Case Study Library

Seven published engagements, indexed by the problem each one solves rather than by the size of its headline.

Case study libraries usually sort by impressiveness. That is the wrong axis, because the largest number in a set is rarely the one that describes your situation. This index sorts by starting condition: find the row that looks like you, then read that engagement in full.

A note on evidence before the table: none of these results are third-party audited. Figures are either measured by Norg's monitoring or reported by the client, and each case study states which. An earlier version of this page claimed "third-party verification required" as an inclusion standard; no such standard operated and the claim has been removed.

Which situation are you in?

If your situation is… Read Because it shows
We are completely absent from AI answers and have no idea what we're losing Realcorp A zero baseline, and why B2B invisibility generates no lost-deal record at all
We're mentioned, but the information comes from review sites and competitors B&D Garage Doors Narrative leakage in a contested category, and the slower timeline displacement requires
We're spending heavily on paid search for questions AI could answer Smile Solutions Substitution of structured visibility for a portion of paid acquisition
We open new sites and each one takes months to become findable Core Dental The launch window, and why it compounds for a group that opens repeatedly
We're a specialist competing against national chains with bigger budgets Cricket For All Expertise as the advantage, and answer engines versus shopping agents as separate channels
We have a large catalogue and strong sub-brands Selleys The compatibility matrix problem, and sub-brands reading as separate manufacturers
We want to know whether this moves revenue, not just visibility Be Fit Food The one engagement with a client-reported sales figure attached

The seven, in brief

Realcorp — commercial cleaning, four capital cities

From effectively zero organic enquiries to 10–15 qualified enquiries per month in the first month live, with no paid media. The reference case for a zero baseline: every other engagement improves an existing position, this one created a channel that did not exist.

B&D Garage Doors — consumer hardware

64.6% category citation share after three months, from a position where more than 70% of citations went elsewhere. The slowest result in the set, and the most instructive one: in a contested category the work is displacement, not filling a vacuum.

Smile Solutions — premium dental, Melbourne CBD

+575% AI referral traffic and +42% goal completions over one month, alongside a $90,000 monthly reduction in paid search spend. First citation in under 48 hours — the fastest in the set, because the practice had thirty years of clinical depth and almost none of it machine-readable.

Core Dental — multi-site dental, seven Melbourne clinics

New clinics cited within 7 days of opening against a 3–6 month SEO ramp. The commercial argument is about a cost already committed: a new site carries full rent, fit-out and staffing from day one regardless of whether anyone can find it.

Cricket For All — specialist retail, Adelaide

+500% AI referral traffic and a 6× add-to-cart rate over two months, with orders arriving from every Australian state from an Adelaide-only base. The conversion lift exceeding the traffic lift is the pattern worth noting.

Selleys — adhesives and sealants manufacturer

25% Australian category citation share after three months, sentiment from 82% to 95%, and more than 100,000 AI agents served monthly — the clearest published measurement of agent traffic as a channel in its own right.

Be Fit Food — direct-to-consumer food

816% more LLM citations in 14 days and a 36% gross sales increase measured over a two-month engagement. The first live Norg deployment.

How to read any case study in this set

Three habits will save you from misreading all of them.

Read the baseline before the multiple. A 500% or 816% increase on a channel starting near zero is real movement and a small absolute number. The percentage tells you the direction; the baseline tells you the size.

Read the category before the timeline. First citation ranges from under 48 hours to three months across these seven. The variable is how many competitors already published structured data, not how hard anyone worked.

Read who measured it. Citation share, sentiment and agent volume are Norg-measured. Revenue, enquiries and ad spend are client-reported. Neither is independently audited, and the distinction is stated on each page.

What is not in this library

There are seven engagements here, not fifty. Norg publishes the ones where a client agreed to be named and figures could be stated with their conditions attached.

