Causaly provides a cloud-based AI SaaS platform used to identify drug targets, determine biomarkers, and aid pathophysiology research to speed drug development. Its platform emphasizes natural-language querying and modeling of chemical permutations to reduce false starts in R&D. The company says its tools can shorten the typical 10–15 year target-to-trials timeline to “several” years and is not itself developing therapeutics. Causaly works with 12 of the world’s largest pharmaceutical and research organizations, including Gilead, Novo Nordisk, Regeneron, the FDA and the National Institute of Environmental Health Sciences. Management plans to incorporate more large-language-model capabilities via fine-tuning (not training LLMs from scratch) to make querying easier without greatly increasing compute needs. The recent funding will be used to support R&D and to continue building out the team. Causaly's platform uses AI and machine learning to scour scientific literature, regulatory documents, clinical trials and proprietary research, mapping correlations and relationships that are difficult for humans to find. It reframes the biomedical workflow from “search, read, and synthesise” to “ask questions and analyse,” enabling researchers to pose complex queries and get rapid insights. The product is positioned to speed decision‑making about which research areas to prioritise and to shorten learning cycles in drug discovery. Founded in 2018 by Artur Saudabayev and Yiannis Kiachopoulos and based in London, the company emphasizes intuitive, interactive access to scientific evidence. Causaly raised $17 million in a Series A and plans to use the capital to grow its technology and sales teams. Management is aiming to use the funding to support expansion into the American market. Causaly builds natural language processing AI that reads, understands and interprets biomedical literature, visualising relevant relationships within seconds. The company says its platform has ingested over 30 million biomedical publications and notes roughly 100,000 new articles are added each month. Causaly works with pharma and biotech companies, hospitals and academia — customers include Novartis — and the platform is used across R&D and commercial functions such as drug discovery, drug safety, clinical trials, epidemiology and HEOR. Leadership describes the product’s aim as helping researchers and decision-makers find pivotal insights hidden in millions of documents and accelerate time-to-insight versus traditional literature reviews. The engineering team is focused on hard natural language understanding problems, including linguistic causality comprehension, with goals of reaching or in some aspects surpassing human reading performance. The company announced a Series A in which new and existing investors committed $4.8M; proceeds will be used to substantially grow technology development and customer success teams. Causaly offers a machine reading platform that analyses millions of documents to build knowledge graphs highlighting cause-and-effect relationships in biomedicine. Its NLP-driven semantic search and causal graphs let scientists explore associations and gather evidence far faster than manual review. The company has extracted more than 100 million causal associations from published academic literature and counts clients including Novartis. Co‑founders Yiannis Kiachopoulos (CEO) and Artur Saudabayev began the project after meeting at Singularity University's Global Solutions Program and chose biomedicine as their initial domain. Causaly aims to build a causal model of the world's research and apply it to drug discovery, health economics and pharmacovigilance. The company intends to use the seed financing to scale the team and accelerate product development.

Total raised

$83M

Funding rounds

4

Latest round

Series B

Latest activity

Jul 2023