EloElo raises $80 million Series C to scale its AI-driven microdrama platform beyond India
Bengaluru-based EloElo Group has raised $80 million in a Series C led by Hornbill Capital and NBCUniversal. The money will fund original content, international expansion and AI research for its Story TV microdrama app, in a round reported to value the company at $500 million.
EloElo Group, the Bengaluru company behind the Story TV microdrama app, has closed an $80 million Series C round, according to reports published around 4 November. Hornbill Capital and NBCUniversal led the investment.
Outlets including TechCrunch, as relayed by other publications, report that the round values EloElo at roughly $500 million and takes its total funding to about $130 million. Valuation figures come from press reports rather than a company statement, so they should be treated as indicative.
The company says it operates four apps with more than 40 million monthly active users and around 300 million installs, and that revenue has grown several times over in the past year. Those user figures are EloElo's own and vary slightly between reports.
The funding is earmarked for original titles, international expansion and AI research. NBCUniversal has said it will help the company grow beyond India, including into the United States.
Microdramas, short serialised episodes designed for phones, have become a crowded category, and EloElo's pitch leans on AI to cut production cost and speed up content creation.
Why it matters
The round is a rare large cheque for an Indian consumer-AI business, and a Hollywood studio as co-lead suggests Indian short-form content is being treated as an exportable product rather than a purely domestic one.
It also shows investors still backing applied AI in media, even as infrastructure and model-layer funding attracts most headlines.
Indicops view
For Indian enterprises, the lesson is practical: AI pays back fastest where it removes a measurable production bottleneck. Media, marketing and customer-content teams should pilot generative workflows with clear unit-cost targets and human editorial control before scaling.
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