You’ve built a killer model — now what? Oftentimes you’ll want to apply your model to make great decisions. We pioneered decisionOS to do just that — it’s the first decision optimization framework built with Causal AI.
Standard AI just generates predictions, decisionOS goes further.
Ensure AI solutions fulfil business, legal, ethical, and other needs
A key reason most AI projects fail is that they lack actionable recommendations. Causal AI understands the objectives and constraints of your business
Causal AI is a fundamental scientific breakthrough and causaLens’ vision for Causal AI extends far beyond enterprise decision making. causaLens has the potential to disrupt a vast range of sectors and industries and has already demonstrated the value of its Causal AI technology in biological applications such as the discovery of cancer biomarkers”
*This should be taken as a personal statement and not a statement or endorsement from Mayo Clinic. Mayo Clinic does not support or endorse any commercial products or companies and never has.
The causaLens platform plays an important role in our investment process and has helped us generate outsized returns. It yields transparent and explainable models that we have confidence in, as they are based on causal relationships”
Transparency and explainability of AI models requires an understanding of causality—an inherent advantage of the causaLens platform”
The novel Causal AI techniques available on the causaLens Platform have facilitated our joint effort of discovering valuable profitable trading strategies”
The causaLens platform has enabled us to discover additional value in our data. Their causal AI technology autonomously finds valuable signals in huge datasets and has helped us to understand relationships between our data and other datasets.”
Causal AI plays an ever more important role in our investment analysis. It empowers our strategists and portfolio managers to generate alpha by identifying new causal relationships in economic, financial and alternative data, with sophisticated, adaptive and explainable models that don’t suffer from overfitting.”
We use causaLens to autonomously discover alpha in our data. We would have not been able to discover these valuable causal signals without it.”
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