Bengaluru, Mar 06: CheerioAI, an AI-powered all-in-one customer engagement platform focused on optimizing the CAC-to-LTV equation, has raised ₹8 crore in a Seed funding round led by Artha Venture Fund II (AVF II). The round also saw participation from Hyderabad Angels, TiE Angels, LetsVenture, VC Mint, Invention Engine, and notable angel investors, including Arjun Vaidya, Anand Shahni (WedMeGood), Vivek Mathur (ex-Elevation Capital), Ajeet Khurana, along with early customers such as Habuild and Nitin Verma (InstaAstro).
From left to right: Nishant Das co founder,Avinash Upadhyay -co founder,Priam Jain-Cto and co founder
The funding will be used to develop multi-modal AI capabilities, including a proprietary small-model LLM for ad generation, expand CheerioAI’s platform from messaging into voice and video, scale AI-driven automation, and strengthen enterprise-grade capabilities to help businesses drive higher engagement, faster growth, and improved ROI while ensuring security and compliance.
CheerioAI’s revenue has grown 450% year-on-year over the past two years. Over the next six months, the company plans to expand its team across engineering, AI, enterprise sales, and customer success to support scaling and global expansion.
Speaking on the announcement, Nishant Das, Co-founder of CheerioAI, said:
“Avinash, Priam, and I are thrilled to partner with Artha and our co-investors. This investment strongly validates our vision and will help us deepen our AI-driven automation capabilities while accelerating expansion into the enterprise segment. At CheerioAI, we envision a future where businesses operate with just three employees — the business founder, the technology founder, and CheerioAI. This funding is a crucial step toward making that vision a reality.”
CheerioAI addresses a major challenge faced by modern enterprises — fragmented communication across multiple digital channels, which often leads to inconsistent customer experiences, higher churn, and operational inefficiencies. The platform unifies marketing, support, and sales workflows across WhatsApp, email, SMS, social messaging, and in-app communication, enabling businesses to automate customer engagement and improve lifetime value.
Commenting on the investment, Anirudh A. Damani, Managing Partner at Artha Venture Fund, said:
“Our decision to invest in CheerioAI is anchored in our strong conviction around the long-term opportunity this team is building. As enterprises operate across an increasingly complex set of digital channels, customer retention and lifetime value optimization have become mission critical. Cheerio’s AI-native approach to orchestrating customer engagement positions it to become a category-defining platform.”
He further added that the round received strong ecosystem validation, with the opportunity referred by one of Artha’s LPs and additional investments coming from founders within its portfolio.
Varun Aggarwal, Founder of Change Engine at Invention Engine, said:
“At Invention Engine, we stand behind exceptional founders taking bold product bets that reshape enterprise workflows. Cheerio’s team demonstrated both ambition and execution, and we believe their AI-led approach to customer engagement will redefine how enterprises drive retention and lifetime value.”
Currently, CheerioAI works with 150+ enterprise customers across six industries and has generated over ₹500 crore in additional revenue for its clients through intelligent retention automation. With growing demand for scalable AI-driven engagement solutions, the company aims to become the default platform for customer lifecycle orchestration, enabling businesses to transform fragmented interactions into seamless, outcome-driven engagements.
Looking ahead, CheerioAI plans to expand its AI capabilities across automation, agentic workflows, and performance optimization, while maintaining a modular architecture that can integrate with evolving AI models. The company aims to empower enterprises globally by automating the majority of customer touchpoints, improving retention, and maximizing lifetime value.