LLM Feed quality

Smarter product data: LLM enrichment you can review field by field

Booncy applies large language models to improve catalog quality at scale. Titles, descriptions, Google product categories, product types, colors, and highlights are rewritten for clarity, relevance, and Shopping performance. Every change is tracked and surfaced in the dashboard as a side-by-side comparison.

From raw feed to optimized catalog

The enrichment pipeline starts with your Merchant Center feed. An LLM-powered process proposes improved values for key attributes, then stores paired before/after samples that advertisers can inspect in the AI Content Enhancement section of the dashboard.

Each comparison card shows the field name, the original feed content, and the LLM-enhanced version, tagged by impact area: Content, Classification, or Attributes.

Supported attributes include title, description, Google product category, product type, color, and product highlight. Only fields where the enhanced value differs from the original are stored and displayed, so every card represents a real improvement.
  • Side-by-side before/after cards for title, description, category, product type, color, and highlights
  • Impact tags (Content, Classification, Attributes) to group improvements by business area
  • Structured compare CSV import with schema validation and de-duplication
  • Curated dashboard grid showing a balanced sample across field types
  • Powered by Gemini (google-genai) as part of Booncy's operational feed quality workflow

Better product data means better visibility: enrichment you can audit, not a black box.

Results are imported via a structured compare CSV and validated against a fixed schema before they reach the dashboard. Samples are capped and curated to show a balanced mix of field types, so advertisers see improvements across content, taxonomy, and attributes in a single view. The same enrichment logic is used internally to raise feed quality before campaigns go live, powered by Gemini through the Google GenAI stack.

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