Day 2

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.

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.