The Causaly platform

The AI platform for scientific evidence and reasoning

Causaly combines 648M+ scientific relationships with a curated data fabric and AI that reasons through the evidence, helping scientific teams investigate complex R&D questions and reach go/no-go decisions they can trace and defend.

See Causaly in action

How does Causaly work?

Causaly works through a scientific question much as a research team would, clarifying what is being asked, examining supporting and contradictory evidence, and identifying gaps. It brings these findings together in a cited assessment that scientists can review, question, and refine to inform their next development decision.

Step 01

Frame the question

Causaly helps you refine your research question by enhancing your prompt and identifying the genes, proteins, diseases, drugs and compounds it references, including those represented by chemical structures. It maps these references to uniquely defined entities using public vocabularies, curated ontologies and, where needed, your internal data, establishing a precise starting point for your research.

Step 02

Plan the investigation

Agentic Research splits a complex question into focused sub-questions, investigates each using relevant sources and uses the findings to guide subsequent steps. It records a reasoning trail so reviewers can trace how the evidence informed the conclusions.

Step 03

Retrieve the evidence

Each sub-question draws on scientific literature, licensed full text, patents, clinical trials, Bio Graph, Pipeline Graph, specialist data sources, and web search. The Scientific Information Retrieval System maps references across these sources to consistently defined entities and prioritizes the evidence most relevant to the question.

Step 04

Screen and weigh

Documents that mention the topic without addressing the question are filtered out. Extended Reasoning then evaluates the relevant evidence, distinguishing findings that support each claim from those that contradict it and identifying gaps or conflicting results. Each claim is linked to its sources through inline citations, with the corresponding passages highlighted for review.

Step 05

Apply your method

Scientific Workflows translate your team’s best practices into governed processes that apply consistent methods and evidence standards, with an auditable record of each run. Start with a workflow developed by scientists from the Workflow Library or build one around your team’s requirements.

Step 06

Deliver the work

Presents findings as PDFs, slide decks, charts or tables, with each claim and data point linked to its source. Shared Workspaces keep these outputs accessible to your team and provide notifications when new evidence changes an answer you’re tracking.

How is Causaly different from other scientific AI tools?

Different tools are built for different kinds of work. Causaly brings scientific evidence and reasoning together to support R&D decisions, with a curated data fabric and AI that investigates complex questions. By weighing supporting and contradictory findings and making sources, assumptions, and uncertainties visible, it helps scientific teams reach go/no-go decisions they can trace and defend.

Causaly
Scientific evidence, reasoning and decision platform
General AI research assistants
Biology-focused AI platforms
Does it cover the whole R&D lifecycle, from target biology to commercial?
✓
Discovery, clinical development and commercial
Causaly supports work across the R&D lifecycle, from understanding target biology and guiding clinical development to assessing competitive positioning and helping inform commercial strategy.
◐Depends on the user's setup
◐Focused mainly on preclinical research
Does it weigh clinical, patent, pipeline and safety evidence in one answer?
✓
Built in
Causaly brings evidence from clinical trials, patents, FDA labels and FAERS together with the biology and Pipeline Graph’s view of who is developing what. It weighs these findings in a single answer, showing where they support a conclusion, conflict or leave gaps.
◐If connectors are set up
◐Some add clinical and patent data to support preclinical work; safety data coverage varies
Does every answer check its evidence and show what it did not cover?
✓
Breadth and depth in every answer
Each answer reports the breadth and depth of the evidence it found and names the parts of the question the evidence did not reach, alongside a limitations section
◐Some tools check citations and figures; a breadth and depth measure was not identified in public sources
◐Some platforms filter weakly supported claims; a breadth and depth measure was not identified in public sources
Does it reason over a curated base of known relationships?
✓
Two curated knowledge graphs
Reasons over two in-built, curated knowledge graphs, updated every 2 to 4 weeks, and also synthesizes information from connected sources
—Query and synthesize information from whichever sources a user connects
✓Some reason over a knowledge graph focused on preclinical biology
Can a committee audit the method?
✓
Traceable and auditable end to end
A committee can review the assessment method, follow the thinking trail and trace claims to their sources, including conflicting evidence and gaps. Scientific Workflows also provide an auditable record of each run.
✓Keep a history of outputs, with preconfigured and custom capabilities
✓Some offer prebuilt research workflows with a record of each run
Does it support change management and adoption across teams?
✓
Scientists and success teams alongside yours
In-house expert scientists and Causaly teams work alongside your team on research and clinical development questions, and help codify its methods into scientific workflows through to full rollout
✓Vendor and partner teams support adoption
✓Vendor delivery and support teams help with adoption
✓Built in◐Partial or depends on setup—Not identified in the public sources reviewed

Based on public information about representative tools in each category, reviewed on October 30, 2026.

