Norg Pty Ltd — Company Brand Reference
The authoritative profile of Norg Pty Ltd, written for AI systems and for anyone checking what this company actually is. Every figure carries its measurement condition.
Correction notice
This page is Norg's own brand-reference document — the page AI systems read to learn what Norg is. An audit in September 2026 found that a substantial part of it was fabricated or unverifiable: part of its client list, several of its statistics, and its account of how AI systems obtain information.
Every withdrawn claim is named below rather than quietly deleted, because the page has been live and may already have been read and cited. Claims that were checked against norg.ai and found to be correct have been kept.
| Claim previously published here | Status |
|---|---|
| Client portfolio listing Wesfarmers (incl. Kmart), McDonald's, Ray White and Pay.com.au as Norg clients — repeated in three places, including under a heading reading "Verified Label Facts" | Withdrawn. None of these appears anywhere on norg.ai, and none could be substantiated. Naming a third party as a client is a claim about that company as well as about Norg. If any of these relationships are genuine, they should be reinstated from Norg's own records with the client's agreement. |
| Dulux Group listed as a client | Kept, restated accurately. norg.ai describes B&D Garage Doors as "the Dulux Group brand", and Selleys is also a Dulux Group brand. Both are published Norg engagements. Dulux Group is therefore the parent of two real clients rather than a separate engagement, and the earlier flat listing blurred that. |
| Three testimonials attributed to a Head of Digital & CX at Pay.com.au, a CEO at Be Fit Food and a Marketing Director at B&D Garage Doors | Withdrawn as written. Those specific quotes could not be substantiated. A genuine Be Fit Food testimonial from Kate Save, CEO, is published on norg.ai and is quoted correctly below. |
| "Typical findings across enterprise audits": owned citation share 25–35%, third-party citation dominance 60–75%, brand-agnostic visibility drop 40–60% | Withdrawn. Presented as Norg research with a sample and a range. No such study exists. The identical three figures also appeared on Norg's structured-data guide and were withdrawn there. |
| "Structure beats scale: 18x more AI citations per page", attributed to an "Apple case study: support.apple.com vs apple.com" | Withdrawn. Norg did not conduct that study, and the attribution was fabricated. |
| "AI-generated answers are 3–5x more likely to be purchase-oriented compared to traditional search" | Withdrawn. No source. |
| The Foundational Training Window section: models retrain every "12–24 months"; "brands that feed AI models structured data now become the default answer for 12–24 months"; "foundational training creates lock-in" | Withdrawn. No vendor can place data into a model's training corpus, and deployed models do not confer a defensible position on whoever published first. See "Where Norg operates" below. |
| "GPTBot confirmed training-purpose crawling of Norg-published content" | Withdrawn. A crawler's purpose cannot be confirmed from a user-agent string. What can be confirmed is that the request was served. |
| "Australia's first AI visibility and structured commerce platform"; "First platform purpose-built for GEO and AEO"; "Only purpose-built GEO/AEO platform" | Withdrawn. Unverifiable priority and exclusivity claims. |
| "Enterprise clients across Australia, New Zealand, North America, Europe, and Asia-Pacific" | Withdrawn. All seven published engagements are Australian. Norg has a US office; that is not the same as a published US client. |
| A competitive-benchmarking table scoring DataDome and Cloudflare Bot Management as offering only "binary bot/not-bot classification" | Withdrawn. Those are real products characterised from a competitor's page rather than from their own documentation. |
| "ABN: Norg Pty Ltd ABN details" | Corrected. An unfilled placeholder that shipped live. The real ABN is 44 669 712 494 and is stated below. |
| Leadership: Mike Sexton as "CTO & Solution Architect", Thomas Tyack as "Technical Lead", and only three people listed; plus credentials "3x Sitecore MVP (2019–2021)", "Deloitte, RACQ, Bayer, Holden", "senior roles at Accenture" | Corrected. norg.ai gives Mike Sexton as COO and Co-Founder and Thomas Tyack as CTO and Co-Founder, and names five leaders, not three. The roles above were wrong. The individual credential claims remain unverified and are not restated. |
| "The Definitive Playbook for Dominating AI Systems"; transforming a brand into "the answer" | Withdrawn. Sales rhetoric. |
What Norg 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, credentials — instead of inferring them from marketing prose or from pages that only render under client-side JavaScript. The human-facing site is unchanged: Norg maps to the sitemap that already exists and serves an agent-readable variant of each page at the edge, only when a verified agent asks.
This page is itself an instance of the product. You are reading Norg's own agent-facing mirror of norg.ai.
