🆕 Skore is live! Errors prevented, not discovered. See it on your own data →
The Tabular AI Company
Stewards of
Scikit-Learn.
Top contributor to TabICL.
Over 200M downloads
every month.
Any AI provider, any ML framework, any compute.
Trusted global partner.
Data science is being rewritten by agents faster than the discipline can absorb it.
AI accelerates data science, but it can’t replace human judgment. For the business, that difference determines what you can explain, forecast, control, and protect.
Decisions taken on models nobody can explain.
More decisions are taken on AI-generated models nobody can fully explain. Models reach production faster than anyone can review them, and the failures look like successes.
AI bills that can’t be forecast, or justified.
Token and compute consumption is unpredictable and hard to attribute. Forecasting the cost of a data science workload is challenging, let alone control it,
Strategy is decided by your vendors.
Each AI tool decision narrows the next. Model, framework and infrastructure chosen for you, constrained by the technology you’re locked in.
Know-how walks out in a text file.
Hard-won expertise written into prompts and agentic skills documented as plain-text turn know-how into leakable IP, while agentic access to production data is largely ungoverned.
What Probabl changes
Scale faster, stay compliant, and show your work.
Predictive modeling is an empirical discipline. We build the software that enforces statistical methodology across human and AI-agent workflows, preventing errors upstream, recording full model provenance, and fitting seamlessly into your existing stack.
✴ AI that makes you a better data scientist
Routine work automated, judgment preserved. Leakage and silent errors are caught as the model is built, so data scientists ship faster on models they can still explain.
✴ Lower total cost, faster time to value
Zero adoption cost, nothing to migrate. Licence, tokens and time in one equation: right-sized models, only the tokens the task needs, and errors that never reach production.
✴ Confidence to deploy, to operate and to grow
Governed access to AI agents, and an audit trail generated rather than assembled. Maintainable with any AI provider, any ML framework, any compute — cloud, on-premise or local
✴ AI transformation you own
Your teams’ judgment compounds into private skills that stay yours, so data science becomes a competitive asset — and your tools adapt to your strategy, not the other way round.
“Building predictive models isn’t just writing code—it’s an empirical discipline. Probabl gives your teams the freedom to leverage AI agents at full speed, with absolute confidence that your statistical methods, lineage, and models are defensible.”
Gaël Varoquaux, Co-Founder & Chief Scientist, Probabl.
Discover Skore
Quality built into every step of the iterative data science workflow.
Designed by the stewards of Scikit-learn, the open-source library it is built on, Skore is the harness that makes AI predictive models hold up. It is the essential methodology layer to build and run models on tabular enterprise data, with quality built in. Validation, governance and reproducibility inside the workflow rather than bolted on after it. Composable with the tools teams already use.
Built by data scientists for data scientists
✴ Security
AI agents work inside a bounded, credentialed workspace.
✴ Governance
Consumption is measured and each task goes to the right-sized model.
✴ Experimentation
Features, parameters and models stored with every iteration, so any run can be reproduced.
✴ Measure
Models are judged on the metric and KPIs the business answers for.
✴ Deployment
The chosen model moves out with its record attached.
What data science leaders say about Skore
Scale agentic workflows with confidence.
From automated leakage detection to audit-ready compliance, more data science teams in companies small and large depend on Skore every day to bring scientific methods to agentic AI.
“Before Skore, catching silent errors and data leakage in agent-suggested pipelines felt like manual needle-in-a-haystack work. Now, empirical checks run automatically inside our notebooks. We get the speed of AI agents without losing faith in our methodology or models.”
Marcus Vance, VP of Enterprise Data & Analytics
“Skore gave us the exact methodology layer we needed to scale generative and agentic workflows safely. Every model generated now arrives with an audit-ready lineage trail and proven statistical rigor. Our teams move at market speed, and leadership has total confidence in the output.”
Elena Rostova, Principal Data Scientist
Move fast and bullet-proof your work.
Whether you are scaling AI agents across the enterprise or elevating your data science workflow, Probabl provides the methodology layer to move fast with complete accountability.
Insights on data science and AI, by humans for humans
The Enterprise Data Scientist blog
Enterprise decision making grounded in the science. Written by the open-source creators and stewards of scikit-learn.
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Community Voices: Interview with Lucy Liu, scikit-learn core maintainer
scikit-learn is one of the most widely used tools in data science, counting over 200 million downloads every month. You may know it as the Python library, but behind the code there is a global community that develops, maintains, and sustains it for the benefit of all data scientists all over the world…
Cailean Osborne, Community Manager
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The path toward Data Scientist Agents that enterprises can trust in production
Data scientists are already adopting AI coding assistants and agents in their workflows, but we’re not yet at a point where we can delegate end-to-end data science workflows or machine learning experiments to agents. The missing puzzle pieces in the capabilities of Data Scientist Agents are proper methodology and statistical thinking.
Yann Debray, AI Data Scientist
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Why Probabl signed the Open Weights open letter
Probabl has signed the Open Weights and American AI Leadership open letter alongside Microsoft, Nvidia, Intel, IBM, Mistral, GitHub, Hugging Face, and over 270 other companies.
Together, we are standing up for the open AI ecosystem.
François Méro, CEO