AI-Powered Auto-Tagging That Gets Smarter Over Time

I'm excited to share a new feature I just shipped for my SaaS feedback collection tool: intelligent auto-tagging that actually improves with usage.

The Problem

Manually tagging customer feedback is tedious but necessary for proper organization and ease of search. Most auto-tagging systems I've seen either:

• Create too many redundant tags (tag explosion)

• Miss important nuances in the feedback

• Require constant maintenance

The Solution

I've implemented an AI-powered tagging system that:

1. Gets smarter over time - As users add more feedback, the system increasingly reuses existing tags rather than creating new ones, naturally converging toward a stable taxonomy.

2. Uses contextual intelligence - Each new piece of feedback is tagged based on:

   • Similar previously tagged feedback

   • Relevant knowledge base articles

   • The complete existing tag universe

3. Maintains the perfect balance - Prioritizes matching existing tags when appropriate, but isn't afraid to create new ones when genuinely needed (1-3 tags per feedback item).

Below is an example of the "Branding" tag:

The Tech Behind It

A quick sneak-peek behind the scenes:

• Built on PostgreSQL with pgvector for embeddings storage

• Vector similarity to find relevant context

• AI model that receives:

  • New feedback to tag

  • Complete list of existing tags

  • Similar feedback examples with their tags

  • Relevant knowledge base articles

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  I'll admit, This feature was really fun to build :) And has a great potential for refining and refactoring (e.g similarity thresholds, max. number of tags, allow user to edit tags to give AI a nudge in the right direction, and so much more)