From Udaipur To Enterprise AI: How Kipps.AI Is Building AI Agents For Everyday Business Conversations
As enterprises look beyond generative AI demonstrations toward tools that can perform actual business tasks, Udaipur-based Kipps.AI is betting on a simple idea: businesses should be able to deploy AI agents that talk to customers, follow up with leads and complete routine workflows without requiring their own AI engineering teams. The first wave of generative AI was largely about answering…
Kipps.AI, an AI-agent platform founded in 2024 by Nishit Chittora and Aditi Mishra in Udaipur, Rajasthan, is pioneering agentic AI for everyday business conversations. The company aims to help enterprises deploy AI agents that can interact with customers across various channels, such as voice, WhatsApp, and web chat. Unlike traditional chatbots, Kipps.AI's agents can interpret natural-language questions, retrieve information from company knowledge bases, maintain conversation context, and perform actions through integrations with business software.
Traditional chatbots have been programmed with predefined decision trees, but large language models now enable AI agents to take on more complex tasks, like handling phone calls, responding to messages, qualifying leads, scheduling appointments, and interacting with internal business systems. This shift from conversational AI to agentic AI presents opportunities for a new generation of Indian startups, such as Kipps.AI.
The founders chose Udaipur as the company's base because they believed businesses were losing potential customers due to the inability of sales and support teams to respond instantly at all hours. Their vision, akin to the AI assistant TARS from the movie Interstellar, is to create an always-available AI layer that handles repetitive conversations while escalating situations requiring human judgment or intervention.
Voice AI could be a particularly significant battleground in the enterprise AI space. While much attention has focused on text-based assistants, voice AI applications may prove more consequential for businesses. A capable voice agent could understand customer requests naturally, answer questions using company information, and initiate appropriate next actions.
However, developing voice AI systems presents greater technical challenges than text-based assistants, including speech recognition, language-model inference, retrieval, business logic, speech generation, and maintaining low latency while ensuring reliability.
Written by urgent.news from Free Press Journal's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
