Learn more about Fin's evolution to a Customer Agent →

The highest performing AI Agent just got even better →

We made some big product announcements at Pioneer 2025. We launched Fin 3, the best-performing and most omnichannel AI Agent for customer service. With Fin 3 we've made deep investments in all four stages of the Fin Flywheel so you can: • Train Fin with Procedures • Test it

The @intercom's Pioneer 2025 Conference was a success and mind-blowing one. Will say this has to be the highlight of the month already. For those in the customer service niche, you all can agree with me that Intercom is one of the industry leaders when it comes to quality

Delivering real value to engineering organizations is a very deep product problem. @silasalberti , Founding Team at @cognition, says it started with the infrastructure that lets agents run in enterprise environments, from virtual machines to the plumbing that connects with AWS,

Building AI agents that actually solve customer problems end-to-end is hard. Join us in Amsterdam for a fireside chat with Fergal Reid, Chief AI Officer at @intercom, as he shares how they built Fin—their AI agent that's transforming customer service. He'll walk through: - Why

There’s no easy component in building frontier AI products. @Harvey’s Head of Applied Research Niko Grupen (@nikogrupen) says the difficulty of an AI problem can be framed by asking tough questions: How much domain expertise is required? How verifiable are the outcomes? How much

Last week in San Francisco, we teamed up with @figma for a meetup on what it really takes to build AI products that customers can trust. Onstage, Jordan Neill (SVP of Engineering, @intercom, building Fin) and Marcel Weekes (VP of Engineering, Figma) compared notes on how

.@eoghan, CEO of @intercom, and @fergal_reid, Chief AI Officer, join @labenz on @cogrev_podcast to discuss how they built Fin—the AI customer service agent that went from 35% to 65% resolution rate over 30 months while pioneering outcome-based pricing at 99¢ per resolution. They

The new AI agents for customer support are incredible. I'm amazed by how well they work. We spent about a month training Intercom Fin using our documents and internal knowledge base. After activation, it's already handling 50% of our tickets 🤯 We have plenty of improvements

Last week in Paris, we partnered with Dust for a meetup on how AI Agents are moving from answering simple questions to reliably handling complex workflows. Onstage, Gabriel Hubert (CEO & Co-founder, Dust) and Christopher Dalley (Director of Product Management, @intercom,

Imagine telling your investors: “We’re giving up $50M to rebuild.” That’s what @Intercom did when they built Fin (their AI that now resolves 65% of customer conversations) I sat down with their co-founder @DesTraynor to ask why they made that bet. Here are 3 lessons I took

Reliability is what takes a product from average to great. As Brett Chen, Tech Lead and Member of the Technical Staff at @perplexity_ai, explains, anyone can build a demo that works 50–60% of the time. But hitting 90%, 95%, or even 99% success requires optimizing for quality,

We see this pattern often. A few forward-thinkers try something new. They roll out @Fin_ai , prove it works, and share the results. (72% resolution rate, incl. high-value, multi-step queries.) Others follow. This is how AI change happens: Proof > promise.

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Next week, Pioneer 2025, the summit for AI customer service leaders, lands in New York City. Join leaders from Anthropic (@AnthropicAI), Boston Consulting Group (@BCG), Crypto .com (@cryptocom), Rocket Money (@RocketMoneyApp), Toast (@ToastTab), and more to explore the

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The “Organizational and System Design” chapter of The AI Agent Blueprint has practical guidance and real examples for leaders asking: “How do we build the internal structure and alignment AI needs to scale?”

As @nrmehta says: “AI is the biggest change management need in human history… we are requiring people to completely rethink the way they do things.” The teams getting this right: → Make an effective case for change → Evolve their team with AI-focused roles → Build systems

Scaling AI is more than a tech decision. It’s a shift in how your team works, collaborates, and improves over time. To realize the full value of AI, support orgs need to rethink: ➤ Team structure ➤ Roles and ownership ➤ How change happens