YuVerse takes its last-mile AI proposition to Global Fintech Fest

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.

From documents to decisions, conversations and workflows

One of the company’s key launches at GFF was Yu1, a platform designed around document-to-decisioning use cases such as underwriting and claims processing.

A lending decision can involve a chain of people and processes, from a relationship manager or salesperson to a credit analyst, underwriter and credit committee. Yu1 seeks to turn that physical journey into an AI-driven workflow. “We are bringing that physical journey to an AI transformation journey and then powering these to multiple facets of AI, starting with the engine on OCR and vision, as well as on the communication layer being powered by voice and video,” as Ramachandran put it.

The platform starts with the documents required for a decision. AI agents analyse what has been submitted, identify what is missing and initiate communication with the customer to collect additional information. The documents can then be reloaded and reclassified, with a credit analyst agent extracting relevant information, preparing a credit assessment memo (CAM) and taking the case through the credit committee process.

The proposition is not simply about replacing individual tasks with AI, but about connecting them into an end-to-end process.

“The terrain of the last mile is rugged and filled with a lot of uncertainties,” Ramachandran said. For enterprises, she argued, the challenge was not simply to “throw a model at the problem”, but to first understand the existing process, establish the standard operating procedure (SOP), work with stakeholders to standardise it and only then begin automation.

That emphasis on implementation also ran through YuBuild, the company’s conversational AI platform.

YuBuild is designed to let enterprises build conversational bots around specific business objectives without having to construct every element of the workflow manually. The process begins with an idea or an intended outcome. A prompt builder then probes for edge cases and requirements, including, in a collections use case, the desired outcome, the number of persuasion attempts and how the system should respond to abusive or unexpected questions.

From there, graph agents construct the conversation, while quality agents test it through automated bot-to-bot interactions before the workflow is deployed across channels.

“YuBuild democratises the building of a conversational bot,” Ramachandran said.

The company said it had already seen an impact in customer collections. “In terms of replacing telecallers, we have been very successful at doing it and automating conversations at scale,” Ramachandran said. “This is something that I always say humans are incapable of doing empathy at scale where machines can. Our collection efficiencies in many cases have gone up 50% over the human agent. So we are not even talking about matching humans anymore. We are seeing how much it can do better,” she said.

The point, in her framing, was not simply that machines could handle more conversations. By carrying context across channels, AI could also reduce the friction of customers having to repeat their histories every time they moved between an automated system, a call centre or another service channel.

That same idea of removing the conventional infrastructure around a task extended beyond financial services with YuBuild Studio, the company’s AI-powered video creation platform.

“Anybody can have an idea, somebody can have a story to say, but how do we make sure the idea and the story reaches the audience that you want?” Ramachandran said. “Should you want an entire crew behind you? Should you build a movie? Should you put a production cost on it? All of that YuBuild Studio takes away.”

The platform is designed to handle elements traditionally associated with video production, including camera angles, lighting, actors and continuity, allowing a user to move from a single idea towards a finished video without assembling a conventional production team.

The fourth launch, YuFlux, extended the proposition into workflow orchestration. Rather than building a separate system for every business process, the platform was designed to let users describe what they wanted to accomplish and have AI agents assemble and execute the workflow.

“There are stories to say, there are bots to be built, there are journeys to be conquered. So we launched something called YuFlux, which encompasses all of this,” Ramachandran said. “In a way, these are the workflow engine, any process, it could be an operation process of an invoicing, or it could be a credit decisioning journey, or it could be an insurance onboarding journey.”

“All you need is to tell the interface what you want to do, and from there, the agents take over, they kind of understand your requirements in depth, puts all the nodes together, builds a workflow and runs them at scale, and then you can deploy it for any of your business process,” she added.

Why financial services remain a critical test

The company’s focus on financial services also reflects the sector’s long history of adopting technology to solve problems of scale.

“I think the financial ecosystem is at the crux of adoption and it has always been like that,” Ramachandran said. “Be it in the late 1800s, when the credit bureaus kind of started coming in. Till now, financial companies have been at the forefront of technology adoption. They are also at the forefront of AI adoption today,” she added.

GFF itself reflected that wider shift. The official agenda placed Agentic AI at the centre of discussions around autonomous financial workflows, personalised services and real-time decision-making, while separate tracks examined AI across lending, banking, rural finance and economic empowerment.

Ramachandran said that the event provided YuVerse with a way to test its proposition against the market. “It gives you a perspective on where the market is, how you get your product-market fit,” she said. “I think some of the products that we have done here have shown a lot of traction and motivates us to do more.”

The wider question, however, was not limited to enterprise efficiency. For Ramachandran, the ability to take AI into the operational layers of financial services also has implications for access.

At the opening of GFF, Prime Minister Narendra Modi called on the fintech sector to move beyond payments into areas including credit, insurance, savings, investments and pensions, while emphasising the need to translate technologies such as Agentic AI into real-world impact.

Ramachandran linked that broader ambition to the role she sees for AI in financial inclusion.

“Technology, I surely believe, exists to reduce the difference between the haves and the have-nots, the true democratising tools,” she said.

That, ultimately, brought her back to the question underlying YuVerse’s last-mile proposition: “How do we make credit decisions reach the last mile? How do we make finance more accessible to the person sitting in one corner in a village? I think that’s what AI is about,” Ramachandran said.

For YuVerse, the answer is not another model layered on top of an existing process. It is the work of taking AI through the messy middle, including documents, conversations, exceptions, workflows and human decisions, until the technology produces something that an enterprise can actually use.