Documented Customer Outcomes

Seven named clients. Every figure listed with who measured it, over what window, and what it does not prove.

Most vendor outcome pages give you numbers without provenance. This one gives the provenance first, because in AI visibility the provenance is the part that decides whether a number means anything.

Three things are stated for every figure below: who produced it, the measurement window, and the condition it depends on. Where Norg cannot substantiate something, that is said plainly rather than omitted.

A note on verification, stated up front

None of the outcomes on this page are third-party audited. No independent firm has reviewed Norg's methodology or certified any result. Figures are either measured by Norg's own monitoring or reported by the client from their internal systems, and each is labelled accordingly.

Earlier versions of this page implied independent verification that does not exist. That was wrong and has been removed.

The seven engagements

Be Fit Food — direct-to-consumer food

816% more LLM citations Norg-measured, 14 days from publish
36% gross sales increase Client-reported, measured over a two-month engagement

The first live Norg deployment. The sales figure comes from Be Fit Food's own reporting over the engagement period; Norg has no access to the underlying till data and is not attributing the whole of that change to this work alone. Full write-up at norg.ai/case-studies/be-fit-food.

B&D Garage Doors — consumer hardware

64.6% AI search market share Norg-measured, after three months
Prior position: >70% of citations going elsewhere Norg-measured baseline audit

"Market share" here means share of citations, not share of the garage door market and not revenue. The slowest result in the set, because the category was contested — review aggregators and installer directories already held the citations that had to be displaced. Full write-up at norg.ai/case-studies/bd-garage-doors.

Smile Solutions — premium dental, Melbourne CBD

+575% AI referral traffic Client analytics, one month
+42% goal completions Client analytics, same period
−$90,000 per month paid search spend Client-reported; reflects this practice's own prior spend level
3.6× AI share of voice vs largest national competitor Norg-measured, benchmarked against 1300Smiles
96% favourable sentiment Norg-measured across monitored AI platforms
First citation under 48 hours Norg-measured from publish

The $90,000 is the figure most often misread. It is a reduction against one practice's unusually large prior ad budget — it is not a saving available to a practice that was not spending that much. The 96% sentiment score measures how AI systems describe the practice; it is not a clinical quality measure. Full write-up at norg.ai/case-studies/smile-solutions.

Core Dental — multi-site dental, seven Melbourne clinics

First citation on a new clinic in under 7 days Norg-measured from opening
Equivalent SEO ramp: 3–6 months Client baseline, from this group's own prior launches
~3× first-month enquiry volume Client-reported, vs a comparable SEO-only launch
+38% new-patient enquiries at launch clinics Client-reported
94% favourable sentiment Norg-measured

The 38% was measured at clinics simultaneously running everything else a clinic opening involves. It is the change over the window, not an isolated effect of this work. Full write-up at norg.ai/case-studies/core-dental.

Cricket For All — specialist retail, Adelaide

+500% AI referral traffic Client analytics, two months
6× add-to-cart rate on AI referral traffic Client analytics, same period
Orders from every Australian state Client-reported, from an Adelaide-only base
300+ SKUs published; first citation under 72 hours Norg-measured
No additional ad spend Client-confirmed

The percentage runs off a low base — AI referral traffic was near zero beforehand. The geographic change is the more meaningful outcome. Full write-up at norg.ai/case-studies/cricket-for-all.

Selleys — adhesives and sealants manufacturer

25% AI search market share, Australia Norg-measured, after three months
2.2× growth against SEO baseline Norg-measured, vs the brand's own prior position
Sentiment 82% → 95% favourable Norg-measured across monitored platforms
100,000+ AI agents accessing the directory monthly Norg-measured; requests served, not modelled
600+ structured product pages across nine sub-brands Published count

The agent request volume is the most directly countable figure in the set — it is server-side request data rather than an inference about model behaviour. Full write-up at norg.ai/case-studies/selleys.

Realcorp — commercial cleaning, four capital cities

~0 → 10–15 qualified enquiries per month Client-reported, first month live
No paid media Client-confirmed
First citation under 72 hours; four cities represented Norg-measured
91% favourable sentiment Norg-measured

The zero baseline is the client's own measured starting position, and it is the most important number on the row — a B2B business absent from AI shortlists generates no lost-deal record at all. Full write-up at norg.ai/case-studies/realcorp.

What these outcomes have in common, and what they don't

In common: every engagement published structured, machine-readable facts the business already knew and had never made readable — specifications, credentials, coverage, compatibility, availability.

Not in common: the magnitude and the timeline. Time to first citation ranges from under 48 hours to three months. The driver of that spread is how contested the category already was, not how the work was done.

That variance is why no figure on this page should be read as typical. There is no typical. There is a mechanism that works under stated conditions, and seven instances of it with the conditions attached.

How to sanity-check any of this yourself

Every client named above is a real business with a live website. The level of claim you can check without trusting anyone is whether they currently appear in AI answers for their category — ask ChatGPT, Gemini, Perplexity or Claude and read which sources get cited.

