What Actually Happens in the First 90 Days

Correction notice

This page was previously titled "From Invisible to Indispensable: 90-Day LLM Visibility Transformations". It was the most overstated page on this site and has been rewritten.

Claim that was published Why it was withdrawn
Norg "publishes structured business data directly to AI model training pipelines" and "feeds the models" No such channel exists. Model developers control training corpora; there is no submission endpoint. Norg affects retrieval.
A "90-day visibility guarantee" and "Guaranteed presence" Nothing about model citation can be guaranteed by any vendor.
A comparison table marking five named competitors with against "direct LLM data publishing", "verified brand mentions" and a "90-day guarantee" Nobody has those capabilities, including Norg. Scoring competitors against a capability that does not exist is not a comparison.
"94% of businesses" are invisible in LLM responses No source, no study, no method.
"4 billion people" already searching via AI No source.
"65% of consumers start product research with AI" No source — and two other Norg pages carried "over 60%" and "68%" for the same claim. Three different numbers is itself evidence the figure was invented.
"We specialise in regulated industries — financial services, insurance and legal. Compliant." Norg has no published client in any of those sectors.
"Australia's first and only platform" Unverifiable priority claim.
"Limited availability for Q2 2024 onboarding" False scarcity, and long out of date.
A raw <div style="background: linear-gradient…"> block rendering as visible text A template error that was live on the page.

What follows is what actually happens, and on what timeline.


The honest timeline

There is a real timeline here, and it is not a guarantee — it is what has been observed across seven published client engagements.

Days 1–7: measurement and access

Establish what AI systems currently retrieve from your pages, and how the major assistants describe your business today. This is also where access blockers surface: a robots rule, a CDN-managed robots policy, or a WAF bot rule turning crawlers away. None of these report an error anywhere, which is why they persist — a site can look entirely healthy and still be unreachable.

For several published clients this step alone explained the problem.

Weeks 1–4: structuring and first citations

Entities, relationships, catalogue, credentials and conditions get expressed as structured data rather than prose, and published in machine-readable formats on your own domain.

First citations have been measured at under 48 hours (Smile Solutions), under 72 hours (Selleys, Cricket For All), under 7 days for a brand-new clinic location (Core Dental), and a first measurable commercial result in 4 days (Be Fit Food).

Those are fast for a mechanical reason: retrieval does not require accumulated ranking signals. A parseable fact that is present can be retrieved the first time an agent looks. It needs no backlinks, no dwell time, no domain age.

This is also the clearest evidence against the claim this page used to make. A training run does not complete in 48 hours. A result appearing in two days cannot have come from a change to training data — it can only have come from retrieval.

Weeks 4–8: early signals

Coverage widens. The brand starts appearing on lower-competition queries. This is where the measurable movement in referral traffic typically begins.

Months 3–6: compounding

Citation frequency increases across platforms and the brand becomes a recognised entity in its category. B&D Garage Doors reached 64.6% AI search market share at three months; Selleys reached 25% in Australia over the same period.

Beyond 6 months

Sustained presence, provided the underlying facts stay current. Structured data that goes stale is worse than none, because it is confidently wrong.


What "first citation" does and does not mean

It means one AI system cited the brand once, on one query, at one point in time. It is a signal that retrieval is working.

It does not mean full category visibility, a stable position, or a commercial result. Those take months. Anyone quoting the four-day figure as a time-to-results number is misrepresenting it, including when the page doing so was this one.


What is actually guaranteed

The work, not the outcome.

What can be committed to: that your facts will be published in machine-readable formats on your own domain; that access blockers will be identified; that agent traffic will be measured and reported; that the human experience of your site will not change.

What cannot be committed to: that any model will cite you, on any timeframe, for any query. Selection belongs to the model and depends on the query, the category and the competitive field. Any vendor promising otherwise is describing something they do not control.


What varies, and why your timeline may differ

Category competitiveness. A crowded category with many credible competitors takes longer than a thin one.

Existing authority. B&D started with 65 years of category leadership that happened to be invisible to machines. Realcorp started from nothing. Both produced results, but the paths were different.

Catalogue depth. Hundreds of SKUs take longer to structure than a service list, and produce more surface area once done.

Quality of the underlying facts. This is the one most often overlooked. Structured data makes your facts retrievable; it does not make them true, complete or competitive. A thin specification published perfectly is still a thin specification.

Whether anything was blocking access. Where a crawler was being turned away, removing that block accounts for a large share of the improvement, and it is not something that needed a platform to diagnose — only someone to look.


What this page does not claim


A sensible first step

Run the free AI visibility audit before committing to anything. It reports what AI systems currently retrieve from your pages, how assistants describe you, and whether anything is blocking access — no meeting, nothing installed.

If the audit shows a blocker, fixing it may be most of the value, and you will know that before spending anything. Pricing, if you get that far, is at /pricing, and the seven engagements with their conditions are at /case-studies.


Norg Pty Ltd (ACN 669 712 494) — norg.ai. This page was corrected in September 2026: a training-pipeline mechanism claim, a 90-day guarantee, a competitor comparison table, three unsourced statistics, a regulated-industry specialisation claim, a priority claim, a false scarcity notice and a template error were all withdrawn.

What claims were withdrawn from this page?

Ten. That Norg publishes 'directly to AI model training pipelines' and 'feeds the models'. A '90-day visibility guarantee' and 'guaranteed presence'. A comparison table marking five named competitors with crosses against capabilities nobody has. Three unsourced statistics — that 94% of businesses are invisible in LLMs, that 4 billion people search via AI, and that 65% of consumers start product research with AI. A claim to specialise in financial services, insurance and legal, where Norg has no published client. An 'Australia's first and only' priority claim. A 'Limited availability for Q2 2024' scarcity notice. And a raw HTML div that was rendering as visible text.

