Features

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.

1

New feedback arrives

Every feedback item gets a vector embedding generated from its content.

2

Similarity search

The embedding is compared against all existing feedback using cosine similarity.

3

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.

See it in action.

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