Agentic commerce
GEO - AI visibility and conversion
Measure, test and convert your AI visibility
- ChatGPT
- Gemini
- AI Mode
- and 5 other tools
Agentic commerce
Product feed for AI and agents
Make your catalog readable by AI
Enrich your catalog's attributes and content with AI, with systematic quality control, feed AI surfaces and measure the impact against a control sub-catalog.
The need
What you tell us
« My products are now discovered on ChatGPT, Google's AI mode, and by agents, which rely on my product feed. Yet my feed is incomplete: missing attributes, poor descriptions, prices and availability sometimes inconsistent with the site. I want to enrich it at scale with AI and know what it delivers. »
- « My product sheets are incomplete »
- « I want to appear in ChatGPT Shopping »
- « My Merchant Center attributes are empty »
- « I want to prepare my catalog for agents »
Our response
- Flow readability audit
- Controlled AI enrichment
- Rollout and testing against a control
Method
Our method in 5 steps
Our stance
The fuel of your experiences
AI engines, agents, search and recommendations all read your product feed. We enrich it, control it, and measure what it delivers.
- Attributes
- QA
- Control
Step 01 : Audit
Measurement of your feed's completeness against Merchant Center specifications, consistency check between site and feed (price, availability, GTIN), and definition of the target taxonomy.
- Completeness against Merchant Center specifications
- Site and feed consistency: price, availability, GTIN
- Target taxonomy and framework
- Prioritization of product families
Step 02 : Pilot
Enrichment of a test product family, writing of prompts and extraction rules, quality control on a sample, and business validation.
- Enrichment of a test family
- Prompts and extraction rules
- Quality control on a sample
- Business validation of the pilot
Step 03 : Industrialization
Deployment of the enrichment pipeline at scale with automatic QA rules, handling of ambiguous cases and the back-catalog.
- Enrichment pipeline at scale
- Automatic QA rules
- Handling of ambiguous cases
- Back-catalogue processing
Step 04 : Deployment & testing
Syndication to Merchant Center, ChatGPT and Ads, powering your search and recommendations, and testing an enriched sub-catalog against a control.
- Syndication to Merchant Center, ChatGPT and Ads
- Powering search and recommendations
- Testing an enriched sub-catalog against a control
- Measurement: prompt panel, Shopping, product sheet conversion
Step 05 : Sustainability
Application of the pipeline to new features, continuous QA with alert thresholds, documentation and handover to your teams to govern the reference framework.
- Pipeline applied to new items
- Continuous QA and alert thresholds
- Documentation and handover
- Governance of the repository
Deliverables
What you get
Taxonomy & attribute repository
Target version, documented.
Enriched and distributed feed
Controlled attributes, AI surfaces powered.
Enrichment pipeline
Reusable for new features.
Quality control framework
Rules, thresholds, review.
Test results
Effect measured against a control sub-catalog.
Who is it for?
Who is this offer for?
Tools used
- Claude
- OpenAI
- Merchant Center
- Shopify
- Akeneo
- Algolia
- E-commerce with a large or technical catalog
- Existing Merchant Center feed, PIM or feed tool in place
- Sponsor: Head of E-commerce, catalog or PIM manager, Head of Acquisition
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To go further
Often associated with
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- AB Tasty
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