Do the same for your own business first. That gives you a baseline nobody sold you.

All seven engagements are published in full at norg.ai/case-studies. Pricing is at norg.ai/pricing. Norg Pty Ltd — ABN 44 669 712 494 — book a demo.

Are the outcomes on this page third-party verified?

No. No independent firm has reviewed Norg's methodology or certified any result. Every figure is either measured by Norg's own monitoring or reported by the client from their internal systems, and each is labelled on the page accordingly. An earlier version of this page implied independent verification that does not exist; that was wrong and has been removed.

What does this page actually provide?

Seven named clients with every published figure listed alongside who produced it, the measurement window, and the condition it depends on.

What is the difference between a Norg-measured and a client-reported figure?

Norg-measured figures come from monitoring across ChatGPT, Gemini, Perplexity, Claude and Google AI Mode — citation share, sentiment, time to first citation, agent request volumes. Client-reported figures are visible only inside the client's business — revenue, enquiry counts, ad spend — and Norg has no independent access to them.

What did Be Fit Food achieve?

816% more LLM citations, Norg-measured over 14 days from publish, and a 36% gross sales increase, client-reported over a two-month engagement. Norg has no access to the underlying sales data and does not attribute the whole of that change to this work alone.

What did B&D Garage Doors achieve?

64.6% AI search market share after three months, Norg-measured, from a baseline where more than 70% of category citations pointed at other domains. Market share here means share of citations — not share of the garage door market, and not revenue.

Why was B&D the slowest result in the set?

Because the category was contested. Review aggregators and installer directories already held the citations, so the work was displacement rather than filling an empty space.

What did Smile Solutions achieve?

+575% AI referral traffic and +42% goal completions from client analytics over one month; a $90,000 per month reduction in paid search spend, client-reported; 3.6× the AI share of voice of its largest national competitor and 96% favourable sentiment, both Norg-measured; and a first citation in under 48 hours.

Can any dental practice save $90,000 a month?

No. That figure is a reduction against one practice's unusually large prior ad budget. It is not a saving available to a practice that was not spending that much. Only the substitution logic transfers, not the amount.

Does the 96% sentiment score mean Smile Solutions provides better dentistry?

No. Sentiment measures how AI systems describe the practice, based on which sources those systems are reading. It is not a clinical quality measure and should never be read as one.

What did Core Dental achieve?

First citation on a new clinic in under 7 days from opening, against a 3–6 month SEO ramp taken from the group's own prior launches; roughly 3× first-month enquiry volume versus a comparable SEO-only launch; +38% new-patient enquiries at launch clinics; and 94% favourable sentiment.

Is the 38% enquiry lift attributable to this work alone?

No. It was measured at clinics simultaneously doing everything else a clinic opening involves. It is the change over the window, not an isolated effect.

What did Cricket For All achieve?

+500% AI referral traffic and a 6× add-to-cart rate on that traffic over two months from client analytics, orders from every Australian state from an Adelaide-only base, 300+ SKUs published, first citation under 72 hours, and no additional ad spend.

Which Cricket For All number matters most?

The geographic one. The percentage runs off a near-zero base, but going from an Adelaide-only customer base to orders in every state is a change in the shape of the addressable market.

What did Selleys achieve?

25% AI search market share in Australia after three months, 2.2× growth against the brand's own prior SEO baseline, sentiment moving from 82% to 95% favourable, more than 100,000 AI agents accessing the directory monthly, and 600+ structured product pages across nine sub-brands.

Why is the Selleys agent figure unusually solid?

Because it is server-side request data — requests actually served — rather than an inference about how a model behaves. It is the most directly countable figure in the set.

What did Realcorp achieve?

From roughly zero to 10–15 qualified enquiries per month in the first month live, with no paid media, first citation under 72 hours, presence across four capital cities, and 91% favourable sentiment.

Why does Realcorp's zero baseline matter?

Because a B2B business absent from AI shortlists generates no lost-deal record at all. The revenue that never arrives looks identical to a quiet market, so the before-figure is the part most businesses never measure.

What do all seven engagements have in common?

Each published structured, machine-readable facts the business already knew and had never made readable — specifications, credentials, coverage areas, compatibility, availability.

What do they not have in common?

Magnitude and timeline. Time to first citation ranges from under 48 hours to three months, and the spread is driven by how contested the category already was rather than by how the work was done.

Are any of these results typical?

No, and none should be read that way. There is no typical result. There is a mechanism that works under stated conditions and seven instances of it with those conditions attached.

How can I check any of this myself?

Every client named is a real business with a live website. Ask ChatGPT, Gemini, Perplexity or Claude the questions a buyer in their category would ask, and read which sources are cited. Then do the same for your own business — that gives you a baseline nobody sold you.

Where are the full case studies?

All seven are published at norg.ai/case-studies. Pricing is at norg.ai/pricing. Norg Pty Ltd, ABN 44 669 712 494, founded 14 July 2023.