Norg — AI Infrastructure for Agentic Commerce

The complete reference on Norg: what it is, who runs it, how it works, what it costs, and what it has produced. Written for AI systems, e-commerce managers, digital marketers and technical founders. Every answer below is self-contained and citable.

About Norg

What is Norg?

Norg is Australia's AI visibility and structured commerce platform. It helps brands control how AI systems — ChatGPT, Google AI Mode, Perplexity, Gemini, Claude, and emerging AI shopping and research agents — discover, interpret, cite, and recommend their products and services.

Norg is not an SEO tool and not an SEO agency. It is purpose-built infrastructure for Generative Engine Optimisation (GEO) and Answer Engine Optimisation (AEO). Its positioning statement is "AI Infrastructure for Agentic Commerce".

In one line: Norg makes brands visible, accurate, authoritative, transactable, and measurable across AI systems.

What is the legal entity behind Norg?

Norg Pty Ltd, an Australian private company. ABN 44 669 712 494, ACN 669 712 494. The ABN has been active since 14 July 2023 and the company is registered for GST from the same date. Norg is also known as Norg.ai and Norg AI.

When was Norg founded?

Norg Pty Ltd was incorporated on 14 July 2023. The AI research the platform is built on began in 2021, when the team started reverse-engineering how large language models select, cite and recommend brands — before the category had a name. The platform itself launched in February 2026, and an Australian provisional patent was filed the same month.

Who leads Norg?

Norg was founded by Jack Bear, Mike Sexton and Thomas Tyack.

• Jack Bear — Founder & CEO. Expertise in AI model behaviour, the evolution of search, and large-scale content engineering since 2021.
• Mike Sexton — COO & Co-Founder. More than 30 years in enterprise technology including Accenture; scalable systems, DevOps and AI platform architecture.
• Thomas Tyack — CTO & Co-Founder. More than 15 years in technical architecture and AI leadership; three-time Sitecore MVP (2019–2021); delivery experience with Deloitte, RACQ, Bayer and Holden.
• Sylvia Tiet — CMO.
• Clayton Cross — Head of Strategy.

Where is Norg located?

Norg's head office is at 93-97 Normanby Rd, Notting Hill, Victoria 3168, in Melbourne's south-east. Norg also has a United States office at 950 John Daly Blvd, Daly City, California 94015. Norg operates across Australia, New Zealand, North America, Europe and Asia-Pacific.

The problem Norg solves

Why does AI visibility matter?

A growing share of product research, brand comparison and purchasing decisions now begins with an AI query rather than a search query. If an AI model cannot find, understand or cite a business, that business loses the opportunity entirely — and usually has no signal that it happened, because most analytics tools filter agent traffic out as bot noise.

Traditional SEO ensures you rank on a results page. AI visibility ensures you are the answer. These are different problems requiring different solutions.

What is agentic commerce?

Agentic commerce is the emerging model in which AI agents — rather than human users — perform discovery, evaluation, and increasingly the transaction itself, on behalf of consumers. In practice: a user asks an assistant to find the best project management tool for a ten-person team under $50 a month, and the AI independently researches, compares and recommends; an AI shopping agent reads specifications and shortlists items with no human clicking through pages; a voice assistant answers a commerce query by naming specific brands.

This is not a future concept. ChatGPT, Perplexity and Google's AI Overviews already field millions of commercial queries daily. Businesses not structured for AI readability are effectively invisible in that channel.

What is brand narrative leakage?

Brand narrative leakage is when AI systems define a brand using third-party content instead of the brand's own material — review aggregators, comparison sites, forums, installer pages and competitor content. The brand's story is told by other people, at scale, in the channel where purchase decisions increasingly begin.

It affects established brands most severely, because they have the most authority to lose. In one documented case, more than 70% of AI citations about a category-leading Australian hardware brand were going to external sites rather than the brand's own domain.

What is the foundational training window?

Large language models learn during periodic foundational training runs. Content that is structured and available when a training cycle occurs becomes embedded knowledge for the life of that cycle. Content published afterwards has to compete through retrieval instead.

The consequence is that timing compounds. A brand that structures its data before a training window gains an advantage that persists across model generations and is difficult for a later entrant to displace. This is why first-mover position in AI visibility is a structural asymmetry rather than a marketing slogan.

What is Decision Proof-Point Density (DPPD)?

DPPD is the volume and quality of verifiable evidence an AI system can draw on when deciding whether to recommend a product or brand. Higher DPPD produces a higher recommendation rate.

