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For most companies, technology transformation is a milestone. For Vymo, a Bengaluru-based sales and distribution platform that now supports digital collections workflows, it has been a natural evolution—one guided by a central question: How can we improve the productivity and effectiveness of the average seller?

When Vymo launched in 2013, smartphones were being integrated into enterprise workflows. The biggest bottleneck faced by a salesperson was understanding why it was necessary to visit a branch to acquire leads or to spend hours on coordination, when that time could be better allocated to meeting with clients.

A decade later, that problem statement remains unchanged, but the tools have evolved. With AI and large language models (LLMs), the industry has shifted from activity-based tools to outcome-based intelligence. Earlier, sellers did the work and used tools for support. Now, they can state the outcome they want and let AI figure out the best path.

Venkat Malladi, co-founder and  CTO at Vymo, says, “We simply rode the wave of AI. It was the natural next step for distribution, sales, and collections.”

AI and Insurance Sales Cycle

To understand the shift, Malladi breaks down the daily reality of an insurance salesperson. To meet a monthly commission goal of ₹50,000, a salesperson typically needs a well-filled funnel, comprising around five policy sales, which stem from roughly ten proposals, supported by 15–30 meetings and approximately 100 calls, all built on a pipeline of nearly 200 prospects.

Their day spans adding prospects, following up, handling applications, and juggling micro-tasks. Historically, this meant switching between multiple tools, WhatsApp, lead systems, follow-up tools, CRM dashboards, reference apps, and more.

That’s changing now. Malladi explains, “Just like programmers say ‘build this function’ and AI generates code, sellers will soon say, ‘here’s my target, give me a plan.’” 

AI agents will assume a wide range of responsibilities, including understanding context, recommending suitable products, following up with prospects, developing proposals, coordinating with carriers, and prioritizing tasks to ensure smoother and more efficient sales processes.

Sellers will operate with multiple AI agents working in parallel, some interacting with customers, some preparing paperwork, and others optimizing leads.

This transformation isn’t limited to the insurance industry. It’s sweeping across industries.

AI Agents and Customer Support

A few months ago, SquadStack.ai launched what it calls the future of customer support, an AI-led, fully autonomous support ecosystem that can resolve up to 80% of queries independently. 

The platform’s features include human-in-the-loop refinement, omnichannel engagement, deep integrations for context-driven responses, enterprise-grade security, and ROI optimisation through AI-driven workflows.

Co-founder & CEO Apurv Agrawal frames it as the beginning of a new era.

“Passing the Turing Test wasn’t the finish line; it was the starting line.

The next 24 months are not about matching humans; they’re about outperforming them.”

SquadStack believes AI will soon lead over 80% of all contact-center conversations and deliver 10 times better outcomes through sentiment analysis, adaptive tone control, and context-aware responses.

Building AI Agents Across Sales, Distribution & Collections

Vymo is developing its own suite of AI agents across all product lines. In collections, the transformation is evident: low-risk defaulters can be nudged with simple digital reminders, while mid-risk profiles that were traditionally handled by telecallers can now be managed by AI calling agents. 

Field agents benefit from AI-assisted day planning, prioritization, and visit preparation, and high-risk customers are directed into settlement or legal workflows.

“This entire spectrum, from digital nudges to field visits, needs seamless flow. And AI agents can improve every interaction,” Malladi says.

Vymo’s differentiation lies in two core layers. First is vertical integration, where the company owns its data, LLM infrastructure, calling capabilities, and workflow engines from end to end. 

The second is its breadth across the customer lifecycle. While most companies focus on a single slice, such as calling agents, enterprises need seamless movement across messaging, calls, field operations, legal processes, and final closure, supported by intelligence that cuts across sales, distribution, and collections.

“End-to-end ownership of the journey is the real differentiator,” Malladi says.

Case Study: Collection for Banks

For banks, collections are a cost-versus-risk game—messages are cheap, calls cost more, and field visits are the most expensive.

Vymo’s collections suite helps banks manage this entire pyramid efficiently, blending digital nudges, AI calling, and field optimization.

With telecalling roles increasingly being replaced by AI agents that mimic human conversation, collections is one of the fastest-moving AI adoption areas in financial services.

Interestingly, Malladi says Vymo’s biggest competition isn’t another company, it’s the internal decision many enterprises face: Do we build this ourselves or work with an external partner?

While some companies provide components, like commission management or standalone calling agents, very few offer: full distribution lifecycle management, complete end-to-end collections, and integrated intelligence across sales, distribution, and collections

This positions companies like Vymo as a partner whose value is measured by ROI versus internal builds.

What’s Next?

The future of enterprise workflows is clear: AI agents that don’t just assist, but own outcomes.

Just as developers now manage AI agents instead of writing boilerplate code, frontline workers will manage AI agents instead of juggling hundreds of micro-tasks.

And the companies that can build vertically integrated, full-stack, outcome-driven, agent-based systems will define the next decade of enterprise transformation.