Same issue, different words.
Users say the same thing in a hundred different ways. Triagly's semantic matching groups them automatically — so "can't log in," "login broken," and "auth fails" show up as one issue with three mentions.
Semantic matching
Vector embeddings compare meaning, not just keywords. Different words describing the same problem are grouped together.
Accurate mention counts
Instead of 5 separate items, you see 1 issue with 5 mentions. Priority scores reflect true frequency.
Less noise, more signal
Your brief and feedback list show unique issues, not repetitive entries. Focus on what matters.
85%+ similarity threshold
Configurable threshold ensures only genuinely related feedback is grouped. No false positives.
How it works.
New feedback arrives
Every feedback item gets a vector embedding generated from its content.
Similarity search
The embedding is compared against all existing feedback using cosine similarity.
Automatic grouping
Items above the similarity threshold are linked as duplicates. The original gets an updated mention count.
Common questions.
What similarity threshold do you use?
The default is 85%. This catches most genuine duplicates while avoiding false groupings.
Can I unlink false duplicates?
Yes. If two items are incorrectly grouped, you can unlink them from the feedback detail view.
More features.
Decision Briefs
Your Monday morning feedback report.
Learn morePattern Detection
See what's trending before it explodes.
Learn moreAI Classification
Every item sorted. Automatically.
Learn moreAI Chat
When the brief raises a question, ask it.
Learn moreIntegrations
Plugs into what you already use.
Learn moreMCP Server
Your feedback data, inside your AI agent.
Learn moreCLI
Feedback data in your terminal.
Learn moreREST API
Build on top of your feedback data.
Learn moreSee it in action.
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