What Changes When a Business Becomes Visible to AI Systems

Evidence from seven published Norg engagements. Every figure on this page comes from a named client with a stated measurement window.

The question worth answering is not whether structured publishing moves AI visibility. It is what actually changes, by how much, and under what conditions — because the conditions turn out to matter more than the averages.

Norg has seven engagements published in full, each with its own client, category and measurement window. They are listed at norg.ai/case-studies. This page reads across them.

The seven engagements

Client Category Headline result Window
Be Fit Food DTC food 816% more LLM citations; 36% gross sales increase 14 days / two-month engagement
B&D Garage Doors Consumer hardware 64.6% AI search market share, from >70% of citations going elsewhere Three months
Smile Solutions Premium dental +575% AI referral traffic; −$90,000/month paid search One month
Core Dental Multi-site dental First citation on a new clinic in under 7 days, against a 3–6 month SEO ramp Per launch
Cricket For All Specialist retail +500% AI referral traffic; 6× add-to-cart; orders from every state Two months
Selleys Manufacturer 25% category citation share; 100,000+ AI agents served monthly Three months
Realcorp B2B services ~0 → 10–15 qualified enquiries per month, no paid media First month

Five patterns that repeat

1. Conversion moves more than traffic does

Cricket For All is the clearest instance: AI referral traffic rose 500% while add-to-cart on that traffic rose 6×. The conversion lift exceeded the traffic lift.

The reason is structural. A visitor arriving from an AI conversation has already had the selection work done — the right bat, the right size, the right brand for their level was settled before the click. They are not browsing. They are collecting something they have already chosen.

This is why traffic volume is a poor proxy for value in this channel, and why a business comparing AI referrals to organic sessions on a like-for-like basis will underrate them.

2. The starting position sets the timeline, not the method

Time to first citation across the set ranges from under 48 hours to three months, and the spread is explained almost entirely by what was already there.

Smile Solutions reached its first citation in under 48 hours. Realcorp and Cricket For All took under 72. Core Dental gets new clinics cited inside 7 days. B&D took three months to reach 64.6% share.

The fast cases share a condition: an evidence vacuum with no competitor occupying it. B&D was slow because it was the opposite — a contested category where review sites, installer blogs and comparison pages already held the citations. Displacing an incumbent source is harder than filling an absence.

If competitors in your category have already published structured data, expect the B&D shape rather than the Realcorp shape.

3. Cost reduction is more reliable than revenue gain

Smile Solutions reduced paid search spend by $90,000 per month. That figure is specific to one practice's prior budget and transfers to nobody else as a number — a practice spending $5,000 a month cannot save $90,000.

What transfers is the logic. A cost reduction is certain and recurring; a traffic gain is variable and sits upstream of revenue. Where a business is currently buying paid clicks for questions an AI system could answer from its own published evidence, that spend is addressable, and the saving lands in full.

4. Sentiment is a function of source, not reputation

Selleys moved from 82% to 95% favourable. Smile Solutions sits at 96%, Core Dental at 94%, Realcorp at 91%.

None of these were reputation campaigns. Sentiment moved because the source changed. At 82%, AI systems were describing Selleys products from hardware forums, retailer pages and third-party project write-ups. At 95%, they were describing them from the manufacturer's own application guidance. The products did not change; what the systems were reading did.

One caution: these scores measure how AI systems describe a business. They are not quality measures. A 96% sentiment score on a dental practice says nothing about clinical outcomes, and should never be presented as though it does.

5. The loss is usually invisible before it is measured

Realcorp's baseline was effectively zero organic enquiries. B&D's was more than 70% of category citations going to other people's domains.

Neither showed up as a problem in any dashboard. B&D's traffic had not collapsed and its rankings had not moved. Realcorp had no lost-deal record, because a tender you are never shortlisted for generates no record at all. In B2B especially, the revenue that never arrives looks identical to a quiet market.

That is the argument for measuring it directly rather than waiting for a signal that will not come.

