Causaly

Causaly provides an enterprise AI workspace purpose-built for life sciences R&D teams. The platform combines agentic research capabilities, a high-precision biomedical knowledge graph (500M facts, 70M directional relationships), and a commercial knowledge graph to support evidence-intensive decisions across discovery, development, and post-market phases.

Core Capabilities:

• Agentic Research: Domain-specialized AI agents plan, search, reason, and conclude on complex scientific questions, synthesizing published literature, proprietary competitive intelligence, and internal data.
• Scientific Workflows: Formalized decision logic for evidence assembly, literature synthesis, competitive monitoring, and decision support—including Target Product Profile (TPP) generation, target identification and prioritization, biomarker discovery, and disease pathophysiology analysis.
• Scientific Information Retrieval System (SIRS): Precision evidence retrieval with hallucination filtering, citation tracking, and recognition of no-answer scenarios.
• Enterprise Data Fabric: Flexible integration of proprietary data, internal documents, and external sources; scans millions of sources to ingest 100k+ documents per day.
• Professional Services: PhD scientists provide configuration, change management, workflow optimization, and adoption support.
• A2A Ecosystem Integration: Workflows exposed as callable agents within customer infrastructure.

Use Cases: Target identification and prioritization, biomarker discovery, disease pathophysiology characterization, target-disease association, indication expansion, TPP evidence generation, portfolio decision support, and competitive landscaping.

Differentiation: Causaly emphasizes scientific rigor, factual grounding, cross-functional governance readiness, and integration of proprietary context—addressing evidence-intensive decisions that require structured, auditable outputs suitable for formal governance moments and multi-function handoff.