How raum detects gaps
Every ticket that flows through raum is scored for confidence. The AI searches your procedures and articles using semantic similarity — it’s not looking for exact keyword matches, but for meaning. When the best match falls below raum’s confidence threshold, the system records the ticket as a potential knowledge gap and groups it with similar tickets under a common topic label. You don’t need to configure anything for detection to work. Gaps appear in the dashboard automatically as the AI encounters them.Knowledge gaps are detected by the AI pipeline regardless of whether you’re running Agent Mode or Copilot Mode. Both modes report low-confidence tickets to the same gaps list.
Reviewing gaps in the dashboard
Navigate to/dashboard/knowledge-gaps to see all detected gaps. Each gap entry shows:
- The topic label raum inferred from the pattern of tickets
- The number of tickets that triggered this gap
- Example tickets you can open to read the actual customer questions
- The current status: open, addressed, or dismissed
How to address a gap
Addressing a gap means giving raum the information it needs to handle that topic confidently. There are two main approaches:Identify the gap topic
Open the knowledge gaps list at
/dashboard/knowledge-gaps. Click on a gap to see its example tickets. Read 3–5 of them to understand the shape of the question — is this a billing question, a how-to, a policy question, or something else?Decide: procedure or article
If the topic requires the AI to follow a specific decision path or take action (for example, processing a refund request or escalating a billing dispute), create a procedure. If the topic is purely informational (for example, explaining how a feature works), adding or updating a help center article is usually sufficient.
Create the procedure or article
Navigate to Resources → Procedures or Resources → Articles and create the new content. Write it with the customer questions from the gap in mind — the more closely your procedure matches the language and intent of the actual tickets, the better the AI’s confidence score will be when matching.
Mark the gap as addressed
Return to
/dashboard/knowledge-gaps and mark the gap as addressed. This moves it out of the open list and lets you track whether the fix had the intended effect. New tickets on the same topic will show up as a fresh gap if confidence remains low, giving you a feedback loop.Status tracking
Each gap has one of three statuses:Open
The gap has been detected and not yet acted on. These are the ones that need your attention.
Addressed
You’ve created a procedure or article to cover this topic. The AI will use the new content on future tickets.
Dismissed
You’ve reviewed the gap and decided it doesn’t warrant a procedure — for example, if the tickets are out-of-scope questions or one-off edge cases.
Example workflow
Here’s a typical knowledge gap workflow from detection to resolution:Weekly gap review
Set aside 15 minutes each week to review open knowledge gaps. Sort by ticket count and focus on the top 3–5.
Read the example tickets
For each gap, read the example tickets. Group them mentally — are these all variations of the same question, or are there sub-topics?
Write a procedure
If the question involves a decision or action, write a procedure in Resources → Procedures. Use the example tickets as a guide for what language and scenarios to cover.
The knowledge gaps API is available at
/api/dashboard/knowledge-gaps if you want to build your own reporting or integrate gap data into an external workflow.