. By end of week one, Kuwalyst is ingesting your data; by end of week two, you have early findings on data gaps and integration issues.

€40k per year (post-pilot exclusive price). Optional, post-pilot. You decide at the end of the pilot whether to continue, based on whether the platform delivered. No multi-year contracts. No platform lock-in. The pilot stands on its own.

Tactical signals from week one: campaign hygiene, spend pacing, anomalies. The full loop (calibrated models, lift-integrated, scenario-ready) takes the four months of the pilot to land properly, because it requires running at least one experiment to ground-truth the science.

Please check more details on our pilot page.

Hosted in France, on Scaleway. No calls to closed-API LLM providers (Anthropic, OpenAI). All inference runs on open-weights models, on EU infrastructure. VPC or on-premises deployment available for stricter requirements. Your data does not leave your infrastructure.