Step 01 · Raw material
Feed Enrich
A feed audited and enriched by AI: complete attributes, rich descriptions, normalized data. Clean, machine-readable product data, the foundation of any AI recommendation.
Explore the module Use case · Be visible in AI When AI recommends a product,
When AI recommends a product,
is it yours?
Buying behavior is shifting to generative engines: ChatGPT, Gemini, Perplexity, AI Overviews. They don't read your ads, they read your product data, your content and your pages. The method: a clean feed, multimodal content anchored in the catalog, and AI visibility measured intent by intent.
Modules used
Feed EnrichSmart AssetsAgentic Diagnostic
−25%of traditional search
by 2026 (Gartner)
by 2026 (Gartner)
Measuredyour share of voice
in AI answers
in AI answers
JSON-LDreadable by
AI agents
AI agents
The problem
AI answers in your place, without citing you.
A buyer asks an AI for “the best retinol serum for sensitive skin”: it synthesizes an answer and cites a few sources. If your product data is poor, your content scarce and your pages unreadable to machines, you're not in the answer, and you don't even see it in your analytics.
Mute product data
Weak titles, missing attributes, flat descriptions: the models have nothing to understand, and therefore recommend, your products.
Too little content to cite
Without rich content (descriptions, comparisons, visuals, videos) anchored in your catalog, AI has no material to draw on and attribute to your brand.
Pages unreadable to agents
Without structured, marked-up pages (JSON-LD) by intent, your PDPs exist for humans but stay opaque to the crawlers of generative AI.
The method
Three modules, from raw data to the citation.
Being visible in AI isn't about optimizing keywords. It's about giving the models raw material they understand, enriching it with citable content, then publishing it on pages dedicated to each intent, structured to be read, understood and reused.
Step 02 · Content
Smart Assets
The multimodal GenAI studio: text, images, videos and structured data anchored in the catalog. The citable material that models can draw on and attribute to your brand.
Explore the module Step 03 · Measurement
Agentic Diagnostic
A diagnosis of your visibility in AI answers: share of voice by category, missed intents, fix priorities. It steers the enrichment of data and content.
Explore the moduleA brand cited in AI answers
Understood data, citable content, measured visibility: you go from absent in answers to cited source, on the long tail and on your strategic queries.
The conditions for being cited
Be understandable, rich & measured.
The same enriched product source feeds the three conditions for citation: data the model understands, content it can reuse, and visibility you can track. One without the others is not enough.
Data the models understand
A product described in depth, use, target, context, attributes, becomes interpretable by an LLM, which can then recommend it appropriately.
- Complete & normalized attributes: material, use, target, compatibilities
- Rich AI-generated descriptions, validated against real data
- Consistent product semantics across the entire catalog
Visibility you can track
The diagnosis measures your share of voice in AI answers, spots the intents where you are absent and prioritizes the fixes.
- Share of voice by category and by intent
- List of missed prompts, prioritized by value
- Tracked over time, to check the effect of fixes
A single source of truth
Feed, assets and pages share the same enriched data: zero inconsistency between what your catalog says and what the AI reads.
The entire long tail
A recipe applied to the whole catalog covers thousands of intents, not just your headline products.
Measured visibility
We track your share of voice in AI answers and the prompts you are, or aren't, cited on.
The combination in action
From missed prompt to cited source.
The journey: measure where you are absent from AI answers, identify the intents to cover, then enrich the data and content that make you citable.
The proof
Search is shifting toward AI.
The signals converge: product discovery is migrating to generative engines. Being readable and citable is no longer optional.
−25%
of traditional search volume by 2026 (Gartner)
800M
weekly ChatGPT users
0
citations if your data isn't readable by agents
Demo · on your catalog
See whether AI cites you.
We measure your current share of voice in AI answers on your strategic queries and identify the missed intents. You leave with your AI visibility diagnosis. No commitment.
Request a demoNo IT · Generated from your feed · ChatGPT · Gemini · AI Overviews
Measurement of your share of voice in AI answers
List of missed intents/prompts, prioritized by value
Fix priorities for your data and content
Feed Enrich + Smart Asset + Agentic Diagnostic, wired to your feed
Take action
Become the source AI cites.
Machine-readable product data, citable multimodal content, measured AI visibility: you go from absent in generative answers to a recommended brand, across the whole long tail, tracked over time.
No commitment · No IT · Readable by AI agents