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Taxonomy

Build an AI-powered product taxonomy to automatically categorize and label your customer signals. Paste your docs, get a 4-level hierarchy, and let AI auto-label everything.

Taxonomy interface

Organize your signals automatically

Stop manually categorizing feedback. Build your product taxonomy and let AI auto-label every signal.

4-Level Hierarchy

Domain, Product, Feature, and Tag levels that mirror how your product is actually structured.

AI Auto-Labeling

AI labels incoming signals to the right Product, Feature, and Tag. Run in backfill or overwrite mode.

Generate from Docs

Paste your product documentation and AI generates your entire taxonomy tree. Then refine from there.

Custom Labeling Instructions

Add descriptions and instructions to any node so the auto-labeler handles ambiguous features correctly.

The 4-level hierarchy

Domains organize at the top. Products, Features, and Tags are auto-labeled by AI.

Domain

Manual

High-level product area or customer journey

e.g. Collaboration, Integrations, Admin & Security

Product

Auto-labeled

Specific offering, module, or major feature set

e.g. Documents, Messaging, REST API

Feature

Auto-labeled

Individual capability or functionality

e.g. Comments & Threads, Sprint Planning, Webhooks

Tag

Auto-labeled

Specific scenario, variation, or fine-grained label

e.g. Inline comments, CSV import, Two-way sync

How it works

1

Paste your docs

Feed in product documentation, feature lists, help docs, or release notes. AI extracts a full 4-level hierarchy.

2

Refine the tree

Review in the Taxonomy Editor. Rename nodes, add missing features, edit AI labeling instructions, drag to reorder.

3

Auto-label signals

AI labels every incoming signal. Backfill existing data or overwrite previous labels. See signal counts per node.

Taxonomy vs Custom Tags

Use taxonomy for product categorization. Use custom tags for cross-cutting concerns like "urgent", "competitor mention", or "follow-up needed".

Taxonomy
Custom Tags
Hierarchical (4 levels)
Flat list
AI-generated structure
Manually created
Auto-applied to signals
Manually applied
Product-focused
Flexible use cases

Best practices

Start broad, then refine — begin with major product areas and add detail over time
Use your customers' language — name nodes using terms customers actually use
Add descriptions to ambiguous features so AI labels them correctly
Review periodically as your product evolves — add new features, retire old ones
Don't over-categorize — focus on distinctions that matter for analysis

What's next

Signals

Auto-label every signal to your taxonomy

Explore Signals

Feedback Collection

Organize feedback with structured categorization

See Use Case

Works with Clusters

Combine taxonomy with AI-generated clusters for the best of both worlds. Structured categorization for known product areas, plus AI discovery of emerging themes.

See Clusters
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Organize at scale

Stop tagging manually. Start analyzing.