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Causal AI:
AI
  • Decision-Makers
  • Asset Managers
  • Governments
  • Banks
  • Marketers
  • Investors
  • Insurers
  • Agencies
Can Trust

causaLens develops human-centered decision-making AI
that organizations trust.

causaLens is the pioneer of Causal AI — a new category of intelligent machines that reason about the world the way humans do, through cause-and-effect relationships and with imagination.

Why Causal AI

Causal AI goes beyond standard predictive analytics, directly augmenting human decision-making. Its recommendations are intrinsically explainable, reliable in real-world scenarios, and sensitive to business and governance constraints.

 

 

We have a vision of a society powered by Causal AI

Learn more about our vision

Trusted leader in the Industry

Organizations Across Industries Trust Causal AI

  • CAPITAL MARKETS
    100M
    Uplift in portfolio returns, with 1000+ models in deployment
  • ASSET MANAGEMENT
    19%
    Average improvement in Sharpe ratios, compared to the state of the art
  • REAL ESTATE
    $15M
    ROI from identifying underpriced commercial properties
  • HEALTHCARE
    100x
    Accerleration of protein biomarker detection
  • TECHNOLOGY
    $19M
    Annualized saving through supply chain optimization
  • GOVERNMENT
    £14B
    Foreign aid budget allocation algorithmically optimized

Enterprise Decision-making AI Levels

AI maturity is a critical determinant of success in almost every industry. A chasm is opening between early and late adopters. Leading organizations use traditional AI to find solutions to business-critical problems, but their challenges cannot be solved with standard correlation-based machine learning. Forward-thinking organizations are turning to Causal AI.

Level 0

No AI

The organization doesn’t use artificial intelligence to make decisions. Most organizations are stuck at L0. AI is confined to data science experiments and has no impact on real decision-making. The limitations of standard machine learning technology are to blame.

 

Core problems include a lack of adaptability to real-world dynamics, and a lack of explainability. Data scientists may attempt to implement “post hoc explainability” methods, but these methods do not produce actionable insights. 

There are many other limitations of standard machine learning (see L1). However, these two mean that business decision-makers do not trust AI systems sufficiently to let them out of the lab. Read up on the shortfalls of current state-of-the-art AI here

Organizations stuck at L0 are wasting resources on AI investment which has zero impact.

The Causal AI Decision-Making Platform

A No-Code enterprise AI platform that turns raw data into improved business decisions.

Learn about the first Causal AI Decision-Making Platform
From predictions to business decisions
Build Causal AI models
Identify valuable data

Augment your decision-making capabilities today

Request a demo