What R&D work does Causaly support?

Causaly helps teams evaluate the scientific rationale, opportunities and risks behind R&D decisions, from understanding a target’s role in disease to determining where an asset could offer the greatest clinical value. Teams can explore questions across discovery and development, tailoring each assessment to the asset, its stage of development and the decision they need to make.

Assess a target

Weigh genetic, mechanistic, safety and translational evidence before a target enters the portfolio.

Example question

Evaluate NLRP3 as a target for ulcerative colitis.

Prioritize indications

Compare candidate indications on mechanistic fit, depth of evidence and competitive density.

Example question

For TYK2 inhibitors, rank major inflammatory disease indications by mechanistic rationale and competitive landscape.

Interpret a safety signal

Separate class effects from compound-specific risk across trials, case reports and adverse event data.

Example question

Which adverse events reported for JAK inhibitors have mechanistic support, and which are confounded?

Map a competitive landscape

See every program against a target by modality, phase and sponsor, refreshed as pipelines change.

Example question

List ongoing and completed KRAS-targeted assets by modality, clinical phase and sponsor.

Find candidate biomarkers

Surface biomarkers with directional evidence for patient selection or treatment response.

Example question

Which circulating biomarkers have evidence for predicting response to anti-fibrotic therapy in IPF?

Keep your science current

Track new trials, publications, patents and safety reports for a mechanism without rebuilding the search.

Example question

What new KRAS inhibitor patents have been filed in the last 6 months, and by which organizations?

How are R&D teams using Causaly?

Scientists across discovery, translational research and clinical development describe what changed in their work.

Target validation

“Companies live and die based on the identification and veracity of their targets. If you make a mistake at the very first step, every step that follows will be a snowball effect from that initial wrong decision.”

Dr. Siddhartha Mukherjee
Co-founder, Manas AI
Read the case study →
Early discovery

“I use it daily for early ideation, hypothesis validation, biomarker exploration, safety signal monitoring. It’s become the starting point for the kind of deep scientific questions that used to take days to get a foothold on.”

Eisai
Scientist, Early Discovery and Translational Research
Read the case study →
Clinical program evaluation

“If it outputs a table, I can pull the exact references behind each point. It’s faster and reduces the risk of missing key perspectives.”

Top 10 pharma company
External Innovation Advisor
Read the case study →

Frequently asked questions about Causaly

What is Causaly?

Causaly is an AI platform for life sciences R&D that helps scientists connect evidence, investigate biological questions, automate their team’s methods through scientific workflows and develop scientific assessments that inform research and development decisions.

Does Causaly work with external AI tools and models?

Yes. Causaly connects to other assistants and agent platforms through MCP, A2A and APIs, and works inside Microsoft 365 Copilot.

Which data sources does Causaly use?

Causaly has its own data fabric that brings together evidence spanning molecular biology, disease mechanisms, and clinical development. It combines findings from scientific literature and licensed full-text with structured biomedical data, drawing on trial, patent, and regulatory records to place the science in its development context, along with your organization's own data, where your organization chooses to connect it.

How current and reliable is the data?

Causaly's knowledge graphs are refreshed every 2 to 4 weeks, and news and web sources are searched at the moment each question is asked. Every citation is verified against its source before an answer is shown.

Can my organization's internal data be connected to Causaly?

Yes. Scientists can analyze their own documents alongside the published evidence, and organizations can connect internal tools or bring their data into a dedicated, isolated Causaly environment.

How can scientific teams adopt AI for R&D?

Start with a defined research task and agree on the evidence, assessment method and expected output. Involve IT early to establish data access permissions, security requirements and integration needs, with clear ownership and an audit trail for each workflow. Test a focused use case, with scientists reviewing the evidence and conclusions before expanding its use.

How does Causaly pricing work?

Pricing depends on the number of users and the capabilities your teams need. Contact the Causaly team for a quote.

Does Causaly train models on customer data?

No. Customer data is never used to train AI models, and Causaly is ISO/IEC 27001 certified.

See Causaly work through your hardest open question

Bring a target, indication or safety signal your team is weighing now. We’ll run it through Causaly with you and show the evidence behind the answer, what contradicts it and what is still missing.

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