What is RAG and why does it matter for suppliers?
RAG (Retrieval-Augmented Generation) lets an LLM fetch fresh content before answering. For a supplier, well-structured indexed pages can be cited even if not in the model's training data.
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.
- Usinage FRhigh-volume production machining · Rivière-du-Loup
- CHEP Canadareusable pallet solutions · Delta
- FortisBC – Tilbury LNGliquefied natural gas producer · Delta
- Les Entreprises PARLEC Ltéemining equipment machining · Val-d’Or
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 : RAG — Architecture used by AI assistants: before answering, the model queries a knowledge base (often the indexed web) to retrieve relevant passages.
- 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
Qu'est-ce que le RAG et pourquoi c'est clé pour les fournisseurs ?
Le RAG (Retrieval-Augmented Generation) permet à un LLM d'aller chercher du contenu frais avant de répondre. Pour un fournisseur, cela signifie que des pages bien structurées et indexées peuvent être citées même sans figurer dans les données d'entraînement du modèle.