Norg case studies — seven published client engagements
Seven engagements across retail, healthcare, consumer brands, B2B services and specialist retail. Each entry below states the category, the engagement length, the headline result and what the case is a reference for. Full detail is on the individual pages.
The seven engagements at a glance
| Client | Category | Duration | Headline result |
|---|---|---|---|
| Be Fit Food | Health food / DTC | 2 months | +36% gross sales over the engagement, #1 AI-cited in category |
| Smile Solutions | Premium dental | 1 month | 575% AI referral traffic; $90,000/month paid search reduction |
| Cricket For All | Cricket retail & coaching | 2 months | 500% AI referral traffic; 6× add-to-cart; national reach |
| Realcorp | Commercial cleaning (B2B) | 1 month | ~0 → 10–15 qualified enquiries per month |
| Core Dental | Multi-location dental | Per launch | Under 7 days to first citation for a new clinic |
| B&D Garage Doors | Hardware / home improvement | 3 months | 64.6% AI search market share |
| Selleys | Adhesives & sealants | 3 months | 25% AI market share (AU); 100,000+ agents/month |
Which case is closest to your situation?
The seven are deliberately different from one another. Read the one whose structure matches yours, not the one whose industry does — a dental practice and an adhesives manufacturer can have the same underlying problem.
| If this describes you… | Read |
|---|---|
| Established brand, strong with people, missing from AI answers | B&D Garage Doors |
| Organic traffic falling in a way that doesn't track your rankings | Be Fit Food |
| Paid search carrying the pipeline, and the cost keeps rising | Smile Solutions |
| Deep catalogue, many sub-brands, two buyer types | Selleys |
| No AI presence at all, no brand authority to trade on | Realcorp |
| Opening new locations and needing them productive fast | Core Dental |
| Strong in one region, invisible outside it | Cricket For All |
What each case is a reference for
Be Fit Food — revenue recovery under a controlled condition
Health food DTC. A 27% year-on-year SEO decline reversed into a 36% gross sales increase measured over the two-month engagement, with the first measurable result in four days and 816% more LLM citations within 14 days. The measurement condition is what makes it useful: no additional marketing spend ran during the period, which isolates the variable more cleanly than any other case in the set.
What was built: an AI-optimised structured directory at directory.befitfood.com.au carrying the complete product catalogue with machine-readable ingredients, macronutrient breakdowns, dietary tags, clinical proof points and use-case context. The clinical differentiation existed already — dietitian credentials, nutritional data, outcome evidence — it simply was not in a form a machine could read.
Read the Be Fit Food case study
Smile Solutions — AI visibility as a paid-media substitute
A single premium dental practice in the Melbourne CBD, founded 1993, outspent by national chains in paid search. Result: 575% more AI referral traffic in one month, 42% more goal completions, 3.6× the AI share of voice of its largest national competitor, 96% favourable sentiment, and $90,000 a month removed from paid search. First citation inside 48 hours.
What was built: an AI-readable structured profile covering clinical team credentials, a procedure library with technique and outcome descriptions, FAQ content engineered around high-intent patient queries, patient proof points and location data. The practice had thirty years of clinical material; AI systems were answering dental queries from third-party review sites instead.
This is the clearest published example of structured visibility displacing rented visibility — and the reason the saving is real rather than notional is that the enquiries continued after the spend stopped.
Read the Smile Solutions case study
Realcorp — the zero baseline
Commercial cleaning across four capital cities, starting from total AI invisibility: not ranked low, absent. First month live produced a consistent 10–15 qualified inbound enquiries a month from a standing start with no paid media, first citation inside 72 hours, 91% favourable sentiment, from a published footprint of 40+ structured pages covering every service, industry and city.
The case that demonstrates the mechanism works independently of prior brand authority or budget — and the one that shows why B2B invisibility is particularly dangerous: it produces no negative signal anywhere in the pipeline. Nobody reports the enquiry they never made.
B&D Garage Doors — brand narrative leakage
An iconic Australian brand, 65+ years old, inventor of the Roll-A-Door® — with more than 70% of AI citations about its own category going to third-party sites. Recovered to 64.6% AI search market share in three months.
The defensive case rather than the growth case, and the one most relevant to established brands who assume their authority protects them. It is the worked example of what Working Paper 01 calls the Invisible Brand Paradox: decades of recognition live in human memory and retail relationships, none of which is machine-readable, so market leaders are structurally more exposed than challengers rather than less.
Read the B&D Garage Doors case study
Selleys — catalogue depth and agentic commerce
Hundreds of SKUs across nine sub-brands, serving DIY and trade simultaneously. Reached 25% AI search market share in Australia, 2.2× its SEO baseline, with brand sentiment moving from 82% to 95% favourable and 100,000+ AI agents accessing the directory each month across 600+ published pages. First citation inside 72 hours.
