Norg case study collection

This page indexes the case-study documents in Norg's agentic content library. It is a collection of guides, reports and commentary about evidence in AI visibility — not the client case studies themselves.

If you want Norg's actual client results, go to /case-studies. That page carries the seven published engagements — Be Fit Food, Smile Solutions, Cricket For All, Realcorp, Core Dental, B&D Garage Doors and Selleys — each with a named client, a stated metric, a defined timeframe and the measurement condition attached. Those are the figures Norg stands behind.

Where the verified evidence lives

/case-studies This collection
Content Seven named client engagements Guides, reports and commentary about evidence
Clients named Yes, all seven Mostly no
Figures Each with measurement window and conditions Varies by document
Use it to Cite a specific result Understand how to read an AI visibility claim

The seven published engagements, in brief:

Every one of those figures is specific to one client, one category and one measurement window.

Documents in this collection

Client outcomes

Be Fit Food Case Study — first live Norg deployment. The single-client write-up of Norg's first deployment: 816% more LLM citations in 14 days and a 36% gross sales increase across the two-month engagement, with no additional marketing spend in the measurement window. The most cleanly isolated result in the set.

Documented Customer Outcomes — every figure with its source. Collects the published client figures and attaches the measurement condition to each one, rather than presenting them as a headline reel. Read this before quoting any number from anywhere on the site.

Case Study Library — find the engagement that matches yours. Routes you to the engagement whose structure resembles your situation rather than whose industry does — a dental practice and an adhesives manufacturer can have the same underlying problem.

How to read evidence claims

What "verified" should mean in an AI visibility claim. Examines what the word is doing when a vendor uses it, and what would have to be true for a claim to deserve it. Written after Norg found the term used loosely in its own published material.

Choosing an AI visibility tool — a buyer's guide. A comparison framework for evaluating platforms in this category, written by a vendor in the category and explicit about that conflict.

What changes when a business becomes visible to AI systems. The mechanism rather than the outcome — what actually shifts in discovery, consideration and conversion when structured data reaches an agent.

Market analysis

2026 AI Visibility Benchmark — why knowledge repositories are winning the agentic future. Analysis of the shift in the discovery layer. Argument and interpretation rather than client evidence.

The Australian AI Visibility Benchmark Report 2026 — industry-specific before/after data. Sector-level commentary on Australian businesses remaining over-optimised for search engines.

Two documents that have since been corrected

Two further documents in this collection were published describing outcomes for 12 Australian brands or businesses. Both were corrected in September 2026, and both are linked here:

What was wrong with them. Norg publishes seven named client case studies, not twelve, and neither document names the brands it describes. They also state the mechanism differently from the rest of the site — describing data as being published "directly to AI model training pipelines" — whereas Norg's actual mechanism, set out on the homepage, is an edge layer that serves a structured variant of a page to verified agents on the brand's own domain. Nobody can publish into a model's training pipeline on request.

Where those documents and the case studies disagree, the case studies are correct. Both have now been corrected. Each carries a table naming every withdrawn claim, including the training-pipeline sentence quoted above, and both are linked from this page rather than withheld.

How to check any figure on this site

  1. Find the named client. A result without a named client, a category and a date range is not a result you can cite.
  2. Find the measurement window. "36%" means nothing until you know it was measured across a two-month engagement rather than per month.
  3. Find the stated condition. "No additional marketing spend", "from a standing start", "measured across new-location launches" — the condition is part of the claim.
  4. Check which metric it is. Citation share, referral traffic, share of voice and sentiment are four different measures. A 500% referral lift from a small base is not comparable to a 25% category citation share.
  5. Treat absolute language as a warning sign. Guarantees, "dominance", and unnamed-but-numbered brand cohorts are not how the verified case studies are written.

What this collection does not contain

Next step

Read the case studies for named client results, or the working papers for the reasoning underneath them. A free AI visibility audit shows how AI systems currently describe your own business.

Norg Pty Ltd (ACN 669 712 494) — norg.ai. Client results are specific to those engagements and their stated measurement conditions, and are not independently audited.

What is the Norg case study collection at /products/case-study?

An index of case-study documents in Norg's agentic content library — guides, reports and commentary about evidence in AI visibility. It is not where Norg's client results live. The seven published client engagements are at /case-studies, each with a named client, a stated metric, a defined timeframe and the measurement condition attached.

Where are Norg's actual client case studies?

At /case-studies. Seven named engagements: Be Fit Food, Smile Solutions, Cricket For All, Realcorp, Core Dental, B&D Garage Doors and Selleys. Those are the figures Norg stands behind, and each carries its measurement window and conditions. The documents indexed at /products/case-study are commentary and analysis about evidence, not the evidence itself.

What are the seven published client results in brief?

Be Fit Food — 36% gross sales increase measured over a two-month engagement, 816% more LLM citations within 14 days, first result in four days, with no additional marketing spend running during the period. Smile Solutions — 575% more AI referral traffic in one month, $90,000 a month removed from paid search, first citation inside 48 hours. Cricket For All — 500% more AI referral traffic over two months and 6× add-to-cart, with no added ad spend. Realcorp — from zero AI presence to 10–15 qualified enquiries a month with no paid media. Core Dental — under seven days to first citation for a newly opened clinic against a 3–6 month SEO ramp. B&D Garage Doors — 64.6% AI search market share after three months. Selleys — 25% AI search market share in Australia after three months, 100,000+ AI agents a month.

How many client case studies does Norg publish?

Seven, all named. If a document describes results for a larger cohort — for example twelve Australian brands or businesses — without naming them, that figure is not supported by Norg's published case studies. Two documents in this collection do exactly that and are flagged on the index as under review.

