HALL 3 - Tech Talks

ADM – Founder’s Voice
Redefining the Future of Data with Agentic Intelligence
In this special Founder’s Voice session, Raghu Mitra shares the vision, philosophy, and engineering journey behind Acceldata’s evolution from data observability to Agentic Data Management (ADM). As enterprises scale AI, analytics, and data-driven decision-making, traditional monitoring and reactive governance are no longer sufficient. The future demands systems that do not just observe data—but understand, reason, and act.
ADM represents this next frontier. Built on the foundation of ADOC, ADM introduces autonomous, AI-powered agents that continuously monitor data ecosystems, diagnose issues with contextual intelligence, and execute corrective actions with minimal human intervention. In this session, Raghu explores the architectural thinking, real-world challenges, and breakthrough innovations that shaped ADM into a self-driving data management platform.
Attendees will gain insight into:
The shift from reactive observability to autonomous data operations
How agentic systems transform reliability, quality, and governance
The engineering principles behind scalable AI-native data management
The long-term vision for intelligent, self-healing data ecosystems
This session is not just a product overview—it is a forward-looking perspective on how agentic intelligence will redefine enterprise data strategy in the AI era.

Artificial intelligence is now core infrastructure. Yet many AI workloads operate on shared public platforms, creating exposure risks for proprietary data and strategic intelligence.
Zero-Leak AI Infrastructure delivers dedicated, private AI compute environments built on isolated GPU servers and controlled networking. No public exposure. No uncontrolled egress. No shared tenancy.
Secure AI begins with sovereign infrastructure.

Engineering for Human-Like, Multilingual Voicebots for Bharat explores how voice-first AI systems can be designed to serve India’s linguistically diverse and mobile-first population. Drawing from his experience building large-scale, real-world platforms, Kiran Kumar Katreddi will discuss the engineering foundations behind creating voicebots that feel natural, conversational, and inclusive across multiple Indian languages and dialects. The session will cover how technologies such as speech recognition, natural language understanding, and text-to-speech come together to handle code-mixed speech, regional accents, and low-resource languages, while operating at scale with low latency. Kiran will also highlight the unique challenges and design considerations specific to Bharat, and how human-like multilingual voicebots can significantly expand digital access, improve customer experiences, and enable intuitive interactions for users beyond English-first, text-based interfaces.

Building AI-First Operating Models explores how organizations can move beyond isolated AI initiatives to embed intelligence at the core of their operating model. In this session, Abhishek Singh shares a practical and strategic view on designing AI-first enterprises where data, models, and decision intelligence are tightly integrated into everyday workflows. The discussion will cover the shift from traditional automation to AI-led orchestration, key principles such as data readiness, scalable AI infrastructure, human-in-the-loop governance, and cross-functional collaboration, along with real-world examples of how AI-first models are driving measurable impact across operations, customer experience, and decision-making. Attendees will walk away with a clear framework to transition from experimentation to enterprise-scale AI adoption and build resilient, future-ready operating models.

When building Agentic AI systems, decision architecture is not a technical afterthought—it is the foundation of scale, trust, and long-term adoption in real-world environments.

The Problem: A brief case study highlighting the consequences of non-reproducibility in AI decisions.

The Challenges: Real-world complexities of maintaining transparency in multi-agentic systems.

The Technical Roadmap: Methods and tools to ensure GenAI systems are fully auditable.

The Decision Rationale: A deep dive into capturing decision snapshots, logging execution paths, and environment versioning, supported by a technical architecture diagram.

Implementation Strategies: Practical takeaways including structured logging, deterministic replays using fixed seeds, shadow-mode testing, and immutable audit trails.

The talk focuses on one of the hardest problems in fashion recommendation systems—new users and new items in rapidly changing catalogs—and how recent advances in large language models enable fundamentally different approaches to representation, understanding, and bootstrapping recommendations at scale.

The session will share practical system designs, trade-offs, and lessons learned from using LLMs to address cold start across candidate generation and ranking, including how we combine textual, visual, and contextual signals to reduce dependence on historical interaction data. The emphasis will be on what translated to measurable online impact, and where LLM-based approaches helped—or failed—compared to traditional heuristics and embedding-based methods.

I believe this talk would resonate well with ML practitioners, recommender system engineers, and applied researchers, and would complement the conference’s focus on recommender systems, applied machine learning, and real-world deployments.

As organizations move from standalone LLM applications to complex, agentic AI workflows, LLMOps becomes the critical backbone enabling scale, reliability, and trust. This session explores how to design robust LLMOps frameworks to build, monitor, and govern multi-agent systems in production. It will cover practical approaches to orchestration, observability, evaluation, cost control, and risk management, along with governance strategies to ensure compliance, safety, and responsible AI at scale. Attendees will leave with actionable insights to operationalize agentic AI systems that are resilient, transparent, and enterprise-ready.