Watch: Demos and Prototypes
At Intercom, we’re experimenting with new products and features every day, especially with AI. We’re exploring what the latest AI models can/can’t do.
We are now sharing some of the work in progress to give people a sense of what we’re up to, and maybe start a discussion with us. We’d love that!
It lets you see exactly how Fin will respond to any of your actual customers, so you can test with confidence before going live. It’s in closed beta for the moment, but Ben has recorded this demo you can watch.
Fin AI Agent as MCP Client
Earlier today we shared how Intercom is using MCP to give AI systems like Claude access to real-time customer data from Intercom. Great for teams without access to our helpdesk to benefit from a wealth of customer data.
Product Engineer Luis Alvarez Aguilar has been working on the next step in that journey: making Fin act as an MCP client so it can connect with third-party and internal business tools, access data, and take action for customers.
In this demo, Luis shows how Fin connects to a Stripe MCP server to:
✅ Answer customer pricing questions
✅ Pull product plan details
✅ Generate a live payment link—all inside a conversation
We’re still in early stages, but it’s exciting to see Fin connecting with external tools through open standards. More to come soon.
Intercom MCP Server
This week, Senior Product Engineer Davy Malone built an Intercom MCP (Model Context Protocol) server using Cloudflare Workers—and it’s been a game changer.
It allows AI systems like Claude to access real-time data from Intercom: user profiles, conversation history, ticket metadata, and more.
For our engineers, this means less time manually pulling context across systems—and faster, smarter troubleshooting when issues arise.
And we’re just getting started. Soon, our AI agent Fin will also be able to connect to MCP servers to access data and perform actions in external systems.
If you’re curious about where this is going, we’ve shared more on our product vision for MCP here.
April 2025
Setting Fin Voice Live
Last week, we shared an early look at Fin Voice, our AI agent for the phone channel.
In this demo, Artem Ankudovich show’s how to take the next step by moving from a testing environment to going live once you’re confident in Fin’s tone, accuracy, and logic.
It’s exciting to see Fin step in as a frontline agent, handling real queries and handing off to another team when needed.
Fin Voice
Phone support is powerful – but also one of the hardest channels to get right. It’s often slow, tough to scale, and frustrating for customers stuck on hold. That’s why we built Fin Voice – our new Voice AI agent that helps businesses deliver instant, 24/7 phone support, without the wait.
In the demo, you’ll see how Fin responds to common support questions in the Fin Voice testing environment. No matter how large your help center is, Fin can instantly give the right answer and guide your customers – just like a human agent. It’s a glimpse of what support could look like without phone trees, hold music, or long wait times.
We’re still in the early stages, but the potential is big.
AI-Generated Content from Video
We want to share with you an experiment we built at a recent Intercom hackathon around converting videos into great content.
Inside Intercom, we share a lot of product videos internally as we develop and test new features. An idea Senior Group Product Manager Chris Dalley and Staff Product Manager Peter Bar tested was whether we could use AI to convert these product videos into great Help Center articles complete with auto-generated product screenshots – turns out yes we can!
This was a small experiment, but it points to a future where support content could is much easier to create, update, and scale – all from recording a quick video.
It’s still early thinking, and we’re not yet sure where it fits – maybe we should add it into the product? Also curious what approaches you have tested for creating great AI content?
Multilingual Workflows
Scaling support used to mean more tools, more workflows, and more manual effort.
AI is shifting that. And our Multilingual Workflows beta is part of that shift. It lets teams build once and serve every customer, in any language, without duplicating effort!
Check out this working demo, and share what’s helping your team scale.
AI Category Detection for Fin AI Agent
AI is transforming how customers get support. In particular, it’s reshaping the invisible parts, like how conversations are assigned to the appropriate teams.
When a customer reaches out, they expect to be understood and quickly directed to the right place. Intercom’s AI Category Detection feature helps Fin do exactly that. It analyzes each conversation to identify things like the request category or sentiment, and then saves this information as conversation data that can be used for routing, reporting, and more.
