Use cases · Customer support
Help center gap finder
Find the questions customers keep asking that your help center does not answer well, and draft new or updated articles for the support team to review and publish.
Where it starts
The trigger.
A weekly review of recent tickets, chats and search terms.
How it runs today
- Agents answer the same questions by hand every week.
- Help articles go stale after product changes.
- Nobody knows which missing articles would save the most time.
The workflow
Four bounded steps.
Each step has an owner and a stop rule.
- 01 / Collect
Pull recent ticket topics, chat questions and help center searches with no good result.
- 02 / Group
Cluster them by question and rank by how often they come up.
- 03 / Draft
Write a new article or an update for the top gaps, using approved product information.
- 04 / Publish
A support lead edits, approves and publishes, then tracks whether the questions drop.
Where people stay in control
Approval points.
- A support lead approves every article before it is published.
- Policy, billing and legal wording are checked by the right owner.
How we would measure it
Against a baseline.
Measured before and after, on the same work.
Tickets on topics that now have an article, before and after.
Help center searches with no result.
Time from gap found to article published.
Related
Go deeper.
More use cases
Other places to start.
Enrollment and program questions
Answer prospective and current students’ routine questions about programs, dates, fees and requirements from approved program information, with the source shown, and route anything personal or unclear to admissions or student services staff.
See the workflow Customer supportDonor and member questions
Answer donors’ and members’ routine questions about giving, receipts, memberships, events and volunteering from approved information, with the source shown, and route anything personal, sensitive or about a specific gift to staff.
See the workflow Customer supportSupport triage and reply drafts
Classify incoming tickets, find the relevant answer, and draft a reply for an agent to check.
See the workflowAn illustrative workflow pattern, not a result from a specific customer.
Bring the messy version
One workflow.
A better way to run it.
Show us the tools, the handoffs, and the part that keeps slowing your team down.
---
title: "Help center gap finder: AI Use Case"
description: "Find the questions customers keep asking that your help center does not answer well, and draft new or updated articles for the support team to review and publish."
url: https://lidvizion.ai/use-cases/help-center-gap-finder/
site: Lid Vizion
index: https://lidvizion.ai/llms.txt
---
[Use cases](https://lidvizion.ai/use-cases/) · Customer support
# Help center gap finder
Find the questions customers keep asking that your help center does not answer well, and draft new or updated articles for the support team to review and publish.
[Talk through this workflow](https://lidvizion.ai/contact/?need=ai-workflow&from=%2Fuse-cases%2Fhelp-center-gap-finder%2F#write) · [Configure your AI](https://lidvizion.ai/services/ai-setup/)
Where it starts
## The trigger.
A weekly review of recent tickets, chats and search terms.
How it runs today
- Agents answer the same questions by hand every week.
- Help articles go stale after product changes.
- Nobody knows which missing articles would save the most time.
The workflow
## Four bounded steps.
Each step has an owner and a stop rule.
1. 01 / Collect
Pull recent ticket topics, chat questions and help center searches with no good result.
2. 02 / Group
Cluster them by question and rank by how often they come up.
3. 03 / Draft
Write a new article or an update for the top gaps, using approved product information.
4. 04 / Publish
A support lead edits, approves and publishes, then tracks whether the questions drop.
Where people stay in control
## Approval points.
- A support lead approves every article before it is published.
- Policy, billing and legal wording are checked by the right owner.
How we would measure it
## Against a baseline.
Measured before and after, on the same work.
### Tickets on topics that now have an article, before and after.
### Help center searches with no result.
### Time from gap found to article published.
Related
## Go deeper.
- [Configure your AI](https://lidvizion.ai/services/ai-setup/): Turn individual AI use into a repeatable way of working.
- [Embeddings and feature vectors](https://lidvizion.ai/resources/basics/embeddings-feature-vectors/): How models turn text, images and records into numbers that can be compared.
- [Close the gaps in your help center](https://lidvizion.ai/guides/close-the-gaps-in-your-help-center/): A four-step weekly routine to find the questions customers keep asking that your help center does not answer, draft articles for the biggest gaps and check whether the questions drop.
- [How to find help center gaps with AI](https://lidvizion.ai/answers/how-to-find-help-center-gaps-with-ai/): How can a support team use AI to find the questions its help center does not answer well and fix them first?
More use cases
## Other places to start.
[All use cases](https://lidvizion.ai/use-cases/)
- [Enrollment and program questions](https://lidvizion.ai/use-cases/enrollment-and-program-questions/): Customer support Answer prospective and current students’ routine questions about programs, dates, fees and requirements from approved program information, with the source shown, and route anything personal or unclear to admissions or student services staff.
- [Donor and member questions](https://lidvizion.ai/use-cases/donor-and-member-questions/): Customer support Answer donors’ and members’ routine questions about giving, receipts, memberships, events and volunteering from approved information, with the source shown, and route anything personal, sensitive or about a specific gift to staff.
- [Support triage and reply drafts](https://lidvizion.ai/use-cases/support-triage/): Customer support Classify incoming tickets, find the relevant answer, and draft a reply for an agent to check.
An illustrative workflow pattern, not a result from a specific customer.
Bring the messy version
## One workflow. A better way to run it.
Show us the tools, the handoffs, and the part that keeps slowing your team down.
[Let’s map your workflow](https://lidvizion.ai/contact/) · [Read our approach](https://lidvizion.ai/approach/)