Fergal Reid is the Chief AI Officer at Fin (Intercom), where he scaled the AI team from 10 people to 55. On Chain of Thought he walked through how Intercom cut a $250K-per-month summarization bill by replacing GPT with a fine-tuned Qwen model, and how the surrounding systems, not the core LLM, drove most of the gains in their Fin agent.
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Latest episode with Fergal Reid · EP 49
How Intercom Cut $250K/Month by Ditching GPT for Open Models | Fergal Reid
Fergal Reid, Intercom ·
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In their words
Not directly because it requires a human in the loop and when we're doing RLHF it's offline rather than in production. We don't have an open loop RL in production.
How Intercom Cut $250K/Month by Ditching GPT for Open Models | Fergal Reid
Quotes lightly edited to remove filler words.
Explained on AI, decoded
- Can switching to an open model actually cut your AI costs?
- When should you use a small language model instead of a frontier model in production?
- Are small language models better than large ones for production?
- How do you cut the cost of running an AI agent?
- What is a token in AI, and why does it decide what AI costs?