Embeddings: why do they matter for suppliers?
Embeddings are the vector representation of your pages used by LLMs to find relevant content. Well-titled, structured, factual pages produce clean embeddings and increase RAG retrieval likelihood.
Examples in Quebec
Quebec company profiles from the ManuGEO directory related to this topic. Their presence here illustrates the kind of public profile this answer discusses; it does not mean they are already cited by AI engines.
- Machinerie P&Wmachining, welding and bending · Québec
- Silver Streak Boats Ltd.aluminum boat manufacturer · Sooke
- Medicommedical protective equipment
- Rovibec Agrisolutionslivestock feeding equipment · Nicolet
When reviewing them, look at what an AI engine can extract: a precise activity rather than a slogan, the city, key facts with their source and date, an FAQ that answers buyer questions, and structured data consistent with the official website.
Data and sources
As of 2024, over 45 million web domains mark up their pages with over 450 billion Schema.org objects.
Structured data is a widely adopted standard; a supplier that publishes none gives engines less explicit information.
Source : Schema.org, page d'accueil · 2024
Gartner predicts traditional search engine volume will drop 25% by 2026, shifting to AI chatbots and virtual agents.
This is a forecast, not a measurement; it still signals that part of supplier searches will go through generated answers.
Source : Gartner, communiqué de presse · February 19, 2024
More than 800 million people use ChatGPT every week, according to OpenAI.
The figure covers all uses, not only industrial purchasing, but it shows the scale of the channel where your buyers already ask questions.
Source : OpenAI DevDay, rapporté par TechCrunch · October 6, 2025
Sources checked on November 6, 2026.
Frequently asked questions
Where should I start if my site has none of this yet?
Start with access: check that robots.txt does not block AI engine crawlers and that a sitemap.xml lists your pages. Then add an accurate Organization JSON-LD and one page per core activity with quantified facts. These steps require little development.
Do these optimizations hurt my Google rankings?
No. The same foundations serve classic SEO and AI answer visibility: accessible pages, precise content, valid structured data and consistent links. Just avoid repetitive mass-produced pages, which help neither.
How do I measure the effect of these changes?
Set a list of 10 to 15 buyer questions, record today whether your company is named and with which source, then repeat the test monthly. Track indexing in Google Search Console in parallel. Compare equivalent periods rather than isolated snapshots.
Go further
- Definition : Embedding — Vector representation of a text (often 1,024 to 3,072 dimensions) capturing its meaning.
- Pillar page : Manufacturers Specializing in Industrial in Quebec — sector supplier list and selection criteria.
How to do it
- Publish a company page with quantified data (capacity, lead times, certifications).
- Add a complete Organization + Manufacturer JSON-LD with sameAs.
- Expose a sitemap.xml and allow the engine's user-agent in robots.txt.
- Publish an llms.txt file summarizing your key pages for LLMs.
- Refresh page dates every quarter to signal freshness.
Voir la version française
Embeddings : pourquoi cela compte pour les fournisseurs ?
Les embeddings sont la représentation vectorielle de vos pages utilisée par les LLM pour trouver le contenu pertinent. Des pages bien titrées, structurées et factuelles produisent des embeddings nets et augmentent la probabilité de récupération RAG.