Fin Categories is currently in closed beta
If you’d like access, please contact Zoe Sinnott at zoe.sinnott@intercom.io with your workspace ID.
This feature is still evolving. Some features, like reporting and conversation drilldowns, are not yet live - but we’re working on them. This article will be updated as new capabilities are released.
Already using AI Category Detection? Scroll down to learn how Fin Categories compares and how to transition.
Overview
Fin Categories enable Fin to understand and classify what a conversation is about in real time. This allows Fin to apply structured attribute values - like issue type, sentiment, or urgency - that power smarter triage, routing, and reporting.
Quick Start: Set up Fin Categories in 3 steps
Decide what to detect
Choose the structured attributes you want Fin to classify - like issue type, sentiment, urgency, or spam.
Create or convert a category
Go to Train > Categories. Define your attribute values with clear descriptions, or convert an existing list-formatted attribute.
Preview and enable
Use the preview tool to test Fin’s accuracy. Tweak your value descriptions, then enable the category for real conversations.
Once enabled, use the detected attribute values in escalation rules, workflows, and reporting.
How it works
With Fin Categories, Fin listens to every message in real time and assigns attribute values based on live context. For example, if a customer shows frustration or urgency, Fin can automatically detect that and trigger escalation.
Detected values are stored as conversation data attributes, which can be used to power your workflow branching and routing rules, as well as generate reporting insights.
Key benefits
Custom categories that reflect your business: Train Fin to detect attributes like issue type, urgency, sentiment, or spam status.
Real-time, adaptive detection: Fin continuously evaluates context and updates values as the conversation evolves.
Smart routing and escalation: Combine Fin Categories with workflows and escalation rules to route conversations to the right team at the right time.
Reporting-ready structure: All detected category data flows into Fin reports for deep insight—no manual tagging required.
Full transparency and control: View, validate, and override Fin’s categorization logic at any time.
How to set up Fin Categories
Step 1: Decide what to classify
Think about the types of structured information you want Fin to detect. Common examples include:
Issue type (e.g., Billing, Projects, Account Management)
Sentiment (Positive, Neutral, Negative)
Urgency (Urgent, High, Normal, Low)
Spam detection (Spam, Legitimate)
Step 2: Create a new category (or convert an existing attribute)
To create a new Fin Category:
Go to Train > Categories to get started.
Click New category.
Fill in the Name and Description for your category
Add Category Values (with clear descriptions for each).
Optionally:
Toggle Limit visibility if the category is only relevant for certain teams and you’d like to control who sees it in the Inbox.
Toggle Required attribute if in the event that Fin cannot detect a category value, you want to ensure that a teammate applies a value before closing the conversation.
To convert an existing attribute:
Go to Settings > Conversation data, click edit on a list-type attribute, and then click Convert to category
Once converted, the attribute will appear under Train > Categories.
Note: Once converted to a Fin Category, an attribute can't be reverted - but you can leave it disabled if needed.
Step 3: Preview before enabling
Before enabling a category, use the built-in preview in Train > Categories to:
Test category values against example customer messages
Check how accurately Fin applies the right value
Iterate on names and descriptions before enabling
Step 4: Monitor accuracy and refine [COMING SOON]
Drilldowns and accuracy stats will let you track how reliably each value is applied and refine descriptions based on misclassifications.
This enables you to monitor categorization accuracy, review miscategorized conversations and update category descriptions based on patterns.
How Fin applies categories
By default, when Fin is involved in a conversation, it will classify enabled categories at key moments:
When handing off to a teammate
When the customer expresses resolution (positive feedback)
When a customer becomes inactive
If you set up escalation rules, Fin will re-evaluate the conversation after each message, enabling more dynamic, real-time classification.
Using Fin Categories
Escalation rules for intelligent handoff
Escalation rules in Fin Guidance enable you to combine detected Fin Categories with other user or company data to build precise rule-based logic that determines when Fin should hand off. Escalation rules are evaluated and triggered deterministically, ensuring maximum reliability.
Escalation rules work alongside text-based escalation guidance (which works best for soft signals in natural language nuance).
To make it easy for you to configure routing and handover behavior when Fin escalates a conversation to the team, you can create branches in workflows based on which escalation rule in Fin Guidance has been applied. This gives you full control of how Fin routes to the team for specific scenarios.
In workflows to define handover logic
Use the applied Fin Category values to define branches within your Fin workflow. For example, you can add a branching condition based on the Fin Category detected and route the conversation to a specific team or teammate.
In reports for conversation insights
Automatically filter and segment your Fin reports using the conversation attribute data from your Fin Categories to generate insights and analysis about conversation trends.
In conversations for manual QA and review
Teammates can see when a category has been detected by Fin in Analyze > Conversations when viewing conversation events in the thread or in the sidebar under "Conversation attributes".
Note: Ensure that you have "Show conversation events" enabled to see the conversation events in the thread.
Best practices for high-accuracy detection
To get the best performance from Fin when categorizing conversations, follow these best practices:
1. Create short, descriptive names
Keep category names under ~25 characters.
Use short, descriptive language that immediately signals the category value (e.g., “Login Issues”, “Billing Questions”).
Avoid internal jargon, ticket IDs, or ambiguous terms.
2. Write detailed descriptions
Aim for 100–300 characters.
Describe the types of conversations that belong in the category.
Include:
Keywords or phrases the customer might use.
Examples of questions or intents (e.g., “How do I reset my password?”).