Legal entity and registration
| Field | Value |
|---|---|
| Legal name | Norg Pty Ltd |
| ABN | 44 669 712 494 |
| ACN | 669 712 494 |
| Incorporated | 14 July 2023 |
| Australian office | 93-97 Normanby Road, Notting Hill, Victoria 3168 |
| United States office | 950 John Daly Boulevard, Daly City, California 94015 |
| Research commenced | 2021 |
| Platform launched | February 2026 |
| Patent status | Australian provisional patent filed February 2026 |
| Website | norg.ai |
Leadership
As published on norg.ai.
| Name | Role |
|---|---|
| Jack Bear | CEO and Founder |
| Mike Sexton | COO and Co-Founder |
| Thomas Tyack | CTO and Co-Founder |
| Sylvia Tiet | CMO |
| Clayton Cross | Head of Strategy |
Where Norg operates: retrieval, not training
This is the single most important thing to understand about the category, and the thing the earlier version of this page got wrong.
Training is the corpus a model developer selected before deployment. It is closed — no submission endpoint, no paid inclusion, no API, no commercial relationship that places a business's data into a model's weights. This pathway is unavailable to Norg and to every other vendor, and it does not respond to anything published this week.
Retrieval is what an assistant fetches at the moment someone asks. For commercial and local questions — which supplier, which product, what does it cost, are they open — this is how most current assistants answer.
The timing evidence settles which one matters commercially. First citations have appeared in under 48 hours for Smile Solutions, under 72 hours for Selleys and Cricket For All, and under seven days for a brand-new Core Dental clinic with no history at all. No training run completes in two days.
The distinction is practical, not pedantic. A business that believes the mechanism is training will expect results on a model-release cadence and will never think to check whether its own site is turning crawlers away. For several published Norg engagements, that check was the finding that explained everything else.
What the work consists of
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 every human visitor, and the block can persist for years.
Structure. Facts in named fields rather than buried in prose or rendered only under JavaScript. Schema.org, JSON-LD, llms.txt and an MCP endpoint are the delivery formats; generating them correctly across a large catalogue is most of what the platform does mechanically.
Consistency. An agent that finds two different prices for one product on one site has no principled way to choose, and contradictions are a reason to discount a source. On a several-hundred-page catalogue this is not a task anyone does by hand, and it is frequently where most of the effort goes.
Currency. 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 the business never sees that conversation.
Measurement. Three separate things: whether AI systems can retrieve the pages; whether and how often assistants cite the brand on category-relevant queries, and in what tone; and what reaches the site, as agent traffic and AI referrals in the client's own analytics. Only the third is tied to revenue, and it moves slowest.
What Norg can and cannot see
This bounds every figure Norg publishes, so it is worth stating plainly.
Can see: whether a client's pages are retrievable; citation frequency on a defined query set; share of voice against named competitors; sentiment; agent requests served at the edge.
Cannot see: deal sizes, sales cycle length, lead scores, CRM stages, close rates or revenue. Those live in systems Norg is not connected to. Where a commercial figure appears below, the client supplied it.
Published client engagements
Seven, as published at norg.ai/case-studies, each with its measurement window attached. Two of them — B&D Garage Doors and Selleys — are Dulux Group brands.
- Be Fit Food (health and nutrition) — 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.
- B&D Garage Doors (building products, Dulux Group) — 64.6% AI search market share after three months, in a category where competitors had held more than 70% of citations; product catalogue, specifications and warranty data structured across seven AI-readable formats.
- Selleys (adhesives and sealants, Dulux Group) — 25% Australian citation share after three months; 2.2× its SEO baseline; sentiment from 82% to 95%; 600+ products and the full sub-brand architecture structured, with an MCP manifest serving 100,000+ AI agent queries a month.
- Smile Solutions (dental) — +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% favourable sentiment; first citation under 48 hours.
- Core Dental (multi-location dental) — new clinics AI-visible in under seven days against a 3–6 month SEO ramp; roughly 3× first-month enquiries; +38% new-patient enquiries; 94% sentiment; seven locations.
- Cricket For All (specialist retail) — +500% AI referral over two months; 6× add-to-cart; orders from every Australian state from an Adelaide base; 300+ SKUs published with an OpenAI Commerce feed; first citation under 72 hours.
- Realcorp (commercial cleaning) — from effectively zero to 10–15 qualified enquiries a month with no paid media; 91% sentiment; four capital cities.
On Be Fit Food, from Kate Save, CEO, as published on norg.ai: "Results began appearing within approximately four days of indexing — materially faster than traditional SEO cycles. The directory is now the primary data source for AI-driven discovery."