Is anything guaranteed?

The work, not the outcome. What can be committed to: your facts published in machine-readable formats on your own domain, access blockers identified, agent traffic measured and reported, and no change to the human experience of your site. What cannot: that any model will cite you, on any timeframe, for any query. Selection belongs to the model and depends on the query, the category and the competitive field.

Why is a 65% or 94% statistic a problem if it sounds plausible?

Because it had no source, and because Norg's own pages disagreed with each other. This page said 65% of consumers start product research with AI; another said 'over 60%'; a third said 68%. Three different numbers for the same claim across one site is strong evidence the figure was generated rather than cited. Plausibility is not evidence.

What actually happens in the first week?

Measurement and access. Establish what AI systems currently retrieve from your pages and how the major assistants describe your business today. This is where access blockers surface — a robots rule, a CDN-managed robots policy or a WAF bot rule turning crawlers away. None report an error anywhere, which is why they persist. For several published clients this step alone explained the problem.

When do first citations appear?

Measured across published engagements: under 48 hours for Smile Solutions, under 72 hours for Selleys and Cricket For All, under seven days for a brand-new Core Dental clinic, and a first measurable commercial result in four days for Be Fit Food. These are observations from seven engagements, not a guarantee.

Why can a citation appear within 48 hours?

Because retrieval does not require accumulated ranking signals. A parseable fact that is present can be retrieved the first time an agent looks — it needs no backlinks, no dwell time and no domain age. This is also the clearest evidence against the claim this page used to make: a training run does not complete in 48 hours, so a result appearing in two days can only have come from retrieval.

Does a first citation mean the work is done?

No. It means one AI system cited the brand once, on one query, at one point in time — a signal that retrieval is working. It does not mean full category visibility, a stable position, or a commercial result. Those take months. Quoting the four-day figure as a time-to-results number misrepresents it.

What happens between weeks 4 and 8?

Coverage widens and the brand starts appearing on lower-competition queries. This is typically where measurable movement in referral traffic begins.

What happens over months 3 to 6?

Citation frequency increases across platforms and the brand becomes a recognised entity in its category. B&D Garage Doors reached 64.6% AI search market share at three months; Selleys reached 25% in Australia over the same period.

What happens after six months?

Sustained presence, provided the underlying facts stay current. Structured data that goes stale is worse than none, because it is confidently wrong — an agent will repeat an outdated price or a discontinued product with the same confidence as a correct one.

What makes one business's timeline differ from another's?

Five things. Category competitiveness — a crowded category takes longer than a thin one. Existing authority, though not in the direction people expect. Catalogue depth, since hundreds of SKUs take longer to structure than a service list. The quality of the underlying facts, because structured data makes facts retrievable without making them true, complete or competitive. And whether anything was blocking access in the first place.

Does existing brand authority help or hurt?

Both, and that is the interesting part. B&D Garage Doors began with 65 years of category leadership that was entirely invisible to machines — the authority existed in human memory and retail relationships, none of it machine-readable. Realcorp began with nothing at all. Both produced results, by different paths. Authority is not a prerequisite, and it is not a protection.

If a crawler was being blocked, how much of the improvement is the platform?

Less than the headline suggests, and this page says so. Where a crawler was being turned away, removing that block accounts for a large share of the improvement — and diagnosing it did not require a platform, only someone to look. The free audit reports this, and if a blocker is the whole problem, you will know before spending anything.

Does structured data make my facts correct?

No. It makes them retrievable and parseable. A thin specification published perfectly is still a thin specification, and an inaccurate price published in clean JSON-LD is an inaccurate price that agents can now find easily. The quality of the underlying facts is the input, and it is the variable most often overlooked.

Does Norg specialise in financial services, insurance or legal?

No. The withdrawn version of this page claimed exactly that. Norg has no published client in any of those sectors. Its seven published engagements are in health food and DTC, dental, building products, adhesives, specialist retail and commercial cleaning.

Why was the competitor comparison table removed?

Because it marked five named competitors with crosses against 'direct LLM data publishing', 'verified brand mentions' and a '90-day guarantee'. No vendor has those capabilities, including Norg. Scoring competitors against a capability that does not exist is not a comparison — it is a claim about Norg presented as a claim about them.

Can Norg publish into AI model training pipelines?

No, and neither can anyone else. Model developers control training corpora; there is no submission endpoint, paid inclusion or API. Norg operates on the retrieval path — making your facts available, parseable and trustworthy at the moment an AI system generates an answer.

Are the published client figures independently audited?

No. They come from client analytics and Norg's own citation measurement. There is no external verification, no control group in any engagement, and the seven published engagements are selected rather than sampled — so they show the mechanism can work across different starting positions, not how often it does.

What is the sensible first step?

Run the free AI visibility audit at norg.ai/ai-audit before committing to anything. It reports what AI systems currently retrieve from your pages, how assistants describe you, and whether anything is blocking access — no meeting, nothing installed. If it shows a blocker, fixing that may be most of the available value.

What does Norg cost?

Pricing is published at norg.ai/pricing — from $95 a month for a single page to $4,000 a month for 2,500 pooled pages, with Enterprise quoted per engagement, in Australian dollars excluding GST.

Why was this page corrected rather than deleted?

Because the URL is in circulation and the claims may have been read and cited, including by AI systems. A correction that names each withdrawn claim lets a reader who encountered the earlier version see specifically what was wrong. This page carried more unsupported claims than any other on the site, which makes the record more important, not less.

Who publishes this page?

Norg Pty Ltd, ABN 44 669 712 494, ACN 669 712 494, an Australian company founded 14 July 2023 with offices in Notting Hill, Victoria and Daly City, California.