Causaly

Causaly is an artificial intelligence company founded in 2018 by computer scientists Yiannis Kiachopoulos and Artur Saudabayev, who met at Singularity University’s Future Studies programme. The company builds AI systems to help life sciences organisations speed up scientific discovery, drug development and health research. Its mission is to accelerate discovery in life sciences through AI technologies, and its vision is to create advanced tools that support the development of essential medical treatments and safer consumer products.

Causaly has developed an AI platform designed for enterprise-scale scientific research. The platform allows scientists to find, read and interpret biomedical information at a scale far beyond human capacity. It analyses tens of millions of scientific papers, public databases and internal research documents. The platform identifies patterns, causal relationships and scientific insights that support research planning, hypothesis generation and mechanistic understanding. Causaly’s technology is positioned as a new way for researchers to manage the increasing volume of scientific knowledge and the growing demand for precision medicine.

A key part of the platform is the Biomedical Knowledge Graph, which organises scientific concepts and relationships with high accuracy. Causaly also provides a Generative AI Copilot built specifically for scientists, a Scientific RAG system for information retrieval and an Enterprise Data Fabric that strengthens how organisations use their internal data. These tools support research teams working in drug discovery, safety studies, competitive intelligence and biological data interpretation.

Causaly’s wider product suite includes Agentic Research for R&D workflows, Discover for enabling research teams at scale, Bio Graph and Bio Graph API for accessing the life sciences knowledge graph, and Pipeline Graph, which brings scientific and competitive information together. The company has its headquarters in London with offices in New York and Greece, and its team includes experts in science, technology and industry.

Causaly began in 2018, when computer scientists Yiannis Kiachopoulos and Artur Saudabayev met during their time at Singularity University’s Future Studies programme. Both founders had a strong interest in the growing volume of scientific information and the need for faster ways to analyse research data. At the same time, life sciences organisations were facing pressure to speed up drug discovery, improve safety assessments and respond to the rise in demand for precision medicine. The founders saw that researchers were struggling with millions of scientific papers, isolated databases and disconnected internal documents. This gap led them to establish Causaly with the aim of building an AI system that could read scientific information at scale, extract causal evidence and support researchers in making accurate scientific decisions.

During the early years, Causaly focused on building a machine-learning engine capable of processing biomedical literature at very high speed. The team worked on natural language processing methods that could identify biological relationships, mechanisms, pathways and cause-effect links. This work resulted in the creation of Causaly’s high-precision Biomedical Knowledge Graph, which became the foundation of the company’s platform. As the graph expanded, the platform began to help research teams understand disease biology, explore mechanisms of action, and support early drug discovery projects.

As the company grew, Causaly expanded its team by hiring specialists in data engineering, biology, pharmacology, artificial intelligence and software development. It opened its headquarters in London and later added offices in New York and Greece to support global operations. The platform moved beyond literature mining and became a full research environment that could combine public data sources with internal enterprise information. This included work on information retrieval systems, scientific reasoning models and agent-based automation for research workflows.

Causaly then introduced new products such as the Generative AI Copilot for scientists, Scientific RAG, Enterprise Data Fabric, Bio Graph, Bio Graph API and Pipeline Graph. These tools were designed to support research planning, safety assessments, target discovery, biomarker work, competitive intelligence and portfolio strategy. The company began working with pharmaceutical companies, biotechnology firms and other research organisations to integrate the platform into their daily scientific processes.

Today, Causaly continues to operate as a global AI company focused on enabling faster and more reliable scientific research. Its platform is used to absorb biomedical knowledge across millions of publications and bring structure to complex scientific information. The company’s team works on expanding the capabilities of its knowledge graph and developing new AI systems to support research and development. Causaly remains active in building tools that improve how scientists access, understand and apply information in life sciences, and its work is ongoing as the organisation continues to support drug discovery and health research across the industry.

