Causaly

What changes when target identification is built on complete, auditable evidence? Most oncology target decisions are made based on a fraction of the evidence in the literature. Chosen by whatever surfaces first, or whatever the scientist already knows to look for. The shortlist does not reflect the best available evidence, but the evidence that was quickest to find. Teams using a more systematic approach are finding candidates that manual review would never surface. 7,000 NSCLC targets narrowed to 1,000 in five hours. Five previously missed RCC targets, each with a fully traceable evidence trail. This ebook shows how oncology teams can identify higher-confidence targets more quickly, uncover missed opportunities, and make every downstream decision on a stronger evidence foundation. Download the ebook: https://hubs.la/Q04jwYD40

The most effective R&D teams are transforming scientific expertise into organizational capability. That's why we built Scientific Workflows. Scientific Workflows enables teams to capture expert methods, codify best practices, and create governed agentic automations that run complex scientific workflows, generating structured, evidence-backed outputs ready for review. Sign up for our launch webinar to see how organizations like yours can operationalize scientific expertise with Scientific Workflows. 👇 https://hubs.la/Q04jlnrD0

Today is a proud moment for us as we launch Scientific Workflows, a new way of transforming scientific expertise into governed, AI-executed workflows that run end-to-end within the Causaly platform. Read our launch blog here: https://hubs.la/Q04j9xQN0 Life sciences R&D requires enormous amounts of scientific insight to support critical pipeline decisions, but the processes that generate it are slow and difficult to optimize or scale. Varying research methods and standards, siloed expertise, lost institutional knowledge, and processes reinvented each time can lead to inconsistency and gaps in key outputs - challenging confidence when it's needed most. Scientific Workflows solves that by enabling organizations to codify best-practice research processes and scientific expertise into repeatable, evidence-backed workflows that execute consistently across the R&D pipeline through autonomous AI agents. This moves organizations beyond scientific intelligence and into scientific execution. R&D leaders and teams can support key pipeline decisions with governed, auditable automated research processes grounded in science -delivering defensible, reproducible outputs at speed and consistency across the enterprise. We are incredibly proud of what the team has built, and excited about the impact Scientific Workflows will have across life sciences R&D.

Attending UK Biotech Day? Come hear Alexander Schurer, Director of Life Science Strategy at Causaly, speak in the Research & Discovery stream about how scientific AI workflows are changing the way biopharma teams approach research, evidence synthesis, and decision making. - Session: Evidence to Decisions: Scientific AI Workflows for Biopharma - Time: 12:15PM - Location: Atlantis 1 Hall The Causaly team will be at our table in the main hall after the session too, so stop by if you want to discuss in more detail. #UKBiotechDay #LifeSciences #AIWorkflows

Medical Affairs teams are under increasing pressure to deliver faster, evidence-backed responses with scientific rigor. On June 10, join us for the first episode of our new webinar series: Causaly Dissected: Accelerating Medical Affairs with Agentic AI Research 🗓 June 10 🕓 4 PM BST / 11 AM EST In this live session, our scientific experts will walk through real Medical Affairs workflows, including: • Scientific inquiry response • Evidence review • Practical applications of agentic AI in day-to-day operations It’s a hands-on look at how AI can support Medical Affairs teams by helping scientists, medical writers, and operations teams work more efficiently with trusted evidence. Register via the link in the comments👇:

Attending UK Biotech Day tomorrow? Come meet the team to see how Causaly accelerates evidence-based research and innovation for scientists. We’re attending UK Biotech Day to showcase how AI is helping biotech and pharma teams accelerate research, streamline decision-making, and drive more confident R&D outcomes. Want to connect before the event tomorrow? Come meet the team to see how Causaly accelerates evidence-based research and innovation for scientists. Book a meeting: https://hubs.la/Q04hsH4w0

Listening to Jeremy Jenkins give the plenary at #BioITExpo. He's giving a strong shout out to Causaly for target identification. What would have taken him days resulted in a 10-page report in about 2 minutes. Bottleneck moves to target validation.

We’re heading to UK Biotech Day to connect with teams across biotech and pharma who are exploring what’s next for AI in life sciences. At Causaly, we believe scientists should be able to spend more time advancing science and less time navigating complexity. That’s why we’ve built the solutions that help teams navigate scientific evidence faster, scale expertise across organizations, and remove friction from research workflows so better decisions can happen sooner. If you’re attending UK Biotech Day 2026 and want to explore how Causaly can support faster, evidence-based research and innovation, come meet the team. https://hubs.la/Q04hsH4w0

Proud to see Causaly included in the Barclays Eagle Labs AI:100 report, spotlighting AI companies to watch in 2026. The report highlights some of the most innovative AI companies across healthcare, enterprise software, infrastructure, and deep tech, and also includes insights from organizations including EY, Google Cloud, UKAI, and Wilson Sonsini Goodrich & Rosati on the direction of the AI ecosystem. At Causaly, we help biomedical and life sciences teams make sense of scientific evidence at scale with trusted AI, accelerating research, discovery, and decision-making. Thanks to Barclays UKand Beauhurst for bringing the report together. Read the report here: https://hubs.la/Q04hk7sk0

Most evidence outputs in pharma are built to be thorough. Very few are built to be acted on. A 40-page brief covering mechanism, preclinical data, competitive landscape, and regulatory precedent can still leave a VP with no idea what the team is recommending. Thorough coverage confirms that retrieval was done well. It does not tell a reviewer what the evidence means, where the argument is weak, or which gaps would change the conclusion if resolved differently. These are what make an output worth reviewing, and what separates a complete evidence summary from a decision-ready one. In our latest piece, our Director of Scientific Affairs, Stavroula Ntoufa, and Senior Scientific Advisor Ramon Dillon Perez, Ph.D break down what decision-ready outputs actually contain and why the distinction matters for the workflows R&D teams are building now. Link in the comments to read 👇