Innovation Leaders: Transform Your Organization with Causal AI
Empower your organization to go beyond black-box predictive modeling.
Discover cause-effect in your data to drive better decisions at scale.
Causal AI makes GenAI safe and trusted for decision-making in the enterprise
Generative AI & Traditional Machine Learning are great for use cases around unstructured data, creative tasks & automation.
But they often struggle with decision-making.
Causal AI takes organizations beyond these purely predictive models:
- Decision, not prediction
Causal AI goes beyond predictions by recommending the optimal action to achieve an outcome, within a given set of business - Track ROI, not technical metrics
Measure the impact of your decisions and AI workflows - Trusted AI, not black-boxes
Causal AI discovers and models cause-effect relationships resulting in inherently explainable AI models that can answer “why”.
Integrate Causal AI’s causal reasoning into Gen AI for data-driven decision-making.
AI that is aligned with your business KPIs
The performance of ML and AI systems is typically measured using technical metrics, which are often hard to translate to business value. Our platform measures the success of your systems directly in line with your business KPIs – track success by the value-generating decisions you make rather than technical metrics like model precision and recall.
Business & data science teams collaborate better through the language of causality
Traditional correlations-based Machine Learning does not extract the full potential of your data or knowledge of your domain experts.
With our platform, business experts & data scientists speak the same language – the language of causality – to align on the inner-workings of AI models.
The Causal AI revolution is underway
Causal AI was featured in 3 Gartner hype cycles and scored 1st on “not using, but plan to in the next year (2024)” in a Databricks survey of 400 senior AI professionals.
Leverage causaLens’ global experience applying Causal AI to your key business challenges
We have been pioneering Causal AI since 2017 and have years of experience helping Fortune 500 companies use Causal AI to address their core business questions.
Causal AI Pioneers
Watch here the presentations from leaders from BMW Group, Nestle, Mayo Clinic, AirBnB, Cisco, Bosch, Alstom, ScotiaBank, TotalEnergies and more at the annual Causal AI conference
Deliver with trust. Leverage inherently Explainable AI.
Causal Models are inherently explainable and can answer “why”, increasing the trust of executives, business teams, and regulators.
Existing explainability methods are often not enough, using proxy models to explain correlations.
Interactive applications that translate data science output into actions
To drive better decision-making, causaLens has created Dara – a bespoke framework for building self-service applications, that we call decisionApps, which business stakeholders frequently use. Dara is the only framework built specifically for Causal AI. It allows business teams to inspect the Causal AI models and run what-if analyses grounded in their data and make better decisions within a fully explainable framework without requiring deep knowledge of the underlying data science that drives it.
Fully extensible. Deploy anywhere.
We are on the Azure marketplace, and continue to partner with marketplaces to ensure full extensibility.
Models built using decisionOS, our Causal AI platform, can be seamlessly deployed and maintained in production as SaaS, on any major cloud platform, and on-premise. Integration with a wide range of data sources, like Snowflake, SAP, and BigQuery, is simple, allowing you to access all of your Enterprise data.
Trusted by Leading Organisations
The causaLens platform has empowered us to create powerful and beautiful decision applications that we rely on for critical decisions.
Gerald Mullaly, Director
Transparency & explainability of AI models requires an understanding of causality – an inherent advantage of the causaLens platform.
Wendy Harrington, Chief Data & AI Officer
Causal questions are in 1st order in tech firms. Lots of good executives have a causal theory of reality in their head. What they often want to see is dashboards, metrics, highly correlated data where the causal filtering is happening in a board room… The interesting challenge for the [Causal AI] community is now that we have these tools, you don’t have to do it intuitively, how do you reconcile this with the traditional mindset?
Amit Gandhi, Vice President and Technical Fellow
Customer Case Studies
Customer Case Study: Inventory Optimization
A leading manufacturer of IT products and equipment sees $19mn in savings from matching inventory levels to customer demand more accurately
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Customer Case Study: Client Retention
North American pension plan improved beneficiary satisfaction and increased retention by 17% using decisionOS powered by Causal AI
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Customer Case Study: Manufacturing Downtime Reduction
$10bn Metals Enterprise sees an expected return of $4M from maximal throughput while reducing downtime
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