Causaly’s mission is to speed up discovery in life sciences by building AI technology that helps researchers understand scientific information quickly and clearly. The aim is to support scientists in finding reliable evidence, identifying links between biological concepts, and reducing the time needed to explore complex research questions. Causaly focuses on creating tools that allow teams to read and interpret large volumes of biomedical data, connect internal and external information, and improve daily research workflows. Through its platform, Causaly seeks to help organisations develop treatments, improve safety decisions, and make better scientific choices based on accurate and organised knowledge.

Causaly’s vision is to create AI systems that support the development of important medical treatments and safer products by making scientific knowledge easier to access and understand. The company aims to build technology that can read, analyse, and connect scientific information at a scale no human team can achieve alone. Causaly plans to support the entire research process by improving how scientists find answers, explore biological mechanisms, and work with data. Its long-term goal is to become a core technology in life sciences research, helping organisations respond more quickly to global health needs and enabling better decisions across the scientific community.

Causaly has received recognition for its work in life sciences and AI research. In May 2025, the company was named among Europe’s top startups, reflecting its growing influence in scientific technology. Causaly raised 60 million dollars in Series B funding in 2023, supporting the expansion of its research platform. The company works with major life sciences organisations, including Novartis and Novo Nordisk. Its Scientific RAG technology, created by co-founder and CEO Artur Saudabayev, has been recognised for delivering AI answers that follow scientific standards. Causaly has also introduced innovations such as Causaly Discover, Deep Research, and agentic AI systems for R&D workflows.

Causaly provides a full set of AI products and services designed to support scientific research, drug discovery, safety studies and competitive intelligence in the life sciences sector. The platform is built to help researchers understand complex biomedical information, connect data sources and automate time-consuming scientific tasks. Each product is designed to work together as part of one research environment.

One of Causaly’s core products is Agentic Research, a science-grade AI system built for research and development teams. It is designed to follow R&D workflows, respect data governance rules and remain accountable to human oversight. Agentic Research supports activities such as exploring disease biology, generating hypotheses, reviewing safety signals, and understanding mechanisms of action. It helps teams quickly identify meaningful scientific insights across a large volume of research data.

Another major component is Causaly Discover, a platform that allows research teams to search, read and interpret scientific information at scale. Discover brings together millions of scientific papers, structured databases and internal company documents. It presents results in a way that allows scientists to trace findings back to their sources. This makes it easier for researchers to understand biological pathways, compare evidence, track emerging science and evaluate drug targets. Discover is widely used for research planning, literature reviews, translational science and early-stage drug discovery work.

Causaly’s Bio Graph is a high-precision biomedical knowledge graph that organises biological concepts and relationships found across scientific literature. It connects genes, diseases, pathways, exposures and many other scientific entities. Bio Graph helps users identify cause-effect relationships and understand biological mechanisms more clearly. For organisations that need to integrate this capability into their own systems, Causaly provides Bio Graph API, which allows internal teams to access the knowledge graph directly and use it in custom applications, analytics platforms and research tools.

The company also offers Pipeline Graph, a tool that combines scientific insights with competitive intelligence. Pipeline Graph helps teams track therapeutic areas, monitor competitor activity, study drug pipelines and understand the scientific rationale behind different treatments. It supports decision-making in portfolio strategy, market assessments and future research planning.

Causaly’s AI platform includes several advanced technologies that enhance its products. One key innovation is Scientific RAG, an information retrieval technology that uses the Causaly knowledge graph to produce AI answers that follow scientific standards. It ensures that every AI-generated answer is traceable to real evidence. Scientific RAG was invented by Causaly co-founder and CEO Artur Saudabayev and is designed specifically for life sciences research.

Another major feature is the Generative AI Copilot, the first AI copilot built for scientists. It can summarise scientific information, generate reports, assist with study design and help teams interpret data. It works with the knowledge graph and Scientific RAG to ensure accuracy and reliability.

Causaly also provides an Enterprise Data Fabric, which helps organisations connect internal documents, research systems and data sources. This allows teams to use their internal data together with public scientific information in a single research environment.