Regulation FD Disclosure (Form 8-K)

On April 7, 2026, Roadzen Inc. (the "Company") participated in the Maxim Group "Powering the AI Revolution" Virtual Conference. During the presentation, the Company's Chief Executive Officer, Rohan Malhotra, discussed the Company's business, financial trajectory, competitive positioning, and market opportunities. Among other things, Mr. Malhotra discussed that (i) the Company currently operates at an annualized revenue run rate of approximately $60 million, and is targeting a run rate of approximately $100 million, or approximately $25 million per quarter, within the next six to twelve months, representing growth of more than 50% on an annualized basis, and (ii) the Company anticipates reaching adjusted EBITDA breakeven in the current quarter. Mr. Malhotra also provided an overview and update on the Company's AI architecture, technology platform and certain regulatory matters, including the following information:

AI Architecture and Competitive Differentiation

The Company is a founding member of the AI Alliance, an organization comprising approximately 25 of the world's leading artificial intelligence research institutions, including Meta, IBM, Uber, and ServiceNow. The Company's contribution to the Alliance-and its core strategic differentiation-is the deployment of AI in production environments at scale.

The Company does not develop large language models or engage in token-based AI business models. Instead, the Company develops specialized, high-precision models purpose-built to perform specific tasks-such as underwriting risk assessment, claims adjudication, and real-time driver behavior analysis-with a high degree of accuracy, which is well above 90%. These models are deployed to support real-money decisions: underwriting a commercial auto insurance policy, adjudicating a claim, or assessing road risk in real time. The cost of AI inference in such a context is de minimis relative to the economic value of the decision being supported.

This approach is supported by more than a decade of investment in proprietary data infrastructure, beginning in 2015 and 2016, encompassing large-scale data ingestion, continuous model training, and production deployment capabilities. The Company has developed more than 300 proprietary AI models and holds data spanning the full insurance value chain, including driving behavior, claims economics, and vehicle repair cost data.

The Company believes that assembling a comparable data asset base-including over 100 million historical insurance claims and approximately 4 billion real-world driving miles, along with the associated data labeling, model training, and production deployment infrastructure-would require a third party approximately three to five years and substantial capital investment to replicate at comparable scale and functionality.

Technology Scale and Measured Performance Outcomes

The Company's AI platform is underpinned by proprietary data assets and production deployment at significant scale:

The Company's DrivebuddyAI platform has demonstrated a 72% reduction in accident rates in the first year of deployment, based on fleet customer data. The Company's AI-powered underwriting and claims platform has achieved an estimated 10 percentage-point improvement in combined ratio performance relative to the industry average, based on internal data. For reference, the average U.S. auto insurance combined ratio currently stands at approximately 104%, meaning that for every $100 of premium collected, the average carrier pays out $104 in claims, distribution, and administrative costs. The Company's platform targets and has delivered measurable improvement on this metric for its enterprise partners.