At the seventh edition of Global Fintech Fest (GFF) 2026, held from September 8 to 11 at Mumbai’s Jio World Centre, YuVerse, the artificial intelligence (AI) arm of fintech infrastructure company Yubi, launched four AI products as it showcased its “last-mile AI” proposition through a series of product demonstrations.. One of India’s largest financial technology gatherings brought together policymakers, regulators, financial institutions, technology companies, startups and investors around the theme of translating emerging technologies into real-world impact.
Yubi, short for “ubiquitous”, describes itself as the technology engine that drives the flow of capital between lenders and borrowers, with a focus on making responsible finance more accessible. Its stated mission is to help bridge the gap between credit demand and supply in India, particularly among businesses that remain underserved by formal lending.
YuVerse, meanwhile, has built its business around what it calls “last-mile AI”: taking AI beyond the model and into the operational processes through which enterprises make decisions, engage customers and deliver services.
“We are a last-mile AI company,” said Mathangi Sri Ramachandran, CEO and Co-founder of YuVerse, in an exclusive interaction with The Economic Times at GFF 2026. “YuVerse is about AI that delivers outcomes that count,” Ramachandran added.
The launches spanned document-to-decisioning, conversational AI, video creation and workflow automation. Rather than treating AI as a capability that sits separately from the enterprise, YuVerse’s products were designed around the processes in which that intelligence would actually be used.
For Ramachandran, that distinction is at the heart of what the company means by the last mile.
“The word last mile came from telco. All the towers were connected, but still the signals were not reaching deep pockets. So telco solved the last-mile AI problem, the last-mile problem, and we have picked up the last mile from there,” she said. The analogy, she explained, was about closing the gap between technological capability and practical use: making large language models (LLMs) useful within real business processes.
