A practical guide to Fin AI suggestions in 2025

Let's be honest, keeping a knowledge base accurate feels like a never-ending chore. You spend ages auditing articles, but your team still misses things. Before you know it, customers are getting fuzzy answers, and your support agents are stuck in a loop, answering the same questions day in and day out.

This is where AI-powered suggestions are supposed to help. The promise is that they can automate the grunt work of finding and plugging gaps in your content, letting you fix problems before they get out of hand.

This post is a straightforward look at Intercom's feature, Fin AI Suggestions. We’ll get into how it works, what it does well, its pricing model, and the spots where it might not be the best fit for teams needing a bit more control.

What is Fin AI Suggestions?

Fin AI Suggestions is a feature built into Intercom that's designed to make its AI agent, Fin, a little bit smarter. Think of it as an internal review process for your automated support.

Its main job is to look at conversations where Fin either couldn't find an answer or gave a bad one. It then compares those failed chats with the successful replies sent by your human agents. From that comparison, it suggests specific tweaks and updates for your knowledge base.

Intercom highlights a few key benefits:

• You know what to fix: It shows you exactly where Fin got stuck and gives you clear recommendations on how to update your content.

• Less manual work: It automatically sifts through unresolved conversations, so you don't have to spend your day reading transcripts to figure out what went wrong.

• Focus on what matters: Suggestions are ranked by how much of an impact they could have, helping you tackle the fixes that will solve the most common problems first.

• You're still in charge: Nothing goes live automatically. Every single suggestion has to be reviewed, edited, and approved by a person on your team.

How Fin AI Suggestions works

Getting started means understanding where these suggestions appear and how you can actually act on them.

Where to find and review Fin AI Suggestions

You'll mainly interact with these suggestions in two places, each one designed for a different kind of role on your team:

1. The Optimize Dashboard: This is the bird's-eye view, built for support managers or team leads. It gives you more context by showing suggestions next to the topics that are causing high ticket volumes or unhappy customers. It helps connect the dots between a content gap and a business problem.

2. The Suggestions Page: This is more of a focused to-do list for whoever on your team is in charge of the knowledge base. It's just a clean list of recommended changes, making it easy to tick them off one by one.

The review process itself is pretty simple. For each suggestion, you can click in and see the original customer conversations that sparked it, which is handy for getting the full story. The proposed changes are highlighted, green for new text, red for stuff to remove. From there, you can edit the suggestion, accept it, or just reject it.

The types of Fin AI Suggestions you'll see

Fin AI Suggestions does more than just recommend edits to existing text. It can propose a few different kinds of improvements.

Sometimes it will suggest adding brand new content as "snippets." Other times it will recommend editing an existing article, flagging duplicate articles that might be confusing the AI, or pointing out contradictory information between two different help docs.

For teams using a synced help center from a platform like Zendesk or Salesforce, there's a neat workflow trick. Once you approve an edit inside Fin, you can push it live to your help center directly. While that sounds convenient, it’s worth remembering that this kind of tight integration can lock you into a specific setup. It takes some careful configuration and basically assumes your single source of truth is one of those platforms.

The good and the bad of Fin AI Suggestions

While the feature definitely has its perks, it's important to look at the whole picture, including where it might leave you wanting more.

What Fin AI Suggestions does well

Let's start with the highlights.

• Direct Publishing: Being able to push approved edits straight to a synced Zendesk or Salesforce help center really does save time. It cuts out a few manual steps in the content update process.

• Duplicate and Contradiction Spotting: A clean, consistent knowledge base is a must for any AI to work properly. Fin's ability to flag articles that are repetitive or contradict each other is a big help for maintaining that content hygiene.

• Impact-Based Prioritization: Not all content gaps are created equal. By ranking suggestions, Fin helps teams focus their energy on the changes that will actually make a difference to their customers.

Where Fin AI Suggestions has limitations

Now for the parts where you might feel a bit constrained.

First, it's a reactive tool, not a proactive one. Fin AI Suggestions only works by analyzing past failures. A customer has to have a bad experience first for a content gap to even get on your radar. It’s a bit like waiting for a bug report to come in before you fix your code. A more advanced approach, like the simulation engine from eesel AI, allows you to get ahead of this. You can test your AI against thousands of your historical tickets before it ever interacts with a live customer, catching issues before they happen.

A look at eesel AI's simulation engine, which allows teams to proactively test their AI against historical tickets before deployment, unlike the reactive nature of Fin AI Suggestions.

Second, its knowledge is a bit siloed. Fin's suggestions are mostly based on what's in your help center articles and internal snippets. But for most teams today, knowledge is scattered everywhere. In contrast, eesel AI can connect to all sorts of sources right out of the box, like Confluence, Google Docs, Notion, and even the context pulled from past support tickets. This gives your AI a much broader "brain" to learn from, not just a walled-off library.

An infographic showing how eesel AI integrates with multiple knowledge sources like Confluence and Google Docs, contrasting with the more siloed approach of Fin AI Suggestions.

Finally, it’s a closed ecosystem. The best features, like direct publishing, are really designed for helpdesks that are deeply tied to Intercom. If your knowledge base lives somewhere else or you use a different helpdesk, you don't get that same smooth workflow. This is a big difference from eesel AI, which is built to be platform-agnostic. It’s designed to plug into the helpdesk and tools you already use, without making you switch everything over.

The cost of Fin AI Suggestions

Pricing can be the deciding factor for any tool, and Fin's model is worth a close look.

Fin is priced at $0.99 per resolution, with a 50-resolution minimum each month. You can also get it as part of a bundle with Intercom’s Helpdesk, which adds a per-agent seat cost on top of that.

The main catch with a per-resolution model is that your costs become unpredictable and actually grow as you get better at automation. The more issues your AI resolves, the higher your bill gets. This can make budgeting a real headache, especially if your support volume changes from month to month. You’re effectively paying a penalty for being successful.

This is where eesel AI's pricing offers a much more predictable path. Our plans are based on a flat monthly or annual fee, tied to the number of AI interactions you expect to have. There are no per-resolution charges. Your costs stay the same, no matter how many tickets your AI knocks out of the park.

To give you an idea, eesel AI's plans look like this:

• Team Plan: For $239/month (billed annually), you get up to 1,000 AI interactions per month and can train your AI on your docs and use the agent Copilot.

• Business Plan: For $639/month (billed annually), you get up to 3,000 interactions and unlock more advanced features, like training the AI on past tickets and running bulk simulations.

• Custom Plan: For larger teams with unlimited needs, we offer custom integrations and advanced setups.

A screenshot of the eesel AI pricing page, showing its flat-fee subscription plans as a predictable alternative to the per-resolution model of Fin AI Suggestions.

Is Fin AI Suggestions the right tool for you?

So, what's the verdict? Fin AI Suggestions is a decent feature for teams who are already all-in on the Intercom ecosystem. If Fin is your main AI agent and your knowledge base is hooked into a platform like Zendesk, it offers a pretty streamlined way to keep your content up to snuff.

But its downsides are pretty clear for any team that needs more flexibility and power. The reactive approach means you're always playing catch-up with customer problems. The limited knowledge sources mean your AI is working with one hand tied behind its back. And the unpredictable pricing can make it tough to budget for as you grow.

For teams that want full control, proactive testing, and predictable costs, eesel AI is built to be a better fit. With the ability to connect all your knowledge sources, a simulation mode to test everything before you go live, and transparent flat-fee pricing, eesel AI gives you the tools to build a smarter support agent that works with the setup you already have.

Ready to see how a more powerful and predictable approach can improve your support? Start your free eesel AI trial today and find out how our simulation engine can level up your support automation.