Fergal Reid

Eoghan McCabe, CEO of Intercom, and Fergal Reid, Chief AI Officer, dive into the creation of their AI customer service agent, FIN. They reveal how FIN achieved a remarkable 65% resolution rate through custom model optimization rather than off-the-shelf solutions. The duo discusses their unique $0.99 per resolution pricing model, aligning customer value with cost. They also tackle the challenges of automation, balancing speed with trust, and the ongoing need for intelligent design in customer service.

This week OpenAI released ChatGPT, its prototype AI chatbot. On this episode of Intercom on Product, Intercom Co-Founder and Chief Strategy Officer Des Traynor sits down to chat with our Director of Machine Learning, Fergal Reid for reaction and analysis on the implications of ChatGPT and what the future holds.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this engaging conversation, Fergal Reid, Chief AI Officer at Intercom and a PhD in machine learning, discusses the challenges of traditional chatbots and introduces Finn, an advanced AI agent designed for customer support. He explains how Finn handles complex tasks like refunds and returns while seamlessly integrating with existing systems. Fergal also highlights the importance of metrics like resolution rate and offers insights on effectively onboarding AI solutions. Their approach balances configuration with a standardized core, ensuring reliability across multiple communication channels.

Fergal Reid, VP of AI at Intercom and the company's first machine learning hire, shares insights on transforming customer service with AI. He discusses Intercom's evolution into an AI-first company and the pivotal role of ChatGPT in this journey. Fergal highlights strategies to mitigate AI hallucinations and the importance of rapid iteration in AI development. He also emphasizes the shift to conversational AI and how it enhances user experiences, illustrating the need for ongoing innovation amid rising AI-native competitors.

Fergal Reid, Chief AI Officer at Intercom who built the Finn system and led their model strategy. He explains swapping GPT for a fine-tuned 14B Qwen to cut a $250K/month cost, the training stack from LoRA to full SFT on H200s, and how re-rankers, retrieval, and query canonicalization drove resolution gains. He also covers A/B testing, the latency paradox, and why vertically integrated AI wins long term.

Fergal Reid, Chief AI Officer at Intercom and a renowned AI strategist, joins VP of Design Emmet Connolly to discuss the evolving landscape of AI. They explore the limits of scaling pre-training, the rise of reasoning models, and the implications of DeepSeek’s industry-shifting optimizations. The duo delves into the shrinking gap between frontier and open models, the importance of the AI layer, and the philosophical underpinnings of verification in AI advancements, emphasizing practical guidance for builders navigating the post-training-first world.

Fergal Reid, VP of AI at Intercom, discusses the impact and evolution of AI in customer service, specifically focusing on large language models like GPD. He highlights the potential of AI in reshaping customer support and emphasizes disciplined development for high-quality AI products.

Fergal Reid, VP of AI at Intercom, discusses the application of machine learning in customer service. Topics include advancements in NLP and ML, challenges in predicting AI technology, and the impact of AI in customer service, including the future potential of autonomous AI bots.

Fergal Reid, VP of AI at Intercom, discusses how the company is using generative AI to transform customer support. They talk about the launch of Fin AI Copilot, which can learn from past conversations and improve agent interactions. Intercom is shifting from chatbots to AI copilots that augment support staff, enhancing efficiency and user experience.

Fergal Reid, VP of AI at Intercom, discusses the evolution of AI models, the capabilities of large language models, the use of vector embeddings in Intercom's database, data privacy concerns with LLMs, impressive results of the Fin chatbot tool, and the occurrence of hallucinations in AI chatbot interactions.