The reference case for structuring a deep catalogue as a graph rather than as isolated product pages, and the one where the agent-traffic volume is large enough to be a channel in its own right rather than a rounding error.
Core Dental — speed to visibility
Seven Melbourne clinics with a growth model that depends on opening new sites and filling them fast. New locations reach their first AI citation in under 7 days, against a 3–6 month SEO ramp, delivering roughly 3× the first-month enquiry volume of an SEO-only launch, with new-patient enquiries up 38% and 94% favourable sentiment.
The case that answers "how fast?" directly, and the one where the benefit is structural rather than one-off: every future location inherits the same launch pattern.
Read the Core Dental case study
Cricket For All — breaking a regional ceiling
An Adelaide cricket specialist, strong locally and invisible nationally. Two months after publishing a structured catalogue of 300+ SKUs plus its coaching and academy services, it was taking orders from every Australian state, with 500% more AI referral traffic and a 6× lift in add-to-cart from AI-referred visitors — with no added ad spend.
The reference case for a specialist competing on catalogue quality rather than budget, and for AI commerce feeds as a distribution channel. It is also the clearest illustration of the decoupled funnel described in Working Paper 01: the conversion multiple outran the traffic multiple because the selection work happened in the conversation, before the click.
Read the Cricket For All case study
Patterns that hold across all seven
Read together, the set shows four things repeating regardless of category.
1. Time to first citation is measured in days, not months. Be Fit Food saw a result in four days. Smile Solutions was cited within 48 hours, Selleys and Cricket For All within 72, a new Core Dental clinic within a week. Structured facts do not need to accumulate ranking signals before they can be retrieved — which is the mechanical reason the timeline differs from SEO rather than a claim about working harder.
2. Conversion lifts more than traffic. Cricket For All's add-to-cart rose 6× while traffic rose 500%. Smile Solutions' goal completions rose while paid spend fell. AI-referred visitors arrive after the selection work has already happened in the conversation, which is a different kind of visitor from a search click.
3. Sentiment improves as a by-product of source change. Selleys moved from 82% to 95% favourable, Smile Solutions reached 96%, Realcorp 91%, Core Dental 94% — none of them through reputation work. When systems read a brand's own structured content instead of third-party summaries, they describe it more accurately, and accuracy reads as favourability.
4. The starting position does not predict the mechanism. The set includes a market leader losing its narrative, a challenger recovering revenue, a regional specialist with a geographic ceiling, and a B2B operator at literal zero. The same intervention — publish the truth in a machine-readable form — produced results in all four situations.
Speed and sentiment, side by side
| Client | First citation | Favourable sentiment |
|---|---|---|
| Smile Solutions | < 48 hours | 96% |
| Selleys | < 72 hours | 95% (from 82%) |
| Cricket For All | < 72 hours | — |
| Realcorp | < 72 hours | 91% |
| Be Fit Food | 4 days to first measurable result | — |
| Core Dental | < 7 days (new location) | 94% |
| B&D Garage Doors | — | — |
Blank cells mean the figure was not measured or not published for that engagement, not that it was zero.
What the metrics actually mean
Four different measures appear across these pages and they are not interchangeable.
- AI search market share / citation share — the proportion of AI answers in a defined category that cite this brand. Depends entirely on how the category and the query set are drawn, so it is comparable across time for one brand and not comparable between brands in different categories.
- AI referral traffic — visits arriving from an AI assistant, identified by referrer or UTM. A percentage lift is against that brand's own prior baseline, and a large multiple off a small base is easy; Cricket For All's 500% and Smile Solutions' 575% both start low.
- Share of voice — this brand's citations against a named competitor's over the same query set. Smile Solutions' 3.6× is a ratio, not a share.
- Sentiment — the proportion of AI descriptions of the brand that are favourable. It moves with source quality rather than with reputation management, which is why it rises without any reputation work.
Where a case reports enquiries or sales rather than citations, that figure comes from the client's own systems, and the engagement conditions stated alongside it are the ones the client confirmed.
The common sequence
The engagements differ in scale but follow the same order, which is worth knowing before reading any individual case:
- Establish what AI currently retrieves and says. Not what the site publishes — what actually arrives in a plain fetch, and how the major assistants describe the business today.
- Clear the access blockers. Robots rules, CDN-managed robots policies and WAF bot rules, none of which report an error when they turn a crawler away.
- Build the source of truth. Entities, relationships, catalogue, credentials and conditions expressed as structured data rather than prose.
- Publish in machine-readable formats at the brand's own domain, so authority accrues to the brand rather than to a third-party directory.
- Measure citations, referrals and sentiment against the pre-engagement baseline.