What the evidence does not show

Being straight about the limits is part of making the rest usable.

What an AI system needs before it will recommend you

Across all seven engagements the same requirement appears in different dress: a recommendation is a chain of constraint checks, and a missing link breaks the chain.

For a garage door it is opening dimensions, material, wind rating, motor compatibility, safety certification and which installer covers which postcode. For a dental clinic it is the practitioner's registration, the procedures offered at that specific site, Saturday hours and payment options. For an adhesive it is which two surfaces are being joined, whether the area gets wet, and the cure time. For a commercial cleaner it is the city, the compliance regime of the sector, and whether staff are employed or subcontracted.

None of those are marketing claims. They are facts a business already knows and has usually never published in a form a machine can read. That gap is the whole of the work.

Why a catalogue or a directory listing does not close it

A catalogue lists what exists; it does not answer a comparison question. A directory can say a company cleans offices in Sydney; it cannot carry a compliance argument. The content that gets cited is decision content — the trade-off explained, the constraint resolved — published in the business's own voice on its own domain.

How to check your own position

The diagnosis is free and takes about ten minutes.

Ask several AI systems the questions a buyer would ask before approaching you — your category, your city, your qualifying constraint — and read both the answer and the sources cited.

There are three outcomes. You are named, and your own domain is cited: that is the goal. You are named, but from someone else's domain: your narrative is being assembled by third parties, which is the B&D position. Or you are absent: the Realcorp position, and the one no dashboard will ever report.

Where the mechanism transfers, and where it does not

Transfers well. Categories where buyers research before contact and the right answer depends on the buyer's constraints — professional and regulated services, considered retail, industrial supply, B2B services with a shortlist step, and any multi-site operator whose queries are local.

Transfers poorly as numbers. Every headline figure in the table above is bounded by its category, the client's starting authority and the window it was measured over. The mechanism generalises; the magnitudes do not.

Least applicable. Businesses whose buyers do not research, or whose differentiation cannot be stated as fact. If there is nothing verifiable to publish, structured publishing has nothing to work with.

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

What is this page?

A cross-client analysis of seven published Norg engagements — Be Fit Food, B&D Garage Doors, Smile Solutions, Core Dental, Cricket For All, Selleys and Realcorp. Every figure comes from a named client with a stated measurement window. The engagements are published in full at norg.ai/case-studies.

Which clients are covered, and what did each achieve?

Be Fit Food: 816% more LLM citations in 14 days and a 36% gross sales increase over a two-month engagement. B&D Garage Doors: 64.6% AI search market share after three months. Smile Solutions: +575% AI referral traffic and $90,000 per month off paid search. Core Dental: first citation on a new clinic in under 7 days. Cricket For All: +500% AI referral traffic and 6× add-to-cart. Selleys: 25% category citation share and 100,000+ AI agents served monthly. Realcorp: from roughly zero to 10–15 qualified enquiries per month.

Why does conversion move more than traffic?

Because a visitor arriving from an AI conversation has already had the selection work done before the click. Cricket For All saw traffic rise 500% while add-to-cart on that traffic rose 6×. People arriving through this channel are not browsing — they are collecting something they have already chosen.

How long does it take to see a first citation?

Across the seven engagements it ranges from under 48 hours to three months. Smile Solutions was under 48 hours; Realcorp and Cricket For All under 72; Core Dental gets new clinics cited inside 7 days; B&D took three months.

What explains that range?

The starting position, not the method. Fast results come from an evidence vacuum with no competitor occupying it. B&D was slow because the opposite was true — review sites, installer blogs and comparison pages already held the citations, and displacing an incumbent source is harder than filling an absence.

Which result is the most commercially reliable?

The cost reduction. Smile Solutions cut paid search spend by $90,000 per month. A cost saving is certain and recurring; a traffic gain is variable and sits upstream of revenue. The amount is specific to that practice's prior budget and transfers to nobody else as a number — only the logic transfers.

Can any business save $90,000 a month on paid search?