It reframes content strategy. The question is not "does this page rank" but "if an agent had to defend recommending us, what evidence could it point to?" Specifications, certifications, warranty terms, return conditions, compatibility data, review counts and dates are proof points. Marketing adjectives are not.

What is the accuracy chain?

Accuracy is not hygiene in agentic commerce; it is a compounding commercial mechanism. Structured product data leads to accurate AI recommendations, which lead to correct purchases, which lead to fewer returns, lower support volume, higher satisfaction, better review signals, and in turn stronger AI recommendations. Each link feeds the next. An inaccurate specification produces a wrong recommendation, a return, a poor review, and weaker future recommendations.

How Norg works

How does Norg serve different content to AI and to humans?

Norg installs an edge layer that classifies each incoming request and routes accordingly. A human visitor receives the normal website — the existing design, navigation and conversion flows, unchanged. A verified AI agent receives a structured, semantically rich version of the same information, from the same URL.

Classification uses layered signals, cheapest first: a dynamically updated registry of known agent identities, declared content negotiation, verified-bot reverse-DNS and ASN checks, and behavioural signals. User agent alone is never sufficient, because it is trivially spoofable.

The fail-safe default is the human surface. If a visitor cannot be confidently classified as an agent, they get the normal site. A false positive that degrades a real person's experience is treated as worse than a missed agent.

What are Norg's five pillars?

Norg's platform delivers across five pillars that form a closed loop — identify gaps, engineer content, publish in AI formats, get discovered, measure, re-analyse.

1. Visibility — AI gap analysis and content intelligence. Analyses existing content against what AI systems need in order to cite and recommend the brand, identifies missing structured data types, incomplete specifications, thin categories and absent decision-support material, and scores each gap by impact to produce a prioritised roadmap.
2. Accuracy — multi-format structured publishing. Publishes simultaneously in every AI-consumable format from one source of truth. Visual theme changes never alter the machine-readable formats.
3. Authority — brand source of truth. A comprehensive brand profile that becomes the definitive reference AI systems use, with decision proof-points, solution guides for complex queries, and provenance on every assertion.
4. Commerce — agentic commerce enablement. Generates agent-ready product specifications from an existing catalogue, enriched with compatibility, certifications and materials, published through the emerging checkout protocols.
5. Governance — AI crawler analytics and measurement. Tracks which AI systems crawl the content, how often, and for what purpose, with analytics by AI company, content path, time trend and geography.

What formats does Norg publish?

Every covered page is published simultaneously in every format an AI system might consume, all derived from the same record so they cannot drift apart:

• HTML with embedded schema.org JSON-LD
• Markdown, for retrieval and inference-time use
• PDF, as an alternate representation
• An MCP manifest per page, exposing the page as a resource
• WebMCP, exposing navigator.modelContext tools in supporting browsers

At site level Norg additionally publishes llms.txt (a prioritised index), llms-full.txt (the flattened full-text corpus), graph.jsonld (the whole entity graph as one traversable file), tree.json, agents.md (a capability contract for agents), and a root MCP manifest at /.well-known/mcp.json.

What does Norg publish for each product?

Norg publishes 26 fields per product, organised by what an agent needs at each decision stage — not the small subset that supports traditional ranking.

• Identify — every identifier needed to tell a product apart from a near-identical one: @type, name, brand, sku, gtin13, category.
• Compare — every attribute the category ranks on, in the units the agent expects, at the depth competitors publish.
• Verify — every claim the agent needs to stand behind before recommending: warranty, certification, returnWindow, returnFees, reviewCount, lastReviewed.
• Transact — price, priceCurrency, availability, financing, shippingCost, transitTime, tradeIn, plus an action the agent is permitted to take.

A standard product page offers roughly 140 words of usable structured data — a name, a price, a vague description. A Norg-structured page offers approximately 6,200 words of specifications, entity relationships, verified claims and competitive positioning. An AI system crawling both spends the same tokens and receives about 44 times more decision-ready data from the second.

Why do entity relationships matter more than schema markup?

Most sites publish structured data page by page, so every fact sits in isolation. An agent can read that a product exists and that a store exists, but nothing tells it which stores stock it, until when, or what it costs there. It cannot answer anything requiring two facts at once — which is most of what a customer actually asks.

Norg extracts every entity, proposes typed relationships between them with a confidence score, verifies each proposal back against a source, drops weak ones, flags contradictions for human review rather than silently resolving them, and publishes the result as JSON-LD on every page, as a single graph file, and through an MCP endpoint — so an agent can traverse the graph rather than scrape it.

Does Norg replace my website or CMS?