In this demo, we show how it can be used to automatically assign conversations to the right support team. No long menus for customers, no manual triage for teammates.
The feature is in closed beta, and we’re still learning more. Stay tuned!
OTP for Fin Tasks
As AI Agents take on more complex and sensitive workflows, security can’t be an afterthought. It has to be built in from day one.
At Intercom, we’ve designed Fin with security as a first principle, enabling customers to configure multiple layers of protection, real-time verification flows, and safeguards that help maintain trust between agents and their customers.
In this demo, Fin supports a customer through a policy amendment, verifying their identity and then guiding them through the process step by step.
Take a look and let us know what you think.
AI Generated Fin Task Instructions
Last week, we shared a demo of Fin showing how we’re blending rules based (deterministic) and natural language based (generative) logic in Fin so that our customers can configure the exact customer journeys they need.
This is a very important thing to learn about and understand if you are using or building AI Agents.
But this whole area is new, it has never existed before the AI era, and so people need to learn how to do it. To help them, we’ve built a feature that uses AI to suggest instructions. Today we’ve a nice follow up demo showing how you can write structured Task instructions using natural language in Fin.
March 2025
Fin Tasks
The future of AI Agents is generative and deterministic workflows blended together, and Agents that complete full Tasks for customers.
Most businesses have complexity, and in exploring different ways Fin can work for our customers, it’s really clear that they need both generative and deterministic steps in single workflows. Here is a demo of our work in progress.
Fin in the Help Center
Knowledge bases are a critical line of defence for support teams but they can also become a bottleneck, leaving customers to sift through hundreds of articles to find what they need.
So we ran an experiment: using Fin to deliver faster, more direct answers inside the Help Center.
We’re still early in the test but already learning a lot about how AI can reshape self-serve support.
Fin Messenger experiments
Over the past month we have been very busy running a series of experiments on the design of Fin in our Messenger. We decided to challenge numerous design decisions that were meant to address problems from a different era.
Every new experiment lead to theories, every theory lead to new ideas and ideas with the best rationales made it to their respective A/B tests. The results were as interesting as the process itself. This demo shows the results, and how we think about our AI products and using data to refine our design decisions.
Fin Testing
As more customers use Fin, we’re learning how important it is to test how Fin answers different types of questions.
So we’ve been building new powerful testing tools. Here is a working beta of one of them.
What makes it so powerful is the ability to bulk test how Fin answers a set of questions. Plus, right below the answers, you’ll find details on the inputs used to generate each response, making it easy to troubleshoot and refine.
We’re already seeing how this is helping customers feel more confident in how Fin responds – and giving them the tools to make those responses even better.
Fin Guidance Assistant
We mortal humans are still trying to learn how to use AI systems. The more we talk to them, the more weird and wonderful things we learn about how they ‘think’. LLMs are a generative technology which makes them unpredictable at times.
But using AI for business, we need to be able to control parts of what they do. We built Fin Guidance to do this, so you can guide Fin. But guidance is like advice, you can give good or bad advice, and so you can give good or bad guidance!
And we’ve seen customers doing this, trying to give good guidance but learning through trial and error that it doesn’t quite work as intended. So we’re now building tools to help our customers write good guidance. This is cutting edge stuff, we’re using Anthropic’s Claude 3.7 Sonnet with Extended Thinking.
February 2025
Real time Inbox Translations
Customer Service teams need to support their customers in many languages, and so end up hiring multilingual speakers, using different translation tools, and ultimately adding a lot of complexity to their support operations.
But AI is excellent at real time translations. So we’ve been building that into the Intercom Inbox. Now any customer and any agent can seamlessly converse, no matter their language.
Fin over API
So far, Chat has been the dominant interface for us to communicate with AI. Makes sense, it is familiar. But that is going to change, and soon. We’re seeing customers who want to build their own interface to Fin (our AI Agent) so we’re building Fin over API. Now, any interface is possible.
Giving Fin Guidance
When Support teams hire new people, they train them, they give them guidance on what to say and how to react in different scenarios.
They need to do the same for AI Agents, so we’ve been building that into Fin.