Deep taxonomies break automated classification
Models mapping products into a taxonomy thousands of nodes deep stall on ambiguous items, and an accuracy target means nothing until the error standard is defined.
Retail AI That Knows
Your Catalog
Product tagging, visual search, and recommendation evals – data operations that keep pace with a catalog that never stops changing.
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Operational priorities
Requirements for Retail AI Data
The conditions that separate a durable data operation from a one-off labeling project.
Relevance metrics need a standard outside the click loop
Clicks are biased by position and presentation, and an LLM judge drifts with its prompt and model version. Both need a stable standard to measure against.
Assistant failures don’t show up in ranking metrics
A shopping assistant can retrieve the right products and still misstate a material, a dimension, or a constraint the shopper gave. Those failures only surface when the conversation itself is graded.
Capabilities
What we deliver for retail teams
Catalog data
Product data enrichment
Extract attributes, map taxonomies, and match duplicates across large catalogs.
Relevance
Search relevance evals
Measure ranking quality with human judgments across queries and locales.
Computer vision
Visual search & shelf analytics
Label imagery for visual discovery, shelf audits, and planogram analysis.
Generative AI
Recommendation & GenAI evals
Evaluate recommendations and shopping assistants against clear rubrics.
The Loop
The relevance loop
Enrichment feeds search; judgments measure it; the misses set the next batch.
Taxonomy & rubric
Your category tree, attribute schema, and relevance rubric encoded into guidelines trained specialists apply consistently.
Calibration
A pilot batch measured on inter-rater agreement per attribute and query class. Rubric ambiguities are fixed before volume.
Production
Catalog enrichment and relevance judgments ship on cadence – with capacity that flexes for launches and peak season.
Eval & re-rank
Relevance evals score each ranking release. Losing query classes define the next judgment batch.
One accountable partner
Built for the search & discovery lead
Ranking experiments start from defensible relevance data, with the evaluation sets already built.
Start with one query class
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on better data
Platform, experts, and workflows – unified in one secure infrastructure.