Optimize reviews the conversations Fin can’t answer and sends to your team, then provides weekly suggestions to fix gaps in Fin’s content, data, and actions.
By analyzing real customer conversations, it highlights the highest-impact opportunities to improve automation and increase Fin’s resolution rate faster.
Key benefits
See what’s blocking resolution by identifying content gaps, customer data gaps, and action gaps.
Prioritize the highest impact fixes using consistent, conversation-based impact scores.
Work from real customer conversations that directly informed each suggestion.
Move from escalation to automation with clearer guidance on when to create content, data connectors, or procedures.
How Optimize works
Optimize brings all AI-powered improvement suggestions into a single experience, making it easier to understand, filter, and act on recommendations.
To get started, go to Fin AI Agent > Analyze > Optimize. This provides you with a list of suggestions grouped by the type of gap that prevented Fin from resolving a conversation.
Note: You’ll only see Optimize once Fin is live and handling real customer conversations.
Content gaps
Content gap suggestions highlight where Fin couldn’t answer because help content was missing, unclear, duplicated, or contradictory.
For each content gap suggestion, you can:
Review AI-generated create or edit recommendations.
See the exact conversations that triggered the suggestion.
Update public articles or snippets directly to improve future resolution.
Tip: Learn more about using AI-powered content suggestions to improve Fin.
Customer data gaps
Customer data gap suggestions appear when Fin needed information from an external system that wasn’t available, such as order status or account details.
Each customer data gap suggestion clearly shows:
A guide which outlines the API and data needed to build connectors so Fin can resolve conversations automatically. The examples provided should be reviewed and adapted by an engineer.
How to create the automation using a data connector.
The customer queries that would get answered with this customer data.
The implementation effort, which is based on technical complexity, infrastructure and dependency requirements, security and compliance considerations, data operations complexity, business logic requirements, performance and scalability needs, testing and validation complexity, and risk assessment.
Sample API documentation (for reference only). This sample schema illustrates the type of request/response structure you might implement. Your actual endpoints, parameters, and auth flows may differ, so treat this as a blueprint for the integration patterns required to resolve these queries.
An escalation guidance recommendation you could implement quickly as a temporary way to route complex queries to your team and maintain a smooth customer experience, while the full automation is being built.
Tip: Learn more about Fin's escalation guidance and rules.
Action gaps
Action gap suggestions identify where Fin needed to take an action in another system, like updating a workflow or canceling an order.
Each action gap suggestion clearly shows:
A guide which outlines the API and data needed to build connectors or tasks so Fin can resolve conversations automatically. The examples provided should be reviewed and adapted by an engineer.
How to create the automation using data connectors and Fin Tasks or Procedures, depending on your setup.
The customer queries that would get answered with this action.
The implementation effort, which is based on technical complexity, infrastructure and dependency requirements, security and compliance considerations, data operations complexity, business logic requirements, performance and scalability needs, testing and validation complexity, and risk assessment.
Sample API Documentation (for reference only). This sample schema illustrates the type of request/response structure you might implement. Your actual endpoints, parameters, and auth flows may differ, so treat this as a blueprint for the integration patterns required to resolve these queries.
An escalation guidance recommendation you could implement quickly as a temporary way to route complex queries to your team and maintain a smooth customer experience, while the full automation is being built.
Analyzing the data
How impact is calculated
Optimize uses a consistent impact model across all suggestion types where impact is based on:
The number of related conversations.
The time period those conversations cover.
This means impact reflects real, historical demand, so it’s easier to judge which fixes are worth prioritizing.
Note: By default, suggestions are sorted from highest to lowest impact, with the option to sort by date (newest to oldest) when reviewing longer time ranges.
View conversations that informed suggestions
If you open the side drawer in the top right, you’ll find the conversations that directly informed a suggestion.
This makes it easier to:
Understand the exact customer questions behind a suggestion.
Validate whether the fix will meaningfully improve resolution.
Share concrete examples with teammates when planning changes.
Filter and sort suggestions
Optimize includes powerful filters so different teams can focus on what matters most.
You can filter by:
Reason: to select a suggestion type (content, customer data, action).
Date range: to review suggestions week by week.
Topic: using AI-generated topics like Billing or Pricing.
Impact: to focus on the biggest wins first.
Accept or reject a suggestion
Once you click Accept, Mark as done, or Reject a suggestion, it’s removed from the list and won’t reappear again.
Optimize settings
Click on the settings icon at the top of the Optimize page to segment content suggestions by Fin audiences you’ve set up.
When you segment content suggestions by audience, each selected audience receives its own set of tailored suggestions. This ensures that recommendations stay relevant and specific to each audience or brand. If you select “Everyone,” the suggestions will be more general rather than audience-specific.
Note:
Segmentation currently only works for content suggestions.
When you save, your current content suggestions will be cleared. New segmented content suggestions will be generated, which may take a few hours.
FAQs
What’s changed from the previous Optimize dashboard?
What’s changed from the previous Optimize dashboard?
Optimize Fin has been simplified and refocused to make improvements easier to act on.
Key changes include:
A single suggestions experience, replacing separate Train and Analyze views.
Clearer automation guidance, instead of prioritizing escalation guidance.
Removal of “Investigation needed” suggestions, which were vague and hard to act on.
Exclusive focus on resolution rate, with other metrics planned for future iterations.
What conversations are included in Optimize?
What conversations are included in Optimize?
Optimize analyzes only meaningful support conversations:
Inbound conversations
Written by a customer
With at least two responses from Fin or a teammate
It excludes pending conversations and automated workflow messages.
How often are suggestions generated?
How often are suggestions generated?
Content gap suggestions are generated weekly, with additional triggers for high volume, sustained activity, or sudden spikes.
Customer data and action gap suggestions are generated weekly.
Suggestions are reviewed and worked on week by week using date range filters.
What happened to the Unresolved Questions report?
What happened to the Unresolved Questions report?
The Unresolved Questions report has been replaced by Optimize. Optimize not only shows where Fin couldn’t resolve conversations, but also provides clear, actionable guidance on how to fix those issues and improve resolution rate.
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