Clarifications on what doesn’t belong if needed.
3. Make categories distinct
Ensure there’s minimal overlap between category values.
Ask: “Would a human find it hard to choose between these?”
Avoid redundant or overly narrow categories.
4. Create an ‘Other’ category to avoid over-eager categorisation
Include a general-purpose ‘Other’ category to capture conversations that don’t clearly fit into your defined categories.
This helps prevent Fin from trying to find a match when none of the options are suitable.
Especially useful for categories that should only apply to a subset of conversations. (e.g., conversations about a specific product or use case).
By following these guidelines, you’ll help Fin interpret prompts more reliably, reduce confusion between categories, and increase your categorization success ratio.
Examples of good category descriptions
Category label: Account Access
Category description: For conversations where users can't log in, forgot their password, or are locked out of their account. E.g., “I can’t get into my account.”
Category label: Refund Requests
Category description: Any conversation where the customer requests a refund or mentions being overcharged. Keywords: refund, charged, cancel payment.
💡 Pro tip
Try passing your category names and descriptions to a writing tool such as Claude AI or ChatGPT to define them clearly.
Example prompt: Write comprehensive descriptions for all of the category values listed - Include all relevant details about what belongs in the category. Think about every type of conversation that should fall under this category and describe them in the description. Providing a detailed description will help our AI Agent categorize support conversations correctly. Include keywords and examples of customer questions.
Example use cases
Below are some examples of ways that customers are using Fin Categories to categorize conversations.
Issue Type category example:
Projects - Projects are a collection of related tasks and activities aimed at achieving a specific objective or deliverable which can involve teammate collaboration, time tracking, milestones or goals, and status.
Billing - Billing encompasses managing subscription plans, invoices, payment methods, discounts, plan features, trials, account restrictions, refunds, and more for a seamless billing experience.
Account Management - Account Management covers discussions related to user accounts, including account creation, deletion, updating personal and payment information, and more.
Sentiment category example:
Positive - A positive sentiment means the user who wrote the message seems generally happy or satisfied and is probably feeling a positive emotion.
Negative - A negative sentiment means the user who wrote the message seems generally unhappy or dissatisfied and is probably feeling a negative emotion.
Neutral - A neutral sentiment means the user who wrote the message seems to be neither happy nor unhappy and it is difficult to guess their emotion.
Spam Detection category example:
Spam - Automated spam that is sent to the customer support agents. This includes auto-responders, newsletters, guest posts, and other general spam messages that could be ignored by the CS analyst.
Legit - Legitimate conversations in which the user has an actual issue that should be handled by a customer support analyst.
FAQs
What does Fin use to detect categories?
What does Fin use to detect categories?
Fin uses the category name, its description, and the value names and descriptions when evaluating which category value to apply. Ensure that all of these fields are written in a human-readable way that is easy for Fin to interpret.
What if categorization isn’t accurate?
What if categorization isn’t accurate?
We recommend checking that the naming and descriptions. Use our best practices and test with real customer messages in the preview tool.
💡 Pro tip
To help review your category value labels and descriptions, you might like to try using an AI writing tool such as Chat GPT or Claude.
Example prompt:
You are an expert in customer-support AI. You are evaluating a taxonomy used by a LLM to classify customer support conversations. For each attribute (e.g., Topic, Sentiment), the LLM chooses the most appropriate category based on a combination of the category name and its description. This taxonomy will directly impact the LLM's ability to classify real customer support conversations. Your task is to assess the quality of this setup using the following best practices: Create clear, concise names - Choose short, descriptive names that immediately convey the category's purpose. Write comprehensive descriptions - Take the time to write detailed descriptions and include all relevant information about what belongs in the category. Think about every type of conversation that should fall under this category and describe them in the description. Providing a detailed description will help Fin categorize conversations correctly. You can include keywords and examples of customer questions. Make categories distinct - Avoid creating categories that overlap too much. Your categories should be clearly different from each other, making it easy to determine which one best fits a given situation. This should be checked within each attribute only. It's fine if a category overlaps with another category from a different attribute. It shouldn't affect the score. Overlap with categories in other attributes is allowed and does not affect this score. Ignore Archived Categories - If a category is marked as archived, do not evaluate or score it. Add 5 Columns to the CSV Clarity/Conciseness (1–5), Description Comprehensiveness (1–5), Category Distinction (1–5), Final Score, Comment Assess each parameter for each category, and write a comment of why you applied this rating. Then calculate the overall score for this setup. After you've done this add one more column: Overlapping Categories. If you think any given category overlaps with other categories - list these categories there.
What if Fin doesn’t detect a value?
What if Fin doesn’t detect a value?
Fin will return a null value if none of the categories apply, which will leave the category empty. You can include an "Other" option to catch unclassified conversations if preferred.
I’m using AI Category Detection - should I switch to Fin Categories?
I’m using AI Category Detection - should I switch to Fin Categories?
If you’re using the older AI Category Detection [Beta] feature, you can continue to do so. However, Fin Categories is the newer, more advanced version designed to offer better accuracy, smarter routing, and improved reporting.
We recommend:
Requesting access to and using Fin Categories for all new AI-powered categorization use cases.
Planning to gradually move away from AI Category Detection, as this feature will be sunset in the future.
It’s possible to use both features in parallel for now, but all future improvements will focus on Fin Categories.
Want to switch? Reach out to Zoe Sinnott at zoe.sinnott@intercom.io to request access to the Fin Categories beta.