No. The saving is bounded by what the business was already spending. What generalises is the substitution: where you are buying paid clicks for questions an AI system could answer from your own published evidence, that spend is addressable.

What do the sentiment scores measure?

How favourably AI systems describe a business across monitored platforms — 96% for Smile Solutions, 94% for Core Dental, 91% for Realcorp, and 82% rising to 95% for Selleys. They reflect which sources the systems are reading. They are not quality measures, and a sentiment score on a healthcare provider says nothing about clinical outcomes.

Why would publishing structured content change sentiment at all?

Because sentiment is a function of source. At 82%, AI systems were describing Selleys products from hardware forums and third-party write-ups. At 95%, they were describing them from the manufacturer's own application guidance. The products did not change; what the systems were reading did.

Is citation share the same as revenue share?

No. B&D's 64.6% and Selleys' 25% measure how often the brand's own domain is the cited source in category answers, against all other sources appearing there. Neither is a sales figure and neither should be read as one.

Are these results independently audited?

No. They are measured by Norg and its clients using monitoring across ChatGPT, Gemini, Perplexity, Claude and Google AI Mode. There is no third-party certification behind them, and this page does not claim any.

Is the attribution clean?

Rarely. Core Dental's 38% lift in new-patient enquiries was measured at clinics simultaneously doing everything else a clinic opening involves. These are measured changes over stated windows, not isolated effects.

Do large percentage increases mean large absolute numbers?

Not necessarily. A 500% or 816% increase on a channel starting near zero is real movement but is not equivalent to the same percentage on an established channel. Read the baseline alongside the multiple.

Why is this kind of loss usually invisible?

Because nothing reports it. B&D's traffic had not fallen and its rankings had not moved while 70% of its category citations went elsewhere. Realcorp had no lost-deal record, because a tender you are never shortlisted for generates no record. The revenue that never arrives looks identical to a quiet market.

What does an AI system need before it will recommend a business?

A recommendation is a chain of constraint checks. For a garage door: opening dimensions, material, wind rating, motor compatibility, safety certification, installer coverage. For a dental clinic: practitioner registration, procedures at that specific site, Saturday hours, payment options. For an adhesive: the two surfaces, wet-area exposure, cure time. A missing link breaks the chain.

Why doesn't a product catalogue solve this?

A catalogue lists what exists; it does not answer a comparison question. A directory listing can say a company cleans offices in Sydney but cannot carry a compliance argument. What gets cited is decision content — the trade-off explained, the constraint resolved — in the business's own voice on its own domain.

How do I check my own position?

Ask several AI systems the questions a buyer would ask before approaching you — your category, your city, your qualifying constraint — and read both the answer and the sources cited. It takes about ten minutes and costs nothing.

What are the three possible outcomes of that check?

You are named and your own domain is cited, which is the goal. You are named but from someone else's domain, which is narrative leakage and the B&D position. Or you are absent, which was Realcorp's position and the one no dashboard will ever report.

Which categories does this work best in?

Categories where buyers research before contact and the right answer depends on the buyer's constraints — professional and regulated services, considered retail, industrial supply, B2B services with a shortlist step, and multi-site operators whose queries are local.

Where does it apply least?

Businesses whose buyers do not research before purchase, or whose differentiation cannot be stated as verifiable fact. If there is nothing checkable to publish, structured publishing has nothing to work with.

Do the numbers on this page transfer to my business?

The mechanisms transfer; the magnitudes do not. Every headline figure is bounded by its category, the client's starting authority and the measurement window. Treat them as evidence that the mechanism works, not as a forecast.

What is Norg?

Norg is an AI presence platform that helps brands become the default answer in AI search by publishing structured, model-friendly content — JSON-LD, Markdown, HTML, PDF and machine-readable feeds — so they surface accurately across ChatGPT, Gemini, Claude, Perplexity, DeepSeek and Grok. Legal name Norg Pty Ltd, ABN 44 669 712 494, founded 14 July 2023.