Graph AI Raises $3M Seed to Rewire Pharmacovigilance Workflows

Graph AI closed a $3 million Seed round led by Bessemer to scale Graph Safety — an AI-native platform that automates adverse event processing, signal detection and regulatory reporting in the $8B pharmacovigilance market.

Pharmacovigilance — the continuous monitoring of adverse drug events (ADEs) — is a regulatory necessity and a persistent operational headache for pharma companies. The work spans clinical trials to post-market surveillance and relies heavily on manual review of unstructured sources: call centre transcripts, legal filings, medical literature, emails and even social media posts. Historically, large service firms have shouldered this load with armies of trained reviewers, creating fragmented, slow and costly workflows that elevate compliance risk.

That operational friction is the market Graph AI is targeting with Graph Safety. The startup’s thesis is simple: replace labour-heavy, reactive processes with an AI-native, end-to-end safety stack that centralises signal intelligence, automates case handling and preserves auditability for regulators.

Graph Safety: automation with human-in-the-loop controls

Founded in 2024 and led by Raghav Parvataraju (CEO), Vijay Ponukumati (CTO), Mohan Konyala (CPO) and Ashutosh Bordekar (CFO), Graph AI presents Graph Safety as a context-aware platform that automates core pharmacovigilance functions — ADE case processing, signal detection, aggregate reporting and regulatory submissions — while retaining humans for required compliance checks.

From the company’s perspective, the value is operational: Graph Safety extracts complete data from structured and unstructured inputs, classifies cases consistently, and builds a centralised safety intelligence database over time. The goal is not to eliminate human oversight but to shift reviewers from repetitive extraction to high-value adjudication and regulatory judgement.

Measurable ROI: speed, accuracy and traceability gains

Graph AI’s early enterprise deployments, the company says, show meaningful productivity wins. Customers have reported up to 70% efficiency improvements, 90% faster regulatory reporting, and substantial cost savings — all while preserving traceability and audit readiness. Graph’s pipeline reportedly covers more than 7,000 marketed drugs, signalling demand at scale across safety operations.

For safety leaders, those metrics translate to lower case-processing time, faster signal detection and reduced risk of missed or delayed reporting — outcomes that matter both for patient safety and regulatory exposure.

A critical part of Graph’s pitch is compliance-first design. By keeping humans in the loop for regulatory-mandated steps and providing end-to-end traceability, Graph aims to offer an auditable alternative to dispersed service models. In industries where regulators expect transparency on how signals were derived and cases adjudicated, an AI platform that provides logs, versioning and standardised reports creates a defensible product advantage.

The founders framed the approach bluntly: “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.”

That roadmap — automation plus auditability — is the core proposition investors are backing.

Bessemer Venture Partners led the $3 million seed, and its public rationale echoes a broader investor shift: moving from labour arbitrage toward “intelligence arbitrage” — software that replaces manual effort with automated, explainable intelligence. In a joint statement, Graph’s backer said, “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.”

For Bessemer, the attraction is clear: a sizeable, regulated market where outcomes are measurable and repeatable and where software can substantively compress cost and time-to-signal.

Graph AI’s promise is substantial, but adoption will hinge on practical issues: dataset quality, model explainability, integration with legacy safety systems, and pharma’s willingness to accept automated outputs into regulatory workflows. Validation studies, transparent model documentation, and pilot results with named enterprise references will be critical next steps to build trust across safety, regulatory and legal teams.

The $3 million seed will fund product innovation, engineering hires and global go-to-market — moves Graph says will accelerate institutional adoption of Graph Safety. If the company can substantiate its efficiency and speed claims with verifiable case studies, it stands to carve a distinct position in a market long dominated by manual services.

Graph AI’s seed funding signals investor confidence in an intelligence-first future for pharmacovigilance. The company’s focus—automating routine safety tasks while preserving human oversight for compliance—models a pragmatic path for regulated industries to adopt AI without ceding auditability or accountability. For pharma safety teams racing to scale monitoring across an expanding drug portfolio, Graph’s platform could be the start of a broader shift from manual casework to centralized, AI-driven safety operations.