Agenda

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Rahul
Singhal
Chief Product & Revenue Officer
Innodata
As Innodata’s Chief Product and Revenue Officer, Rahul drives overall product vision, strategy, and marketing to position Innodata as a leading AI/ML vendor. His teams develop cutting-edge generative AI solutions that are productionizing AI for the future. Taking generative AI offerings to new heights, Rahul secured Innodata as a Gartner Cool Vendor and also speaks regularly on expert panels and podcasts. Prior to joining Innodata, he was Chief Product Officer at Equals 3, an AI marketing platform that won several accolades including Gartner Cool Vendor, CES Top 5, and IBM Watson ISV award. Before Equals 3, he spent 12 years at IBM, the last three of which he spent leading the product portfolio for the Watson Platform which included a collection of APIs for vision, speech, data, and language. During his tenure at Watson, he grew usage of the services by over 100X and launched over 15 new services. Prior to Watson, Rahul was a member of IBM’s Strategy and Transformation Practice. Mr. Singhal is also an at New York University (NYU) where he teaches Competitive Strategy and Advanced Experimental Design and Machine Learning.
09 October 2025 11:30 - 12:00
Fireside chat | Coordinated intelligence: Architectures for distributed decision-making
In complex, high-stakes environments, no single agent or human holds all the context - making distributed decision-making a critical capability. This fireside chat explores how AI systems, multi-agent architectures, and human oversight can work in concert to make faster, more informed, and more resilient decisions. Jack, Rahul & their guest (TBA) will discuss practical applications, from orchestration strategies to governance frameworks, that ensure decisions remain aligned, transparent, and effective at scale.
09 October 2025 14:00 - 14:30
Live workshop | Training & refining existing and pre-trained models
This hands-on workshop walks participants through practical techniques for fine-tuning, retraining, and optimizing both custom-built and pre-trained models. We’ll cover approaches for adapting models to domain-specific tasks, improving performance with high-quality data, and mitigating issues such as drift and bias. Attendees will leave with actionable methods and tooling insights for accelerating model refinement in real-world deployments.

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