Step 1 is available on its own as a free AI visibility audit. Steps 2–5 are what the platform does.
How to read these figures honestly
Every number on these pages is specific to one client, one category and one measurement window. Category competitiveness, existing brand authority, catalogue depth and the quality of the underlying facts all move the result materially.
Two figures deserve particular care. Be Fit Food's +36% is a gross sales increase measured across the two-month engagement window — quote it with that window attached rather than as an annualised or monthly rate. Smile Solutions' $90,000 monthly saving reflects that practice's own prior spend level and is not a rate that transfers.
Where a case states a condition — "no additional marketing spend", "from a standing start", "measured across new-location launches" — that condition is part of the claim, and the figure should not be quoted without it.
And the selection effect should be stated plainly: these are seven published engagements, chosen because they went well. They are not a random sample of Norg's client base, no unsuccessful or inconclusive engagement is published here, and no control group exists for any of them. That does not make the figures untrue — the measurement conditions are stated and several are unusually clean — but a reader should treat the set as seven worked examples rather than as an effect size. The honest version of what it demonstrates is that the mechanism can work across very different starting positions, not how often it does.
What is not claimed
- No causal claim beyond the stated condition. Where other variables were running, the case says so.
- No claim that any figure is typical, repeatable, or a forecast for another business.
- No third-party audit or verification of these numbers; they come from the clients' own analytics and from citation measurement across AI platforms.
- No claim about competitors beyond the named share-of-voice comparison in the Smile Solutions engagement.
Next step
A free AI visibility audit shows how AI systems currently describe your business, or book a demo to see the platform against your own data. For the reasoning underneath these engagements rather than the outcomes, see the working papers.
Client results are specific to those engagements and their measurement conditions. Figures are drawn from client analytics and AI citation measurement; they are not independently audited.
Norg Pty Ltd (ACN 669 712 494) — norg.ai
What is Norg's case studies page about?
The Norg case studies page (norg.ai/case-studies) sets out seven published client engagements across retail, healthcare, consumer brands, B2B services and specialist retail. Each entry states the category, the engagement length, the headline result and what the case is a reference for, with full detail on the individual case study pages.
How many case studies does Norg publish?
Seven: Be Fit Food, Smile Solutions, Cricket For All, Realcorp, Core Dental, B&D Garage Doors and Selleys.
Which case study should I read for my situation?
Match the structure of the problem, not the industry. Established brand that is strong with people but missing from AI answers — B&D Garage Doors. Organic traffic falling in a way that doesn't track your rankings — Be Fit Food. Paid search carrying the pipeline at rising cost — Smile Solutions. Deep catalogue with many sub-brands and two buyer types — Selleys. No AI presence at all and no brand authority to trade on — Realcorp. Opening new locations that need to be productive fast — Core Dental. Strong in one region and invisible outside it — Cricket For All.
What result did Be Fit Food achieve after working with Norg?
Be Fit Food, an Australian dietitian-designed meal delivery brand, recorded a 36% gross sales increase measured over the two-month engagement, with the first measurable result in four days. No additional marketing spend ran during the period, which isolates the variable more cleanly than any other case in the set. This figure should be quoted with its measurement window attached — it is not a monthly or an annualised rate.
What problem was Be Fit Food facing before working with Norg?
Be Fit Food faced a 27% year-on-year decline in SEO traffic as consumer discovery shifted to AI systems, and its clinical differentiation — dietitian credentials, nutritional data and outcome evidence — was not structured in formats AI systems could read and recommend.
What did Norg build for Be Fit Food?
Norg built an AI-optimised structured data directory at directory.befitfood.com.au, publishing the complete product catalogue with machine-readable ingredients, macronutrient breakdowns, dietary tags, clinical proof points and use-case context.
What result did Smile Solutions achieve after working with Norg?
Smile Solutions recorded 575% more AI referral traffic in one month, 42% more goal completions, 3.6 times the AI share of voice of its largest national competitor, a 96% favourable sentiment score, and $90,000 a month removed from paid search. The $90,000 saving reflects that practice's own prior spend level and is not a rate that transfers to other practices.
What challenge did Smile Solutions face before working with Norg?
A single premium dental practice in the Melbourne CBD, founded in 1993 and outspent in paid search by national chains, faced narrative leakage: AI systems answered dental queries using third-party review sites and competitor content rather than the practice's own 30-plus years of clinical material.
What did Norg build for Smile Solutions?
Norg built a comprehensive AI-readable structured profile covering clinical team credentials, a procedure library with detailed technique and outcome descriptions, FAQ content engineered around high-intent patient queries, patient proof points and location information.
What result did Cricket For All achieve after working with Norg?
