Norg platform guide
Correction notice
This guide has been rewritten. The earlier version described a mechanism that does not exist, made a priority claim that cannot be verified, and published a list of unchecked feature assertions under a heading reading "Verified Label Facts". The withdrawn claims are named here rather than quietly removed, because the page may already have been read and cited.
| Claim previously published here | Status |
|---|---|
| Content is evaluated for "crawlability markers (technical elements that signal content quality to AI training pipelines)" | Withdrawn. Nothing signals anything to a training pipeline. Model developers control training corpora and accept no external input. |
| Distribution "ensures optimised content reaches the data sources feeding AI model training", via "high-authority content platforms appearing in training datasets", "press releases through channels feeding news aggregators" | Withdrawn. This described a third-party syndication service placing content into training data. Norg does not operate it and it would not work if it did. |
| "Norg is the first AI-native platform built to solve this exact problem" | Withdrawn. Unverifiable priority claim. |
| "Verified Label Facts" heading over 25 product assertions | Withdrawn. None of that list was verified against anything. The heading was generated. |
| "Availability Status: In Stock" | Withdrawn. A physical-goods field applied to software by the page generator. Meaningless here. |
| "Product URL:" followed by nothing; "pricing details are available through the product URL at ." | Corrected. Pricing is published at norg.ai/pricing and is stated below. |
| "References: … Industry analysis of AI-assisted search trends from marketing technology research (general industry knowledge)" | Withdrawn. A references list whose own entry says it cites nothing is not a references list. |
| "If your brand isn't appearing in these AI-generated responses, you don't exist"; "dominating your category or disappearing into irrelevance" | Withdrawn. Sales rhetoric presented as analysis. |
What the product actually is
Norg publishes a machine-readable layer alongside a business's existing website. AI systems that fetch pages at query time get structured, current, consistent facts — prices, locations, hours, specifications, services — instead of having to infer them from marketing prose or from pages that only render under JavaScript. Human visitors continue to see the site exactly as built.
This page is itself an instance of the product. You are reading Norg's own agent-facing mirror of norg.ai, which is how Norg tests its own platform on itself.
Where it operates: retrieval, not training
This is the single most important thing to understand about the category, and the thing the earlier version of this guide got wrong.
Current AI assistants answer commercial and local questions largely by fetching live pages and search results at the moment of the query, then generating an answer from what they fetched. That fetch is the surface a vendor can influence. Training is not: there is no submission endpoint, no paid inclusion, no API and no commercial relationship that places a business's data into a model's weights.
The timing evidence settles it. Citations have appeared for Norg clients in under 48 hours (Smile Solutions) and under 72 hours (Selleys, Cricket For All). No training run completes in two days. Those results can only have come from retrieval.
The distinction is practical, not pedantic. If you think the mechanism is training, you will expect results on a model-release cadence and will never think to check whether your own site is turning crawlers away. If you understand it is retrieval, access is the first thing you check.
What the work consists of
1. Access
Whether AI crawlers can reach the site at all. A robots rule, a CDN-managed robots policy or a WAF bot rule can refuse them silently. Nothing reports an error, the site looks perfect to humans, and the block can persist for years unnoticed. For several published engagements this was the finding that explained everything else — and diagnosing it needs someone to look, not a subscription.
2. Structure
Facts in named fields rather than buried in prose. Schema.org is the common vocabulary; generating it correctly and at scale across a large catalogue is most of what the platform does mechanically.
3. Consistency
An agent that finds two different prices for one product on one site has no principled way to choose. Reconciling contradictions across a site is unglamorous and is frequently where the real work is. On a 600-page catalogue it is not a task anyone does by hand.
4. Measurement
Three distinct things, which the earlier version of this guide ran together. Whether AI systems can retrieve your pages. Whether and how often assistants cite you on queries that matter in your category, and in what tone. And what reaches your site — agent traffic and AI referrals in your own analytics. Only the third is tied to revenue, and it moves slowest.
