New Causaly Work Helps Scientists Complete More of the Work Behind R&D Decisions, from Discovery Through Clinical Development
Scientists set the objective, and Causaly Work plans the approach, runs the research and analysis, and delivers finished work the team can review, trust, and share
LONDON, Nov. 12, 2026 /PRNewswire/ -- Before a life sciences R&D team can make a decision, scientists must find and weigh the evidence, analyze the data, compare the options, and prepare the reports, figures, and presentations the team needs. Causaly, the agentic AI platform for life sciences R&D, today introduced Causaly Work to help scientists from discovery through clinical development complete more of that work for their team's next decisions.
While Causaly's Agentic Research answers a question, Causaly Work pursues an objective. It pairs Causaly's scientific reasoning with a computational layer that produces a wide range of completed outputs. A scientist describes what they need to accomplish, such as ranking drug targets, building the case for a biomarker, or comparing past trials to inform a new study, and adds their own data or public sources. Scientists steer the work, challenge the results, and keep the decisions, while Causaly Work returns finished work the team can review.
"High-stakes decisions in a development program depend on understanding every option, and the work to evaluate those options rarely follows a fixed script," said Yiannis Kiachopoulos, co-founder and CEO of Causaly. "Causaly Work adapts to the problem in front of it, so scientists can finally explore the paths they once set aside for lack of time. It widens the set of options and evaluates each one across the dimensions that matter, giving the people at the decision gate a fuller picture to judge from."
Many promising paths in a development program go unexplored, not because they lack merit, but because exploring them takes more time than a team has. A single program decision can require searching the literature, analyzing internal and public datasets, working across specialist tools and systems, and turning the findings into reports, figures, and presentations.
Every step and handoff takes time, so teams look at fewer options. General-purpose AI tools can speed up single steps, but they give an answer to trust, not work to check. Scientists can't see which methods were used, what was ruled out, or what evidence backs the result. That leaves researchers accountable for decisions they can't fully check.
Purpose-built for life sciences, Causaly Work plans, carries out, and documents the work so the team can review every step:
- Scientific planning before anything runs - A complex problem has many possible paths. Causaly Work first writes a plan that sets out the methods it chose and why, the alternatives it sets aside, the assumptions and evidence gaps, and the criteria for judging when the work is done.
- Multiple agents that research methods and carry out the plan - Causaly Work directs specialized agents that research and validate the most appropriate methods, then coordinates them as they apply tools and scientific skills to complete each step.
- Evidence and code the team can inspect - Code runs in a private workspace and stays open for review. Conclusions link back to the scientific literature and Causaly's biomedical knowledge graph, so reviewers see the evidence behind a result as well as the calculation that produced it.
- Finished work products the team can share - Reports, ranked tables, figures, notebooks, slides, and interactive dashboards bring the rationale, caveats, and open questions together in one place the team can trust and pass along.
Causaly Work joins Agentic Research and Scientific Workflows in the Causaly platform, and each fits a different kind of scientific work. Agentic Research answers a scientific question with cited evidence in minutes. Scientific Workflows run an approach that experts have codified into Causaly, giving teams a stable, governed process for standard operating procedures and other recurring work. Causaly Work takes on open-ended objectives, such as ideation and exploratory analysis, and selects the methods, rubric, and evaluation that fit each one. All three draw on the same scientific evidence base.
To learn more about Causaly Work, visit https://www.causaly.com/products/causaly-work and register for our webinar at www.causaly.com/products/causaly-work.
About Causaly
Causaly is the agentic AI platform purpose-built for life sciences research and development. Powered by a high-precision biomedical knowledge graph and the world's scientific literature, Causaly enables specialized AI agents to automate scientific workflows and accelerate drug discovery and development with transparent, evidence-backed insights. Leading pharmaceutical and biotechnology organizations rely on Causaly to unlock scientific insights from external and internal information at unprecedented levels of precision and efficiency. For more information, visit causaly.com.
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