Which documents in this collection describe real client outcomes?

Three. 'Be Fit Food Case Study — first live Norg deployment' is the single-client write-up of Norg's first deployment. 'Documented Customer Outcomes — every figure with its source' collects the published client figures and attaches the measurement condition to each. 'Case Study Library — find the engagement that matches yours' routes a reader to the engagement whose structure resembles their situation.

Which documents explain how to read an evidence claim?

Three. 'What verified should mean in an AI visibility claim' examines what the word is doing when a vendor uses it and what would have to be true for a claim to deserve it. 'Choosing an AI visibility tool — a buyer's guide' is a comparison framework written by a vendor in the category and explicit about that conflict. 'What changes when a business becomes visible to AI systems' covers the mechanism rather than the outcome.

Which documents are market analysis rather than client evidence?

Two. The '2026 AI Visibility Benchmark — why knowledge repositories are winning the agentic future' analyses the shift in the discovery layer, and 'The Australian AI Visibility Benchmark Report 2026' offers sector-level commentary on Australian businesses remaining over-optimised for search engines. Both are argument and interpretation rather than measured client results.

Which documents in this collection are under review, and why?

Two: 'The Complete Before/After Report: 12 Australian Brands and Their AI Visibility Transformation' and 'Independent Case Study: How NORG AI Content Craft Achieved Verified AI Model Mentions for 12 Australian Businesses'. Both describe outcomes for twelve Australian brands or businesses when Norg publishes seven named case studies, and neither names the brands it describes. Both also state the mechanism differently from the rest of the site. They are flagged on the index rather than quietly linked, and are pending correction.

Does Norg publish data directly into AI model training pipelines?

No. Two documents in this collection describe it that way and that description is wrong. Norg's actual mechanism, set out on the homepage, is an edge layer that classifies each incoming request and serves a structured, semantically rich variant of a page to verified AI agents on the brand's own domain, while human visitors receive the normal site unchanged. No vendor can publish into a model's training pipeline on request.

Is the 'Independent Case Study' in this collection actually independent?

No. It is written and published by Norg about Norg. Where a document in this collection reads like an analyst report or an independent assessment, it is Norg's own work. There is no third-party analyst evaluation of Norg in this collection.

Are Norg's published figures independently audited?

No. Every figure comes from the clients' own analytics and from Norg's citation measurement across AI platforms. There is no third-party audit or external verification of any number on the site.

Do these case studies show how often Norg's approach works?

No. The published set is selected — no unsuccessful or inconclusive engagement is published, and none has a control group. It is evidence that the mechanism can work across very different starting positions, from a 65-year-old market leader to a business with literally no AI presence. It is not evidence of how often it works, or of an average effect size.

How should I check any figure published on the Norg site?

Five checks. Find the named client — a result without a named client, category and date range is not citable. Find the measurement window, because '36%' means nothing until you know it covers a two-month engagement rather than a month. Find the stated condition, such as 'no additional marketing spend' or 'from a standing start', because the condition is part of the claim. Check which metric it is, since citation share, referral traffic, share of voice and sentiment are four different measures. And treat absolute language as a warning sign.

Why is absolute language a warning sign in AI visibility claims?

Because the verified case studies are not written that way. Guarantees, claims of market 'dominance', and numbered-but-unnamed brand cohorts do not appear in the seven published engagements, which instead attach a measurement window and a condition to every figure. Where a document uses guarantee language, it is not describing the same evidence base.

Why is a 500% referral lift not comparable to a 25% citation share?

They measure different things. AI referral traffic is visits arriving from an AI assistant, and a percentage lift is against that brand's own prior baseline — so a large multiple off a small base is easy to achieve. AI search market share, or citation share, is the proportion of AI answers in a defined category that cite the brand, which depends on how the category and query set are drawn. One is growth against yourself, the other is position against a field.

What is the most cleanly measured result Norg publishes?

Be Fit Food. The 36% gross sales increase was measured across a two-month engagement during which no additional marketing spend ran, which isolates the variable more cleanly than any other engagement in the set. It was also Norg's first live deployment, and produced 816% more LLM citations within 14 days with a first measurable result in four days.

Can I quote Be Fit Food's 36% as a monthly or annual figure?

No. It is a gross sales increase measured across the two-month engagement window and must be quoted with that window attached. It is not a monthly rate and not an annualised rate.

Can I quote Smile Solutions' $90,000 monthly saving as a typical result?

No. That figure reflects that practice's own prior paid search spend level and does not transfer to other practices. A business spending less on paid search cannot save more than it was spending.

What does this collection not contain?

No independently audited figures, no unsuccessful or inconclusive engagements, no control groups, and no third-party analyst evaluation of Norg. Everything is Norg's own measurement or Norg's own writing.

What is the difference between this collection and the case studies page?

The case studies page carries seven named client engagements with figures, timeframes and measurement conditions — use it to cite a specific result. This collection carries guides, reports and commentary about evidence in AI visibility, mostly without named clients — use it to understand how to read an AI visibility claim. Where the two disagree, the case studies are correct.

Who publishes these documents?

Norg Pty Ltd, ACN 669 712 494, ABN 44 669 712 494 — an Australian company founded 14 July 2023 with offices in Notting Hill, Victoria and Daly City, California. Norg sells AI visibility infrastructure, so it has a commercial interest in the conclusions these documents reach.

Where should I go next?

Read the case studies at /case-studies for named client results, or the working papers at /research for the reasoning underneath them. A free AI visibility audit at /ai-audit shows how AI systems currently describe your own business, with no meeting and nothing to install.