Causaly

Causaly is the agentic AI platform built for pharmaceutical and biotech R&D operations. Founded in 2018 in London, the platform combines a proprietary biomedical knowledge graph of 500 million facts and 70 million directional relationships with autonomous AI research agents designed for high-stakes scientific decision-making. Causaly serves 12 of the top 20 global pharmaceutical companies, including named customers Gilead, the FDA, and the National Institute of Environmental Health Sciences. Core use cases span target identification, drug repositioning, mechanism of action investigation, safety assessment, competitive intelligence, and biomarker validation across the full drug discovery and development pipeline. Causaly is built API-first and connects natively to four integration categories. First, internal proprietary data ingestion allows pharmaceutical companies to bring their own clinical trial records, compound libraries, and research repositories directly into the platform, creating a unified evidence layer across internal and external sources. Second, a Microsoft technology partnership (announced June 2026) combines Causaly's knowledge graph and scientific reasoning with Microsoft's enterprise AI infrastructure for biopharma R&D decision workflows, currently in early customer engagement. Third, the platform supports MCP as a client, enabling research agents to connect to external data sources and agentic AI frameworks. Fourth, the biomedical knowledge graph integrates data from published scientific literature, clinical databases, and regulatory filings, updated with more than four million data points monthly. Notable gaps for teams evaluating the platform: Causaly does not publish native integrations with EHR systems such as Epic or Cerner, clinical trial management platforms such as Veeva Vault or Medidata, or general-purpose collaboration tools. Causaly is enterprise-only with no public self-serve pricing. No pricing page exists on the website; all contracts are sales-led and billed annually. The platform does not offer trial access, freemium tiers, or individual researcher pricing. Contact the sales team via the demo request form on causaly.com. Causaly is not designed for individual academic researchers, small biotech startups, or teams that need accessible self-serve literature tools. Elicit (from $12/month) covers structured systematic reviews at accessible pricing with a self-serve academic model. Scite.ai (from $12/month) provides citation analysis and smart reference verification for individual researchers. Semantic Scholar delivers AI-assisted academic search for free via the Allen Institute for AI. ResearchRabbit is free for academic users and maps citation networks without enterprise overhead. For general-purpose AI research rather than pharma-specific evidence synthesis, Gemini Deep Research (from $19.99/month) and ChatGPT Deep Research (from $20/month) handle broad research tasks at a fraction of the cost and with no sales process required. Causaly provides no value outside life sciences R&D and is not a fit for organizations with diverse research needs spanning multiple domains. In Q2 2026, Causaly launched Scientific Workflows (May 2026), extending the platform from ad-hoc AI research queries into governed, repeatable, end-to-end agentic automations that codify expert research processes for entire R&D teams, directly addressing the reproducibility gap in pharmaceutical R&D where output quality has historically varied by individual scientist. The Microsoft joint solution (June 2026) is in early biopharma customer engagement. Documented customer outcomes include a 5x productivity improvement over PubMed for target identification (ProQR, February 2025 case study) and a 75% reduction in proposal time during a disease area transition (a top 10 global life sciences company, April 2026 case study). The platform holds ISO 27001 certification. Causaly Agentic Research launched in September 2025 as the AI agent framework powering the current product suite. The knowledge graph adds more than four million data points monthly from biomedical literature and regulatory sources.

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