Oliver Matthews – Associate, Office of the CEO at causaLens
casuaLens is a London-based startup infusing AI models with “Causal AI” which adds nuance, context, reasoning, and understanding of cause-and-effect relationships in AI systems — allowing them to better mimic human reasoning. Unlike traditional machine learning, which predominantly relies heavily on historical data and statistical analysis, causal AI enables decision-making processes that more closely resemble human thought. With causal AI, businesses can better understand why customers take certain actions like making a purchase or churning from a service subscription — then make better strategic decisions.
The causaLens technology not only boosts the accuracy of AI output reduce the occurrence of “AI hallucinations” or errors in results.
“causaLens is committed to helping enterprises make better decisions,” said Oliver Matthews from causaLens.
“By harnessing AI to analyze cause-and-effect and leveraging those insights across various use cases, we can ground AI in causal reasoning.”
— Oliver Matthews, Associate, Office of the CEO at causaLens
Maksim Sipos, Founder & Chief Scientific Officer
causaLens goes beyond mere predictive analytics by offering actionable insights for companies in a wide range of industries such as retail, healthcare, and finance. For instance, an IT products manufacturer saved $19 million annually by matching inventory levels to customer demand more accurately using causaLens, and saw a time-savings of 80% because team members no longer had to analyze data on large spreadsheets.
“Causal AI elevates business capabilities beyond prediction to enable prescriptive recommendations that improve business outcomes,” explained Matthews.
A unique aspect of causaLens is its capability to integrate knowledge from human experts—not just datasets.
“This approach not only enriches our models with information beyond existing enterprise data but also engages stakeholders in the modeling process, increasing their engagement and buy-in because they understand how it works,” Matthews added.
From Concept to Reality: The causaLens Origin
Founders Darko Matovski and Maksim Sipos launched causaLens to expand the capabilities of data analysis. Matovski, a PhD in computer vision and Sipos, a PhD in physics, became inspired by the idea of developing technology that could understand cause and effect — just like humans. They were particularly interested in creating models that don’t simply analyze the past or make predictions but offer actionable steps to shape the future.
In 2017, causaLens was born and it has rapidly ascended as a powerhouse in the AI space securing collaborations with GE Healthcare, Bergfreunde, and Aisin. The growth trajectory also included a $45 million Series A round of venture capital funding in 2022.
A Milestone Partnership: causaLens and LIFT Labs
In the spring of 2024, causaLens participated in the Comcast NBCUniversal Vertical AI Accelerator. The opportunity allowed the company to connect with key Comcast executives to test its solution and explore potential long-term partnerships.
Darko Matovski, Founder & CEO
“The accelerator presented a great chance to understand the opportunities for Comcast with regard to AI, and if causal AI could be helpful to their processes,” said Matthews.
The accelerator was also an opportunity to interact with the other startups in the cohort to learn how they see the future of AI and what solutions they are bringing to enterprises.
“There are some super interesting technologies in this cohort,” he said. “While we are here to explore possible connections within Comcast, the program also opened avenues for invaluable networking with peers in the AI space, which is crucial for our growth and evolution.”
Looking ahead, causaLens is focused on expanding its capabilities into generative AI and expanding into new industry verticals. In the long-term, the company envisions becoming the go-to solution for enterprises seeking to enhance decision-making through AI.
“We see causal AI not just as a tool, but as a collaborative partner working alongside humans to enhance decision-making capabilities, said Matthews.