HALL 1 (Main) - Keynotes / Tech Talks

As large language models (LLMs) move from experimentation to production, building reliable and scalable infrastructure has become critical. This session takes a deep dive into the architecture behind modern LLM systems—covering how organizations scale model deployments, intelligently route workloads, and design resilient AI platforms that can handle real-world demand. With a focus on NVIDIA’s approach to resiliency, the discussion will highlight how advanced GPU infrastructure, optimized networking, and fault-tolerant system design help ensure consistent performance even under heavy and unpredictable workloads. Attendees will gain insights into best practices for maintaining uptime, improving efficiency, and building robust AI systems that can support enterprise-scale generative AI applications.

The FMCG industry is undergoing a major transformation driven by data, artificial intelligence, and digital platforms. In this session, we will explore how organizations can reimagine traditional FMCG operations by leveraging advanced analytics, AI-powered decision-making, and connected digital ecosystems. From demand forecasting and intelligent supply chains to personalized consumer engagement, the talk will highlight practical strategies for building agile, data-driven enterprises that can adapt quickly to evolving market dynamics and unlock new growth opportunities.

As AI systems evolve from assistants to autonomous decision-makers, a new paradigm is emerging—Agentic Commerce. In this session, we explore a future where AI agents don’t just recommend products but actively evaluate options, negotiate prices, and complete purchases on behalf of users and organizations.

The talk will examine how businesses must rethink digital commerce when the “customer” is increasingly an intelligent agent rather than a human browsing a website or app. From product discovery and trust signals to pricing strategies, APIs, and marketplaces designed for machines, agent-driven transactions are set to reshape the commerce ecosystem.

Attendees will gain insights into the technologies enabling agentic commerce, the opportunities it unlocks for retailers and platforms, and the challenges around governance, transparency, and user control as AI becomes the new buyer in the digital economy.

As financial institutions move from AI experimentation to real-world deployment, the challenge is no longer building agents — it is running them reliably in production.

In this session, Orkes and StoneX Group Inc. cover the full journey: from traditional workflow orchestration to the next frontier of agentic AI. Orkes will walk through how modern systems are built on Orkes Conductor — starting with the orchestration patterns that enterprises already rely on for durability, visibility, and operational control — and then extending that foundation into agentic use cases with MCP integration, dynamic agent coordination, and governance at scale.

StoneX, a Fortune 50 institutional-grade financial services franchise and Orkes customer, will then share how they put this platform into practice — orchestrating complex, multi-region workflows that span compliance, and settlement across markets. Their story illustrates what becomes possible when a battle-tested orchestration platform handles the hard problems of state, resilience, and auditability, freeing teams to focus on business logic.

Together, this session makes the case that the path to reliable agentic AI in production does not require starting from scratch — it starts with the right orchestration foundation.

This presentation introduces Inya Voice OS by Gnani.ai, an integrated voice AI platform that brings together Speech-to-Text (STT), Text-to-Speech (TTS), and Voice-to-Voice capabilities into a unified system. It explores how combining these components enables seamless, real-time conversational experiences, reducing latency and improving accuracy across voice interactions. The session will highlight the design principles behind building scalable and efficient voice pipelines, including handling multilingual speech, optimizing model performance, and enabling natural, human-like responses. It will also discuss practical applications of such an integrated voice stack in enterprise environments, demonstrating how end-to-end voice systems can enhance user engagement and operational efficiency. Additionally, the presentation will touch upon key challenges in deploying voice AI at scale, such as noise robustness, domain adaptation, and real-world variability in speech. The presentation will also include select case studies illustrating real-world deployments and measurable impact across industries. It will conclude with insights into future directions for voice technologies, including more personalized, context-aware, and adaptive voice interactions

Why the agentic AI era demands a completely different foundation — and what builders in India need to get there. Everyone today is trying to build agents, and most of them will struggle to make it to production. Not because the reasoning is wrong. Not because the model isn’t good enough. But because the infrastructure underneath — fragmented, opaque, and designed for a pre-agentic world — simply wasn’t built to support how agents run.

This session makes the case that the agentic era isn’t just a model problem — it’s a systems problem. It maps what agentic workloads demand: persistent memory, low-latency inference, stateful orchestration, real-time observability, and cost control that doesn’t collapse under multi-agent load. This is the infrastructure conversation the agentic AI era has been avoiding.

Traditional revenue management and marketing science models are increasingly throttled by Human-in-the-Loop bottlenecks and search space explosion where the exponential growth of variables (cross-elasticity, competitor tactics, and channels etc.) and the number of models outpaces manual oversight. Static ML workflows simply cannot scale when every scenario requires human-led iterative corrections.

This session explores the agentic shift where classical ML models are wrapped within a reasoning loop. By treating models as “Tools” rather than final outputs, LLMs act as a central “brain” to decompose high-level goals into executable plans. These systems utilize long-term memory to retain historical context and self-correcting loops to autonomously refine model parameters or flag data anomalies. Unlike isolated model outputs that require human intervention to stay relevant, the autonomous system can perform diagnostic sub-routines, run multiple what-if and optimization scenarios, and automate modeling activities at scale.

As AI agents move into real-world use, the data layer becomes critical to their success. Traditional databases struggle with the real-time, high-concurrency, and context-heavy demands of agentic systems.