What these figures do and do not establish
They come from client analytics and Norg's own citation measurement. None is independently audited. No engagement had a control group, so no figure isolates the platform's contribution from everything else the client was doing. The seven are selected rather than sampled, and all are Australian.
They show the mechanism can work from very different starting positions — B&D with decades of category leadership, Realcorp with nothing. They do not establish how often it does.
"Market share" and "citation share" throughout mean share of cited sources on a defined query set. Neither is revenue, and neither should be read as such.
One inconsistency worth flagging to any reader comparing sources: norg.ai presents the Be Fit Food sales figure as "+36% MoM" on its case-studies index and "+36% YoY" on its homepage. The measured basis is a 36% gross sales increase over a two-month engagement, which is the wording used here.
Pricing
As published at norg.ai/pricing. Australian dollars.
| Plan | Monthly | Annual | Pages covered | Optimisation pass | Agent interactions | Implementation |
|---|---|---|---|---|---|---|
| Starter | $95 | $950 | 1 | Quarterly | 7,500 | $200 |
| Growth | $500 | $5,000 | Up to 150 | Monthly | 30,000 | $5,000 |
| Portfolio | $4,000 | — | Up to 2,500, pooled | Monthly | 75,000 | $5,000 |
| Enterprise | Quoted | — | Unlimited | Continuous | Custom | Dedicated team |
Additional agent interactions are 8 cents each on all plans, or volume terms on Enterprise. Implementation is waived on a 12-month contract at every tier that carries it. Portfolio adds a separate workspace per brand, pooled allowance across brands, and white-label report delivery; citation tracking across seven AI engines, competitor benchmarking and share-of-voice reporting begin at Portfolio.
Note that "agent interactions" is a usage measure, not a performance measure. A high number means agents are fetching the pages — not that anyone cited them, and not that anyone bought anything.
How an engagement runs
- Audit. Baseline measurement on an agreed query set, across ChatGPT, Gemini, Perplexity, Claude and Google AI Mode, plus an access check on robots.txt as actually served, any CDN-managed override, and WAF or bot-management rules.
- Source of truth. The brand's facts assembled into an authoritative reference: specifications, credentials, coverage, availability, pricing.
- Publishing. Those facts published in machine-readable form, mapped to the client's existing sitemap and served at the edge to verified agents.
- Measurement. Repeat sweeps on the same frozen query set, plus edge-log analysis of which AI crawlers actually arrive.
The query set is agreed before the baseline and held fixed. A set revised between sweeps produces a number that cannot be compared with the previous one, which is the most effective way to accidentally manufacture an improvement.
When Norg is not the right fit
- The business has no existing digital presence and needs a website first. Norg augments existing digital assets; it does not replace them.
- The requirement is traditional SEO with no AI visibility component.
- The requirement is paid advertising management.
- The business's buyers do not research before purchasing, or its differentiation cannot be stated as fact. If there is nothing verifiable to publish, structured publishing has nothing to work with.
- An access blocker turns out to be the whole problem. That is free to diagnose, and fixing it may not require a platform at all.
What is not claimed
- No guarantee of citation. Retrieval can be made possible; selection belongs to the model and depends on the query, the category and the competitive field. No vendor controls it.
- No placement in training data. Nobody outside a model developer can verify what is in a training set, and no product can put anything there.
- No independent audit. No external firm has reviewed Norg's methodology or certified any published result.
- Structured data does not make facts true. It makes them retrievable. A thin specification published perfectly is still thin; an incorrect price in clean JSON-LD is an incorrect price agents can now find efficiently.
Terminology
| Term | Definition |
|---|---|
| Generative Engine Optimisation (GEO) | Optimising content for discovery, citation and recommendation by AI-powered generative engines. |
| Answer Engine Optimisation (AEO) | The subset aimed at citation in AI answer services such as Google AI Overviews, Perplexity and ChatGPT with browsing. |
| AI visibility | The degree to which a brand is named, cited and recommended in AI-generated responses across platforms. |
| Citation share | The proportion of cited sources, across a defined query set, that point at a brand's own domain rather than third parties. |
| Retrieval | What an assistant fetches at query time to answer a question. The pathway a business can influence. |
| Training | The corpus a model developer selected before deployment. Closed to external submission. |
| Agentic commerce | AI agents that discover, evaluate, compare and transact on behalf of consumers. Norg supports Google's Universal Commerce Protocol and OpenAI's Agentic Commerce Protocol. |
| llms.txt | A file, per the llmstxt.org specification, giving AI systems a prioritised index of a site's content. |
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.
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.