About CausoAI
We make causal inference accessible.
Every analytics team deserves to know the true causes of their business outcomes — not just what correlated with what. We built CausoAI to make that possible, without requiring a background in causal inference.
Why we started it
Close the gap between data and decision.
Almost every company can tell you what happened last quarter. Very few can tell you what caused it. Those are different questions with different answers, and budgets are set on the second one whether or not anybody has actually answered it.
The gap is not a shortage of data or of tools. The methods that answer “why” have existed for decades and the software to run them is open source. What has been missing is the layer in between — something that applies those methods correctly, checks whether your data can actually support the conclusion, and explains the result to the person who has to make the call.
Without that layer, causal analysis stays locked with specialists, and everyone else keeps deciding on correlations that look convincing and are sometimes exactly backwards. That is the layer we are building.
Our Principles
Causation over correlation
Correlations mislead. Causal models make better decisions. We build tools that enforce this distinction.
Automated, not manual
Causal analysis shouldn’t require a PhD. Our platform handles method selection, assumption checking, and interpretation.
Explainable to executives
Statistical outputs only matter if decision-makers understand them. AI-generated plain-English summaries bridge that gap.
Rigorous by default
The readiness score makes it hard to run a bad analysis — and tells you exactly what to fix when something is wrong.
Where we are going
What the next year looks like.
CausoAI is in early access, which means the roadmap is still shaped by the people using it. Three things are set.
Going deeper, not wider
Marketing, sales and customer teams ask a small number of expensive questions over and over. The next year is about answering those specific questions really well — ready-made setups for attribution, discounting and churn — rather than spreading thin across every industry.
Shrinking the one manual step
Reviewing the causal diagram is the only part that needs a person, so it should take minutes. Better first drafts, clearer suggestions and business-language prompts instead of statistical ones.
Meeting your data where it lives
Today that means CSV files, PostgreSQL, MySQL and Google Sheets. Next it means the systems these teams actually work in — CRM, ad platforms and warehouses — so getting started stops being an export job.
A year from now, the test is simple: a marketing lead should be able to settle a budget question on Monday and defend the answer in Thursday’s review, without putting a ticket in with the data team.
Who is behind it
You should know who is handling your data.
Joona Rantanen
Founder — CausoAI
Helsinki, Finland
Joona has spent his career at the applied end of data science, including consulting work in telecom where the job was figuring out which commercial and sales actions actually moved the numbers, and which merely moved alongside them. More recently he has built machine learning models for customer churn — work that runs into the same wall from the other side: a model can tell you who is about to leave, but not what would keep them.
Asking those questions over and over, across telecom, public sector, healthcare and circular economy projects, is what led to CausoAI. He holds a Master’s in Machine Learning, Data Science and Artificial Intelligence from Aalto University, where he wrote his thesis on large language models.
Based in
Helsinki, Finland
Founded
2026
Stage
Early access
Legal entity
Luma Software Oy
Questions about the company, the platform or how your data is handled? Get in touch.