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Product Agent is currently in beta, and we’re seeking partners to help shape its future.
AI searches are changing how consumers find and evaluate products. Agents such as ChatGPT, Perplexity, and Gemini prioritize semantic understanding, contextual cues, and content richness. To compete in these environments, brands need to modernize how their product data is structured. Product Agent helps you compete by improving content depth and enriching product information to make it accessible to AI agents and search platforms through two core workflows:
  • Monitor: Measure and analyze how products appear in AI search contexts.
  • Activate: Enrich and deploy product data optimized for agentic commerce channels.

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Monitor: Track and Benchmark AI Visibility

Monitor helps you understand how your catalog performs in AI search results. It focuses on how well product content ranks and competes in real-world AI search prompts. You can track catalog performance with fabric’s built-in prompts, or your own custom prompts, and then benchmark against top-performing competitors in your category. In addition to visibility tracking, Monitor provides content guidance: actionable recommendations to improve data completeness, structure, and relevance. These insights help prioritize content upgrades that will deliver the most visibility lift.

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Example use cases

  • Evaluate AI search readiness: Understand how well your catalog is structured for AI search.
  • Category-level benchmarking: Compare your brand’s visibility against competitors.
  • Prompt-based auditing: Run custom prompts to evaluate how your listings perform in realistic searches.

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Activate: Enrich and Deploy Optimized Product Data

Once visibility gaps are identified, Activate helps you optimize product data and publish it to your sales channels. Activate provides tools to enrich your catalog using fabric’s golden standard data set. Features such as taxonomy mapping and brand configuration ensure that product data is not only enriched, but also consistent with your brand standards, and validation options allow you to retain editorial control.

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Example Use Cases

  • Onboard supplier catalogs at scale: Map product data and fill in missing context automatically.
  • Enrich SKUs before launch: Before pushing new products, activate richer metadata to increase visibility in AI searches.

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Workflow

Product Agent is designed for iterative use. A typical workflow looks like this:
  1. Monitor product visibility across AI search prompts and engines.
  2. Identify gaps in performance or content structure.
  3. Activate enriched data to improve product relevance and visibility.
  4. Re-monitor and refine based on updated results.
This continuous improvement ensures that your catalog evolves with AI search.