At the Snowflake Startup Mixer held on July 17th in Hyderabad, founders of startups, technology leaders, and investors gathered. The central theme of the event was transforming data into decision-making and subsequent actions. During the panel discussion, product demonstrations, and networking, it was discussed how startups are moving beyond simple data collection and analysis to create intelligent systems capable of making autonomous decisions using agentic AI.
A panel session titled 'Startups That Work Around the Clock: Building Companies That Never Sleep' was moderated by Shivani Mutanna, Senior Director of Content Partnerships at YourStory. Participants included Utkarsh Sharma, Associate Creative Director at Pocket FM; Kishor Indukuri, Founder and CEO of Sid’s Farm; Abhishek Deshpande, COO and Co-founder of Recykal; and Kiran Kalluri, Partner at Dallas Venture Capital.
The discussion began with the question of what 'working around the clock' truly means. For the participants, it did not mean employees working 24 hours a day, but rather creating systems that continue to execute critical workflows even after teams have finished their work.
In the case of Pocket FM, which serves an audience across different countries, content production never actually stops. AI has become an integral assistant throughout the entire creative process, accelerating production while maintaining quality. The company uses AI for material generation, asset processing, and production support, while humans remain responsible for quality and narrative.
A similar concept is applied at Sid’s Farm. With thousands of daily deliveries, raw material movements, and customer interactions, data continuously flows from farms and production sites into logistics networks and to consumers. Indukuri explained that AI helps the company interpret this data quickly enough to react before minor operational issues escalate into more serious ones.
At Recykal, the workday begins before dawn when waste collectors start their routes and continues late into the evening as loads move through the city. Every stage generates operational data, providing opportunities to optimize decisions in a supply chain that rarely stops.
From an investor's perspective, Kalluri noted that the key difference lies between companies that merely use AI tools and those fundamentally built around AI-supported operations. If removing AI leaves workflows largely unchanged, the company is primarily using AI for efficiency gains. However, if the absence of AI requires a complete overhaul of the business itself, then the organization can be considered truly AI-native.
As AI integrates deeper into business processes, startups are also rethinking scaling. For Recykal, AI ceased to be viewed as a separate technological initiative and became a corporate requirement. Deshpande stated, 'We have an internal mandate. Every Monday there must be a new AI initiative. We must discuss it, and anyone can propose it, from an intern to a CXO.'
He explained that the company mapped every business function, identified repetitive processes suitable for AI, and used years of operational data to redesign workflows. The results were significant: Recykal increased annual revenue from 400–500 crore to 1,400 crore while growing its staff from approximately 80 to 128 employees.
At Pocket FM, Sharma emphasized that management positioned AI as a support tool, not a replacement for creative specialists. The company invested in AI-powered audio, video, and image generation tools while ensuring that humans continued to perform quality checks and maintain the emotional experience expected by viewers. Creating stories consisting of hundreds or even thousands of episodes still requires human judgment, with AI accelerating production rather than replacing creativity.
At Sid’s Farm, AI implementation efforts are focused on consumer-facing operations. Indukuri noted, 'Where we actively use AI, or the area we constantly monitor, is the consumer side.' The dairy industry generates a massive amount of operational data daily. AI increasingly helps forecast demand, reduce waste, optimize production planning, and dynamically adjust inventory across delivery channels.
Kalluri added that these examples demonstrate what distinguishes enduring companies that use AI from enterprises that merely wrap existing language models. He warned, 'If there is no ability to capture these processes, those are red flags we see. And those are not companies that will be able to scale and grow. They might succeed with one specific client in a very niche case, but if they need to expand their business and attack the entire TAM, it becomes very difficult if they lack these foundations.'
For investors, stronger indicators of long-term scalability are owning differentiated data, building robust workflows, and embedding AI into all products, go-to-market functions, and internal operations, rather than just branding the company as AI-oriented.
As startups automate more workflows, the question of where AI should end and where humans must retain control becomes increasingly important. The panelists agreed that while AI can significantly accelerate task execution, critical business decisions still require human oversight.
Deshpande cautioned against accepting AI outputs as infallible. 'I feel that AI is a 'yes-giver,' so we must be very smart. Human intervention is needed. You cannot replace people. Fundamentally, you must clearly understand what you want.'
Indukuri reported that Sid’s Farm integrates AI into recruitment, customer support, and operational planning, but believes AI should first handle routine queries before escalating complex situations to humans. For example, customer support can automatically answer questions about delivery or order status, while dissatisfied customers or quality issues should be routed to human teams. Instead of replacing employees, AI allows them to focus on problems requiring judgment, empathy, and context.
The discussion also highlighted that technology alone is insufficient to define startup success. Founders need disciplined organizations, reliable processes, high-quality data, and a clear understanding of where AI creates real business value. Deshpande observed that AI is already changing the approach startups take to talent: 'I have seen an intern deliver better results than someone with 10 years of experience. The younger generation adapts faster.'
He added that founders themselves must become active users of AI before expecting their teams to adopt new tools. Success depends on defining the right business problems first; only then can AI yield meaningful results instead of adding unnecessary complexity.
The evening concluded with a live demonstration titled 'Blueprint: From Data to Action with Agentic Workflows,' presented by Akshat Parik, Harish Chintakunte, and Navedea Odja from Snowflake. The session demonstrated how startups can significantly reduce the time required to build AI-based applications by combining enterprise data with agentic AI capabilities. Using Snowflake Cortex Code, a specialized AI coding agent for data workloads and AI, the speakers showed how developers can move from a natural language prompt to a production-ready application in minutes.
A use case example for fraud detection in the fintech sector illustrated this process. One prompt generated an application that ingested customer and transaction data, calculated fraud risk scores, created dashboards, and formed an AI-driven investigation workspace. In addition to dashboards, users could ask questions about fraud trends, customer behavior, and transaction patterns in natural language. AI broke down these queries into multiple reasoning steps before generating actionable insights.
The team also highlighted the flexibility of Snowflake models. Instead of locking organizations into one base model, the platform supports several leading models, including Claude, Llama, DeepSeek, Mistral, and OpenAI, allowing businesses to choose the appropriate model for each task while keeping data within a secure governance system. One message remained clear throughout the evening: AI is no longer just helping startups work faster. It is increasingly becoming part of how enterprises are designed and function. Startups are beginning to embed AI into the core of their products, workflows, and daily decision-making, rather than viewing it as a standalone tool.