Day 2

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.

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.