Graph AI Secures $3 Mn to Scale AI-Driven Drug Safety Platform

Graph AI’s platform, Graph Safety, seeks to transform this workflow by offering a unified AI-native safety stack that combines data integration, intelligent automation, and end-to-end auditability.

Graph AI has raised $3 million in seed funding led by Bessemer Venture Partners to expand Graph Safety, its AI-native platform designed to automate adverse event processing, signal detection, and regulatory reporting across the pharmacovigilance ecosystem.

Pharmacovigilance, or the continuous monitoring of adverse drug events, remains a critical yet highly manual process for pharmaceutical companies.

The industry still depends on extensive human reviews of unstructured data from sources such as medical literature, call center transcripts, and social media, often resulting in fragmented workflows, slower turnaround times, and increased compliance risks.

Graph AI’s platform, Graph Safety, seeks to transform this workflow by offering a unified AI-native safety stack that combines data integration, intelligent automation, and end-to-end auditability.

Founded in 2024 by Raghav Parvataraju (CEO), Vijay Ponukumati (CTO), Mohan Konyala (CPO), and Ashutosh Bordekar (CFO), Graph AI positions its platform as a context-aware solution that allows human reviewers to focus on regulatory decisions rather than repetitive data extraction.

The company stated that Graph Safety extracts complete information from structured and unstructured inputs, classifies cases consistently, and builds a centralized intelligence layer over time. This shift allows pharmacovigilance teams to move from manual entry to strategic oversight, improving compliance and operational efficiency.

According to early enterprise deployments, the platform has delivered up to 70 per cent efficiency gains, 90 per cent faster regulatory reporting, and significant cost savings while maintaining traceability. Graph AI’s customer pipeline currently spans more than 7,000 marketed drugs, reflecting growing demand for scalable automation solutions in drug safety.

The company emphasised that its design keeps humans in the loop for all regulatory-critical steps, maintaining full transparency through audit logs and versioned documentation. This compliance-first approach, it said, is intended to support regulatory readiness and reinforce trust in automated workflows.

In a statement, Graph AI’s founders said, “The life sciences industry continues to grapple with outdated technology, fragmented point solutions, data silos, and manual handoffs that hinder decision-making and elevate compliance risks. At Graph AI, we’re addressing these challenges with a unified, AI-native safety platform that integrates context, compliance, and intelligence into a single seamless ecosystem. Our vision is to make patient safety smarter, faster, and more connected, empowering pharmaceutical and biotech enterprises to achieve safer outcomes, stronger regulatory confidence, and exponential efficiency across safety operations.”

Bessemer Venture Partners, which led the round, highlighted the broader trend of intelligence-led transformation in life sciences. The firm stated, “We’re excited to partner with Graph AI as they redefine labour intensive and inefficient pharmacovigilance workflows through AI-native solutions that prioritise both accuracy and scalability. At Bessemer, we’re deeply optimistic about the transformative potential of AI products to reimagine traditional service models, as for the first time, delivery is shifting from labour arbitrage to intelligence arbitrage, empowering enterprises to work with firms that deliver faster, smarter, and more adaptive solutions. We look forward to supporting the Graph team as they continue to scale new heights.”

The seed funding will be used to strengthen product innovation, expand engineering teams, and accelerate go-to-market initiatives. As Graph AI continues to refine its platform and validate results through live deployments, it aims to establish itself as a key player in the future of AI-driven pharmacovigilance.

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