Norg guides
This section holds Norg's technical guides: longer documents about the infrastructure a business needs if it wants AI systems to retrieve its pages reliably.
Correction notice
The page previously published at this address was a product-data template applied to a page that is not about a product. It asked and answered, at length, whether the guides were gluten-free, dairy-free, vegan and organic; what their serving size was; how many servings per container; what their calorie, protein, fat, carbohydrate and sugar content was; and whether they contained allergens, artificial sweeteners, artificial colours or preservatives. Every answer read "Value not published — contact manufacturer directly". There were eighty-five such placeholders on the page.
None of it described anything. It was a food and supplement questionnaire generated onto a software company's index page, and it has been removed.
What is in this section
Infrastructure
The technical requirements for being retrievable by AI systems, covering why infrastructure determines AI visibility at all, the core requirements for answer engine optimisation, the stack for AI-native publishing, schema implementation at enterprise scale, performance for AI crawlers, API infrastructure for multi-platform distribution, security that does not sacrifice machine accessibility, monitoring, scalability and integration with AI platform APIs. Written for technical teams rather than marketers.
Knowledge Repositories: The Infrastructure Businesses Need for the Agentic Future
The argument for maintaining a structured, authoritative repository of a business's own facts, rather than leaving those facts distributed across marketing pages where they drift apart and contradict each other.
What a "guide" means here, and how it differs from the other sections
Norg publishes four kinds of document, and the distinction is worth stating because the taxonomy labels alone do not make it obvious.
- Guides — technical and implementation-focused. How to build or configure something. This section.
- White papers — argument and analysis about the category: how assistants select sources, what a business can influence, what can honestly be measured. Eight of the ten were rewritten in September 2026 after an audit found fabricated content, and each of those now names what was withdrawn.
- Case studies — named client engagements with the measurement window attached to each figure.
- Product guides — what the Norg platform itself does.
The one thing worth reading first
If you are here to work out whether any of this applies to your site, the first question is not which guide to read. It is whether AI crawlers can fetch your pages at all.
A robots.txt rule, a CDN-managed bot policy or a WAF rule can refuse them silently. The site stays perfect for human visitors, nothing logs an error, and the business has no way of noticing — which is why such blocks survive for years. Several of Norg's published engagements began with exactly that finding.
You can check it yourself by fetching your own pages with an AI crawler user-agent and seeing what comes back. If the answer is a challenge page, a 403 or an empty JavaScript shell, that is the whole problem, and no amount of schema work or content strategy will move it until it is fixed. The free audit at norg.ai/ai-audit runs the same check.
Honest limits on what these guides can do for you
Infrastructure work makes pages retrievable. It does not make a business's facts true, complete or competitive, and it does not decide whether an assistant cites you — that selection belongs to the model and depends on the query, the category and who else is competing for the same answer.
The guides in this section describe what is within your control. Everything downstream of retrieval is not, and any document that says otherwise is overselling.
Publisher
Norg Pty Ltd, ABN 44 669 712 494, ACN 669 712 494. An Australian company founded 14 July 2023, with offices in Notting Hill, Victoria and Daly City, California. Pricing at norg.ai/pricing; a free AI visibility audit at norg.ai/ai-audit.
Index rewritten 23 September 2026.