#artificial intelligence platform

In this episode of TechDogs Discover Dialogues, host Vikramsinh Ghatge speaks with Nalin Agrawal, Director of Solutions Engineering at Dynatrace, about the growing challenges enterprises face in managing modern cloud-native environments. As organizations across BFSI, e-commerce, and digital platforms operate increasingly complex microservice architectures, traditional monitoring tools are struggling to keep pace. The discussion highlights how AI-powered observability is helping businesses improve reliability, reduce downtime, and make more informed operational decisions.

Nalin outlines a three-stage AI framework that is reshaping observability. Causal AI identifies the root cause of issues across interconnected systems in real time, while Predictive AI analyzes trends and patterns to detect potential failures before they become incidents. The final stage, Autonomous Operations, empowers AI-driven systems to take corrective actions such as scaling infrastructure, rerouting workloads, or resolving operational bottlenecks with minimal human intervention.

The conversation also explores the transition from legacy monitoring approaches to observability-first architectures. Rather than treating monitoring as an afterthought, organizations are increasingly embedding observability into the software development lifecycle from the beginning. Nalin also highlights the distinction between AI Observability vs. AI-Based Observability, emphasizing that enterprises must not only use AI to monitor systems but also monitor the performance, effectiveness, and business impact of AI applications themselves.

Google’s Gemini journey reflects a steady evolution from a multimodal chatbot into something far more capable. Gemini 1 introduced native multimodality and long-context understanding, while Gemini 2 added reasoning, tool use, and early agentic behavior. With Gemini 3, Google brings these strengths together, creating an AI that understands context more naturally, reasons in a human-like way, and plans tasks smoothly—marking a real step toward AI that actively supports thinking and decision-making.

Gemini 3 is Google DeepMind’s most advanced model family yet, with Gemini 3 Pro pushing boundaries in reasoning, multimodal intelligence, and agentic execution. It can process text, images, video, audio, and code together, handle complex workflows, and operate across tools with greater autonomy, enabling deeper problem-solving and richer, more interactive user experiences across Google products.

Key improvements include state-of-the-art reasoning with optional Deep Think Mode, enhanced video and audio analysis, advanced document intelligence, and powerful agentic coding capabilities. Gemini 3 can autonomously execute multi-step tasks, support low-code and no-code workflows, and deliver faster insights for business planning and operations. Its 1M-token context window and improved cross-modal reasoning further strengthen its ability to manage complex, real-world scenarios.

AI is rapidly reshaping how we interact with technology, and commerce is next in line. OpenAI’s new feature, ChatGPT Instant Checkout, brings autonomous shopping directly into conversations, allowing users to discover products, ask questions, and complete purchases without leaving the chat. This marks a significant shift from traditional browsing toward a seamless, chat-first buying experience.

The system works through the Agentic Commerce Protocol (ACP), developed with Stripe, which connects ChatGPT to merchant platforms like Shopify and Etsy. When a user expresses intent—such as wanting a specific product—ChatGPT retrieves verified listings, provides relevant options, and enables instant payment within the same interface. No redirects, no forms, just frictionless commerce handled by AI.

Industry experts see this as a major turning point in e-commerce. Brands will now compete within conversational interfaces, where clean product data, authenticity, and contextual relevance dictate visibility. For consumers, it means faster decisions and personalized guidance; for marketers, it means rethinking optimization for AI-driven environments.

In the latest Discover Dialogues episode, TechDogs features an engaging conversation between Matthias Goehler, Chief Technology Officer for EMEA at Zendesk, and host Vikramsinh Ghatge, Sr. Marketing Director and Editor-in-Chief at TechDogs. Together, they explore how artificial intelligence (AI), automation, and empathy are transforming the way businesses deliver customer experiences (CX). As products become increasingly commoditized, Matthias emphasizes that CX has become the ultimate differentiator, shifting power from brands to customers. He explains how this evolution — from Customer Relationship Management (CRM) to Customer Experience (CX) — has redefined loyalty and made experience central to business growth.

Matthias details how CX has evolved from a mere support function into a strategic growth driver, where a single poor interaction can prompt 60% of customers to switch brands. The discussion delves into AI’s growing role in enhancing CX through three layers: AI Agents that automate up to 80% of interactions, AI Co-pilots that assist human agents with contextual insights, and AI in Operations that optimizes analytics, routing, and quality assurance. He stresses that while AI brings speed and efficiency, the real power lies in combining automation with empathy — allowing humans to focus on emotional and high-value customer interactions.

On the question of emotional intelligence, Matthias explains that while AI cannot feel, it can understand sentiment through tone and context. This capability enables it to adapt communication styles and prioritize responses based on customer emotions — paving the way for more human-like, proactive engagement. As a leader, Matthias urges businesses to build technology around customer outcomes, not just features, encouraging co-creation through customer councils and early-access programs that guide Zendesk’s innovation roadmap.

Dell Technologies has announced updates to the Dell AI Data Platform to help customers better support the full lifecycle of AI workloads by enhancing unstructured data ingestion and transformation, to agentic inferencing and AI-powered knowledge retrieval. Key highlights from Dell: Dell and NVIDIA AI Data Platform reference design offers a validated, GPU-accelerated reference architecture for…

In this episode of Discover Dialogues, Vikramsinh Ghatge, Sr. Marketing Director and Editor-in-Chief at TechDogs, sits down with Varun Srinivas, Founder & CEO of Therix.ai and Co-Founder at Coditas, to explore what it truly takes to turn AI prototypes into scalable, real-world solutions.

Over the past decade, Varun has been at the forefront of transforming solid engineering principles into practical AI deployments. In this candid conversation, he breaks away from buzzwords and highlights how AI must be built with people, not just algorithms, at the center.

The discussion kicks off with an eye-opening statement: “AI isn’t coming for your job—but someone using AI might.” Varun quickly dismantles the fear-driven narratives surrounding AI and instead urges leaders to focus on empowering teams, fostering experimentation, and applying disciplined engineering practices.

One standout topic is the rise of “vibe coding”—the fast, intuitive, generative AI-driven approach to development. While this style is excellent for prototyping, Varun stresses that it cannot replace structured engineering. Without robust testing, constant evaluation, and system observability, vibe coding can crumble under real-world production demands.

Top Takeaways from the Episode:

Prototypes ≠ Production: AI isn’t magic; it needs the same rigor as any software product.

Adoption is Cultural: Break the myths around AI and celebrate small, iterative successes.

Clean Code Still Matters: Generative AI doesn’t excuse poor design—clarity, simplicity, and structure still rule.

Empower, Don’t Control: Great leaders guide with curiosity, not heavy-handed oversight.

Agility is Essential: AI strategies must evolve rapidly, sometimes week to week—not annually.

Varun also highlights a crucial but often overlooked point—the difference between good data and usable data. It’s not just about neat rows in a database but providing AI models with timely, relevant context. He advocates for strong feedback loops, collaborative team efforts, and making AI deployment an inclusive, organization-wide journey rather than an isolated technical project.

This episode is essential for anyone navigating:

AI-powered product development

Engineering teams experimenting with GenAI

Enterprises driving AI transformation

Startups scaling MVPs into production-ready systems

Tune in for a refreshing, real-world perspective on what it takes to move AI beyond the prototype—and how culture, code, and collaboration need to align for true success.

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