Automated Commerce

The next sales channel Agentic Commerce

Shopping and product discovery is about to change. Preparing your products starts now, learn what to do and how to apply on scale.

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Agentic Commerce - The next sales channel

Agentic Commerce: What Drives Visibility

AI agents demand product data that most webshops can't deliver. This is the gap Automated Commerce closes.

Essential for AI discovery

  • Complete, structured product data

    All attributes: material, size, details, use-case

  • Semantic & intent-rich content

    Titles, descriptions, alt-text aligned with real user intent

  • Localized, multi-market content

    Language & localised relevance per region

  • Deep product context

    Use-cases, comparisons, enriched specifications

  • Up-to-date & dynamic content

    Seasonality, trends, real-time relevance

  • Machine-readable structure

    Schema markup, clean taxonomy, AI-readable fields

No findability and relevance

  • Missing or inconsistent product attributes

    AI cannot confidently match products to queries

  • Generic, keyword-stuffed content

    No real understanding of intent or differentiation

  • Single-language, non-localized catalogs

    Invisible in global or AI-driven discovery

  • Thin product pages

    No context, no trust, no reason to select

  • Static, outdated content

    Irrelevant for current demand or trends

  • Unstructured or messy data

    AI simply skips these products

Get ready in 3 steps

Prepare for agentic commerce in actionable steps, your AI-assistant Auto mate guides and assists.

Frequently Asked Questions

AI agents like ChatGPT Shopping and Perplexity Buy parse structured product data to compare and recommend items. They evaluate attribute completeness, metadata quality, and schema markup. Products with missing data get skipped entirely. The agents do not browse like humans, they read structured data.
AI shopping agents need complete product attributes (size, material, color, weight), unique metadata per product, JSON-LD schema markup, product FAQs, and multi-language content. Most stores have 30-60% of these fields empty. Every missing field is a reason for the agent to skip your product.
Start by checking attribute completeness across your catalog. Fill gaps in product data: materials, dimensions, care instructions. Add unique metadata per product. Implement schema markup. Create content in multiple languages. The stores that prepare now will capture the next wave of commerce.
Connect your catalog and get an AI-readiness score in minutes. The score checks attribute completeness, metadata quality, schema markup, and content structure for every product. You see exactly which products have gaps and what data is missing, so you know where to start.
Traditional SEO optimizes for Google rankings: meta titles, keywords, backlinks. AI agent readiness goes deeper: complete product attributes, structured data, product FAQs, and multi-language content. AI agents compare products on data completeness, not page rank. You need both, but the requirements are different.
No. AI fills the gaps automatically: missing attributes extracted from images, metadata generated per product, schema markup added, content translated. Set up your enrichment rules once. The platform monitors your catalog continuously and flags new gaps as they appear. Your catalog stays agent-ready as standards evolve.

AI agents are already shopping. Are your products ready?

The shops that prepare their product data now will capture the next wave of commerce. Connect your catalog and get your AI-readiness score in minutes.

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No credit card required - Free to start - AI-readiness score in minutes

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