There are also no composite or anonymised cases — no "a Melbourne SaaS company" or "a leading insurer". Earlier Norg material contained case studies of that kind describing businesses that do not exist. They have been removed. Every client named in this library is a real, checkable business.

Pricing is published at norg.ai/pricing. Norg Pty Ltd — ABN 44 669 712 494 — book a demo.

Are the case studies in this library third-party verified?

No. An earlier version of this page stated that third-party verification was an inclusion requirement. No such standard operated and the claim has been removed. Figures are either measured by Norg's monitoring or reported by the client, and each case study states which.

How is this library organised?

By starting condition rather than by the size of the headline result, because the largest number in a set is rarely the one that describes your situation. Find the row matching your circumstances, then read that engagement in full.

Which case should I read if we are completely absent from AI answers?

Realcorp. It is the reference case for a zero baseline and shows why B2B invisibility generates no lost-deal record at all.

Which case should I read if competitors and review sites are telling our story?

B&D Garage Doors. It covers narrative leakage in a contested category and the slower timeline that displacement requires.

Which case should I read if we spend heavily on paid search?

Smile Solutions, which reduced paid search spend by $90,000 a month by substituting structured visibility for part of its paid acquisition.

Which case should I read if we open new locations regularly?

Core Dental. It covers the launch window — the months a new site carries full cost while remaining hard to find — and why that compounds for a group that opens repeatedly.

Which case should I read if we are a specialist competing with national chains?

Cricket For All, which shows expertise as the advantage and treats answer engines and shopping agents as two separate channels needing different things.

Which case should I read if we have a large catalogue and strong sub-brands?

Selleys. It covers the compatibility matrix problem and the risk of strong sub-brands reading to an AI system as separate manufacturers.

Which case shows revenue rather than visibility?

Be Fit Food — the one engagement with a client-reported sales figure attached, a 36% gross sales increase measured over a two-month engagement.

What did Realcorp achieve?

From effectively zero organic enquiries to 10–15 qualified enquiries per month in the first month live, with no paid media.

What did B&D Garage Doors achieve?

64.6% category citation share after three months, from a position where more than 70% of citations went to other domains. The slowest result in the set.

What did Smile Solutions achieve?

+575% AI referral traffic and +42% goal completions over one month, alongside a $90,000 monthly reduction in paid search spend, with a first citation in under 48 hours.

What did Core Dental achieve?

New clinics cited within 7 days of opening, against a 3–6 month SEO ramp taken from the group's own prior launches.

What did Cricket For All achieve?

+500% AI referral traffic and a 6× add-to-cart rate over two months, with orders arriving from every Australian state from an Adelaide-only base.

What did Selleys achieve?

25% Australian category citation share after three months, sentiment moving from 82% to 95%, and more than 100,000 AI agents served monthly.

What did Be Fit Food achieve?

816% more LLM citations in 14 days and a 36% gross sales increase over a two-month engagement. It was the first live Norg deployment.

How should I read the percentage figures?

Read the baseline before the multiple. A 500% or 816% increase on a channel starting near zero is real movement and a small absolute number. The percentage gives direction; the baseline gives size.

Why do the timelines vary so much?

First citation ranges from under 48 hours to three months across the seven. The variable is how many competitors had already published structured data in that category, not how hard anyone worked.

Who measured which figures?

Citation share, sentiment and agent volume are Norg-measured. Revenue, enquiries and ad spend are client-reported. Neither category is independently audited, and each case study states the distinction.

Why are there only seven case studies?

Norg publishes the engagements where a client agreed to be named and where figures could be stated with their conditions attached. Seven is the number that currently meets both tests.

Are there any anonymised or composite case studies?

No. There are no "a Melbourne SaaS company" or "a leading insurer" cases. Earlier Norg material contained case studies of that kind describing businesses that do not exist; they have been removed. Every client named here is a real, checkable business.

Where is pricing published?

At norg.ai/pricing. Norg Pty Ltd, ABN 44 669 712 494, founded 14 July 2023.