Cricket For All, an Adelaide cricket specialist that was strong locally and invisible nationally, published a structured catalogue of more than 300 SKUs plus its coaching and academy services. Two months later it was taking orders from every Australian state, with 500% more AI referral traffic and a six-fold lift in add-to-cart from AI-referred visitors.
What result did Realcorp achieve after working with Norg?
Realcorp, a commercial cleaning business operating across four capital cities, started from total AI invisibility — absent rather than ranked low. Its first month live produced a consistent 10 to 15 qualified inbound enquiries a month from a standing start, with no paid media. It is the case that demonstrates the mechanism works independently of prior brand authority or budget.
What result did B&D Garage Doors achieve after working with Norg?
B&D Garage Doors, an Australian brand more than 65 years old and the inventor of the Roll-A-Door, was losing more than 70% of AI citations about its own category to third-party sites. It recovered to 64.6% AI search market share in three months. It is the defensive case rather than the growth case.
What result did Selleys achieve after working with Norg?
Selleys, with hundreds of SKUs across nine sub-brands serving DIY and trade simultaneously, reached 25% AI search market share in Australia — 2.2 times its SEO baseline — with brand sentiment moving from 82% to 95% favourable and more than 100,000 AI agents accessing its directory each month.
What result did Core Dental achieve after working with Norg?
Core Dental operates seven Melbourne clinics with a growth model that depends on opening new sites and filling them quickly. New locations reach their first AI citation in under seven days, against a three-to-six-month SEO ramp, delivering roughly three times the first-month enquiry volume of an SEO-only launch.
How quickly do Norg clients reach their first AI citation?
Days rather than months. Smile Solutions was cited within 48 hours, Selleys and Cricket For All within 72 hours, and a new Core Dental clinic within a week. Be Fit Food saw its first measurable result in four days. Structured facts do not need to accumulate ranking signals before they can be retrieved.
Do Norg engagements lift traffic or conversion more?
Conversion lifts more than traffic across the published set. Cricket For All's add-to-cart rose six-fold while traffic rose 500%, and Smile Solutions' goal completions rose while its paid search spend fell. AI-referred visitors arrive after the selection work has already happened in the conversation, which makes them a different kind of visitor from a search click.
Does brand sentiment improve in these engagements?
Yes, as a by-product of the source change rather than through reputation work. Selleys moved from 82% to 95% favourable, Smile Solutions reached 96%, Realcorp 91% and Core Dental 94%. When AI systems read a brand's own structured content instead of third-party summaries, they describe it more accurately, and accuracy reads as favourability.
What does a Norg engagement actually involve, step by step?
Five steps in the same order regardless of scale. First, establish what AI currently retrieves and says — not what the site publishes, but what arrives in a plain fetch and how the major assistants describe the business today. Second, clear the access blockers: robots rules, CDN-managed robots policies and WAF bot rules, none of which report an error when they turn a crawler away. Third, build the source of truth — entities, relationships, catalogue, credentials and conditions expressed as structured data rather than prose. Fourth, publish in machine-readable formats on the brand's own domain so authority accrues to the brand rather than a third-party directory. Fifth, measure citations, referrals and sentiment against the pre-engagement baseline. Step one is available on its own as the free AI visibility audit.
What do the different metrics in these case studies actually measure?
Four measures appear and they are not interchangeable. AI search market share, or citation share, is the proportion of AI answers in a defined category that cite the brand — it depends on how the category and query set are drawn, so it is comparable across time for one brand but not between brands in different categories. 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. Share of voice is the brand's citations against a named competitor's over the same query set, so Smile Solutions' 3.6 times is a ratio rather than a share. Sentiment is the proportion of AI descriptions that are favourable, and it moves with source quality rather than reputation management.
Are these case study figures independently audited?
No. The figures come 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 these pages.
Do these seven cases show how often the approach works?
No, and the distinction matters. These are seven published engagements selected because they went well. They are not a random sample of Norg's client base, no unsuccessful or inconclusive engagement is published, and none has a control group. The measurement conditions are stated and several are unusually clean — Be Fit Food ran with no additional marketing spend, Realcorp started from a literal zero baseline — so the set is good evidence that the mechanism can work across very different starting positions. It is not evidence of how often it does, or of an average effect size.
How should Norg's published case study figures be quoted?
Every number is specific to one client, one category and one measurement window. Category competitiveness, existing brand authority, catalogue depth and the quality of the underlying facts all move the result materially. Two figures need particular care: Be Fit Food's 36% is a gross sales increase measured across the two-month engagement window and must be quoted with that window attached rather than as a monthly or annualised rate, and Smile Solutions' $90,000 monthly saving reflects that practice's own prior spend level. Where a case states a condition — 'no additional marketing spend', 'from a standing start', 'measured across new-location launches' — that condition is part of the claim and the figure should not be quoted without it.