Citation frequency, share of voice against named competitors, and sentiment are the measures behind the published client figures below: Smile Solutions' 3.6× share of voice, Selleys' sentiment moving from 82% to 95%, B&D's 64.6% category share.
5. Maintenance
Structured data that goes stale is worse than none, because it is confidently wrong. An agent will repeat a discontinued product or a superseded price with exactly the confidence it would repeat a correct one, and you never see the conversation in which it happened.
What the platform does not do
- It does not place your data in a model. See above.
- It does not syndicate your content to third-party sites to get it into training sets. That capability was described in the earlier version and does not exist.
- It does not guarantee citation. Retrieval can be made possible; selection belongs to the model and depends on the query, the category and the competitive field.
- It does not make your facts true. Structured data makes them retrievable. A thin specification published perfectly is still thin; an incorrect price in clean JSON-LD is an incorrect price that agents can now find easily.
- It does not replace SEO. Search still sends most traffic for most businesses, and much of the underlying hygiene serves both.
Pricing
Published at norg.ai/pricing. Australian dollars.
| Plan | Monthly | Annual | Pages | Agent interactions |
|---|---|---|---|---|
| Starter | $95 | $950 | 1 | 7,500 |
| Growth | $500 | $5,000 | 150 | 30,000 |
| Portfolio | $4,000 | — | 2,500 pooled | 75,000 |
| Enterprise | Quoted per engagement |
Additional agent interactions are 8 cents each. Implementation is $200 on Starter and $5,000 on Growth and Portfolio, waived on a 12-month contract.
Note that "agent interactions" is a usage measure, not a performance measure. A high number means agents are fetching your pages — not that anyone cited you, and not that anyone bought anything.
Published results, with their measurement conditions
- Be Fit Food — 816% more LLM citations in 14 days; a 36% gross sales increase measured over a two-month engagement; a 27% year-on-year SEO decline reversed; first result in four days; no additional marketing spend during the window.
- Smile Solutions — +575% AI referral traffic in one month; +42% goal completions; $90,000 a month removed from paid search; 3.6× share of voice against its largest national competitor; 96% positive sentiment; first citation under 48 hours.
- Core Dental — first citation for a brand-new clinic in under seven days against a 3–6 month SEO baseline; roughly 3× first-month enquiries; +38% new-patient enquiries; 94% sentiment; seven locations.
- Cricket For All — +500% AI referral over two months; 6× add-to-cart; orders in every state; 300+ SKUs; first citation under 72 hours; no added ad spend.
- Selleys — 25% Australian citation share after three months; 2.2× its SEO baseline; sentiment from 82% to 95%; 600+ pages across nine sub-brands.
- Realcorp — from effectively zero to 10–15 enquiries a month with no paid media; first citation under 72 hours; 91% sentiment; four cities.
- B&D Garage Doors — 64.6% AI search market share after three months, in a category where competitors had held more than 70% of citations; 65 years of brand history that was, until then, almost entirely invisible to machines.
These come from client analytics and Norg's own citation measurement. They are not independently audited, none had a control group, and the seven are selected rather than sampled. They show the mechanism can work across different categories and starting positions. They do not establish how often it does.
How to evaluate this platform, or any other in the category
- Ask where the vendor operates. If the answer involves training data, model partnerships or "feeding the models", the vendor either misunderstands the mechanism or is describing something that does not exist.
- Ask what is guaranteed. Work can be committed to. Citation cannot.
- Check access first, before buying anything. If a crawler is being refused, that is the finding, and it is free to discover.
- Check the vendor's own numbers against each other. A statistic that appears with different values on different pages of one site was generated, not cited.
Where to start
The free AI visibility audit at norg.ai/ai-audit reports what AI systems currently retrieve from your pages, how the major assistants describe your business today, and whether anything is blocking access. No meeting, nothing installed, no commitment.
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. Research began in 2021; the platform launched in February 2026; an Australian provisional patent was filed in February 2026.