In this episode of the Agentic AI Podcast by lowtouch.ai, host Rejith chats with Vinod, a seasoned IT leader with 25+ years in tech transformations at giants like Fidelity and MassMutual. As a fractional CTO, Vinod shares actionable insights on making your data platform AI-ready—from clean data and modern architectures to governance and security. Discover why AI thrives on trusted data, how platforms like Microsoft Fabric, Databricks, and Snowflake stack up, and practical steps to move from AI pilots to enterprise-scale impact.
Whether you're a CIO, CTO, or business leader, learn how to prioritize high-value use cases, avoid common pitfalls, and build responsible AI without disrupting operations.

In this episode, we explore LMCache, a powerful technique that uses caching mechanisms to dramatically improve the efficiency and responsiveness of large language models (LLMs). By storing and reusing previous outputs, LMCache reduces redundant computation, speeds up inference, and cuts operational costs—especially in enterprise-scale deployments. We break down how it works, when to use it, and how it's shaping the next generation of fast, cost-effective AI systems.

In this episode, Rejith Krishnan and Biji Thomas discuss how the UAE is leveraging AI to transform key sectors like oil & gas, tourism, and logistics. From predictive maintenance in energy infrastructure to personalized travel experiences and autonomous supply chains, AI is fueling innovation across the Emirates. We also examine the unique challenges—like data localization, talent gaps, and regulatory frameworks—that shape AI adoption in the region. Tune in for a deep dive into how the UAE is positioning itself as a global AI hub.

In this episode, we uncover the common reasons behind the high failure rate of agentic AI projects—from unclear objectives and poor context design to lack of integration and human oversight. We break down real-world pitfalls, offer practical strategies for success, and share a framework for building scalable, reliable agent-based systems. Whether you're launching your first AI agent or scaling enterprise automation, this episode will help you avoid costly mistakes and unlock the full potential of agentic AI.

In this episode, we explore how the Opera Neon AI browser is transforming the web experience with built-in intelligence. From contextual search and AI summaries to task-based browsing and voice-driven commands, Opera Neon blends AI into every interaction. We discuss its sleek visual design, productivity-focused features, and how it challenges traditional browser models. Tune in to discover how AI-powered browsing is evolving from passive navigation to proactive digital assistance.

In this episode, we explore how leading food brands Such as Pepsi, Nestle and others are using AI and data analytics to decode consumer behavior—tracking preferences, predicting trends, and personalizing marketing in real time. From analyzing purchase patterns to optimizing flavor development and packaging, we break down how AI-driven insights are helping brands stay ahead in a fast-changing market. Tune in to discover how the food industry is blending data and taste to better serve today’s conscious, connected consumer.

In this episode, we compare two leading frameworks shaping the future of agentic AI: the LangChain Agent Protocol (LCAP) and the Open Agent Protocol (OAP). We explore their architectural differences, interoperability, community support, and real-world applications. Whether you're building custom agents for enterprise automation or integrating open-source agent systems, this episode will help you understand the strengths and limitations of each approach—and how to choose the one that fits your AI strategy.

In this episode, we break down the key differences between Large Language Models (LLMs) and Small Language Models (SLMs). While LLMs like GPT-4 dominate with broad capabilities and massive datasets, SLMs are emerging as lightweight, cost-efficient alternatives optimized for specific tasks and edge deployments. We explore their use cases, trade-offs in accuracy vs. speed, and how enterprises can choose the right model based on context, control, and cost. Whether you’re building AI at scale or deploying on-device assistants, this episode helps you navigate the evolving landscape of language models.