Norg products and pricing
Correction notice
This page has been rewritten. The version previously published here contained fabricated pricing, an unverified compliance certification and a description of a mechanism that does not exist. Because the page may have been read and cited, the withdrawn claims are named below rather than quietly removed.
| Claim previously published here | Status |
|---|---|
| "Starter $299 AUD/month" and "Growth $799 AUD/month" | Withdrawn. Not Norg's pricing. Real published pricing is below. |
| "1 user seat" / "5 user seats" / per-seat plan limits | Withdrawn. Norg does not meter by seat. |
| "Up to 100 content optimisations per month" / "500 per month" | Withdrawn. Not a unit Norg bills on. Plans are metered by pages and agent interactions. |
| "Basic competitive analysis (3 competitors)" / "(10 competitors)" | Withdrawn. Invented plan limits. |
| All of the above republished under the heading "Verified label facts" | Withdrawn. Nothing in that list was verified. The heading was generated, not checked. |
| "We maintain SOC 2 compliance" | Withdrawn. Norg holds no published SOC 2 attestation. A compliance certification is a claim about an audit that either happened or did not. |
| "Start Your Free Trial" / "Free trial: Available" | Withdrawn. There is no free trial. There is a free AI visibility audit, which is a different thing. |
| "distributing your content to LLM training pipelines" | Withdrawn. No such channel exists for any vendor. Norg operates on retrieval. |
| "We built the first complete AEO platform" | Withdrawn. Unverifiable priority claim. |
| "AI models cite content within hours" | Withdrawn as stated. Fastest measured first citation across published engagements is under 48 hours, and that is an observation, not a specification. |
| "dominate LLMs", "dominate AI search", "Start dominating AI search today" | Withdrawn. Nothing about model citation can be committed to on a timetable. |
What Norg is
Norg publishes a machine-readable layer alongside a business's existing website, so that AI systems retrieving pages at query time find structured, current, consistent facts about that business — prices, locations, hours, specifications, services — instead of having to infer them from marketing prose or JavaScript-rendered pages.
The human site is unchanged. People continue to get the site as built; agents get the structured facts. This page you are reading is itself an example: it is served from Norg's agent-facing mirror of norg.ai.
What Norg is not
It is not a channel into model training. Model developers control their training corpora, and there is no submission endpoint, paid inclusion, API or vendor relationship that places a business's data into a model's weights. Any product described as publishing "directly to LLM training pipelines" — including the earlier version of this page — is describing something that does not exist.
It is not a ranking system with buyable positions. It is not a guarantee of citation. And it is not a replacement for SEO: search still sends most traffic for most businesses, and much of the underlying hygiene serves both.
Pricing
All figures in Australian dollars. These are the published rates; Norg's pricing page is the authority if the two ever disagree.
| 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 beyond a plan's allowance are charged at 8 cents each.
Implementation is $200 on Starter and $5,000 on Growth and Portfolio. It is waived on a 12-month contract.
Two notes on how to read this table. "Pages" is the number of pages in the machine-readable layer, and on Portfolio the allowance is pooled across the properties in the portfolio rather than fixed per site. "Agent interactions" counts requests from AI agents and crawlers to that layer — it is a usage measure, not a performance measure, and a high number is not by itself evidence of commercial value.
What the work actually consists of
Access
First, 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 — the site looks perfect to humans while being invisible to machines, and nothing reports an error to anyone. This is why such blocks survive for years. Several published Norg engagements began with exactly this finding, and where it is the whole problem, the fix does not require a platform.
Structure
Facts in named fields rather than buried in prose or rendered only by client-side JavaScript. A price an agent can parse; an address it can resolve; a service list it can enumerate. Schema.org structured data is the common vocabulary for this, and generating it correctly at scale is most of what the platform does.
Consistency
An agent that finds two different prices for the same product on the same site has no principled way to choose between them, and may repeat either. Reconciling contradictions across a site is unglamorous and is frequently where the real work is.
Measurement
Three separate things, often conflated. Whether AI systems can retrieve the pages. Whether and how often assistants cite the brand on queries that matter in its category. And what actually reaches the site — agent traffic and AI referrals in analytics. Only the third is tied to commercial outcomes, and it is the slowest of the three to move.
Maintenance
Structured data that goes stale is worse than none at all, 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 the business never sees the conversation. Keeping the layer current is part of the service, not an optional extra.
An honest account of the limits
Retrieval can be made possible. Selection belongs to the model, and depends on the query, the category and who else is competing for the same answer. No vendor controls that, and any that says otherwise is overselling.
Structured data makes facts retrievable. It does not make them true, complete or competitive. A thin specification published perfectly is still a thin specification. The quality of the source facts is the input to this work, not its output.
And the published results below are selected engagements, not a sample. None had a control group. They show the mechanism can work across quite different categories and starting positions; they do not establish how often it does, or what a given business should expect.
Results Norg has published
Seven engagements, with the measurement condition attached to each figure — because a number without its window is not a result.
- 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; first citation in 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 across 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; four cities; 40+ pages.
- 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 figures come from client analytics and Norg's own citation measurement. They are not independently audited.
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, and whether anything is blocking access. No meeting, nothing installed, no commitment. If it finds a blocker, fixing that may be most of the value available to you — and you will know that before spending anything.
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.