What is Causaly?
Causaly provides an advanced AI platform tailored for the life sciences sector, aiming to enhance Research and Development (R&D) productivity and mitigate risks. It integrates sophisticated AI capabilities, including a generative AI copilot and Scientific RAG™ for information retrieval, with a high-precision biomedical knowledge graph containing millions of causal relationships. This combination allows research teams to efficiently find, interpret, and utilize vast amounts of biomedical information.
The platform facilitates the unification of external and internal data through its Enterprise Data Fabric, creating a single source of truth for organizations. This supports research continuity and enables faster discovery of critical insights. It is designed for enterprise-level deployment, offering features like central AI governance to ensure reliability and trustworthy results for complex scientific inquiry, ultimately strengthening R&D pipelines and supporting data-driven decision-making.
Features
- Generative AI Copilot: Ask complex biomedical questions and receive trustworthy responses with inline citations.
- High-Precision Knowledge Graph: Access a large graph with 500 million relationships focusing on causality, not just co-occurrence.
- Enterprise Data Fabric: Securely integrate internal and external data sources into a unified platform.
- Scientific RAG™: Advanced retrieval-augmented generation optimized for life sciences information retrieval.
- Bio Graph API: Provides programmatic access to the knowledge graph for bioinformaticians and data scientists.
- GenAI Operating System: Offers central AI governance, supporting multiple LLMs for reliable, enterprise-scale AI.
Use Cases
- Rapidly identifying and prioritizing drug targets.
- Discovering and validating novel biomarkers.
- Understanding complex disease biology and pathophysiology.
- Accelerating literature review and evidence synthesis.
- Integrating internal research data with public biomedical knowledge.
- Improving safety assessment in clinical development.
- Enhancing collaboration between research, data science, and IT teams.