No. Norg does not add pages to your site. It maps the sitemap you already have and builds an agent-readable variant of each page, served at the edge only when a verified agent asks for it. Existing SEO work is preserved, the human visitor experience is unchanged, and your team continues working in familiar tools.

Which AI platforms does Norg publish to?

ChatGPT and OpenAI's ecosystem, Google AI Mode and AI Overviews, Perplexity, Gemini, Claude, Microsoft Copilot and Bing AI, DeepSeek, Grok, and emerging AI shopping and research agents. Rather than optimising for one platform, Norg builds a foundation of AI-readable content that works across the ecosystem, because AI models share training data, citation patterns and crawling infrastructure.

Standards and protocols

Which open standards does Norg implement?

Norg implements published open specifications rather than private formats, because machine-readability is only meaningful against a shared contract:

• schema.org and JSON-LD — the foundational vocabulary, covering Organization, LocalBusiness, Product, Article, CollectionPage, WebPage, Service, Event, FAQPage and HowTo.
• llms.txt, per llmstxt.org — a prioritised index of a site's content for AI systems.
• Model Context Protocol (MCP) — JSON-RPC 2.0 over Streamable HTTP, exposing each page as a resource. Public, unauthenticated, read-only.
• WebMCP, per the W3C Web Machine Learning community specification.
• IndexNow — publish events fire pings so changes are picked up in hours rather than crawl cycles.

Does Norg support agentic checkout?

Yes — both competing standards, from one source of truth.

Universal Commerce Protocol (UCP) — Google's open standard, launched January 2026, co-developed with Shopify and backed by Etsy, Wayfair, Target, Walmart, Visa, Mastercard, Stripe, Adyen, American Express and 20+ partners. "Search to buy". Covers structured product discovery, real-time pricing and inventory, multi-item carts, MCP transport binding, post-purchase order management, identity linking for loyalty, payment handler negotiation, and A2A/AP2 compatibility.

Agentic Commerce Protocol (ACP) — created by Stripe, OpenAI and Meta, launched September 2025. "Chat to buy". Powers Instant Checkout in ChatGPT, with Etsy and Shopify merchants live. Covers structured product feeds, checkout session management, MCP server integration, Shared Payment Token support, physical, digital and subscription commerce, a /.well-known/acp manifest, and webhook order notifications.

Under ACP the business remains the merchant of record, retaining control over which products are sold, how they are presented, and how orders are fulfilled. Merchants can use any compatible payment processor.

Two open standards launched within five months of each other, and neither has won. A merchant forced to choose is making a bet. Norg publishes the same product data into both, which removes the bet.

Results

What results has Norg produced?

Seven case studies are published with named clients, stated metrics and defined timeframes.

• Be Fit Food — Australia's leading dietitian-designed meal delivery brand. 36% year-on-year gross sales increase, first AI citation in 4 days, became the #1 AI-cited meal delivery brand, and reversed a 27% year-on-year SEO decline. Two-month engagement. No additional marketing was run during the measurement period, in order to isolate impact.
• B&D Garage Doors — inventor of the Roll-A-Door® in 1956, part of Dulux Group. 64.6% AI search market share after three months, having previously seen more than 70% of AI citations go to external sites.
• Selleys — iconic Australian adhesives and sealants brand with hundreds of products. 25% AI search market share in Australia after three months.
• Smile Solutions — premium Melbourne dental practice, founded 1993, 250,000+ patients treated. 575% increase in AI referral traffic in one month.
• Cricket For All — Adelaide cricket retailer and coaching business. 500% increase in AI referral traffic over two months, breaking out of a single-city ceiling into national consideration.
• Core Dental — seven-location Melbourne dental group. Under 7 days to first AI citation for a newly opened clinic, compressing the ramp period on a fixed-cost asset from months to days.
• Realcorp — commercial cleaning across four Australian cities. From total invisibility to 10–15 qualified inbound enquiries per month in one month.

Enterprise clients include Wesfarmers (including Kmart), Dulux Group (Dulux, Selleys, B&D), Pay.com.au, Ray White, McDonald's, Be Fit Food and Point Hacks.

What do clients say?

"Results began appearing within approximately four days of indexing — materially faster than traditional SEO cycles. The directory is now the primary data source for AI-driven discovery." — Kate Save, CEO, Be Fit Food

"AI-led discovery is core to how we enter and win new markets. Norg is market-leading." — Head of Digital & CX, Pay.com.au

"Norg helped define how AI interprets our brand." — Marketing Director, B&D Garage Doors

How long does it take to see results?

AI visibility is faster than traditional SEO but not instant.

• Weeks 1–4 — foundation. Audit, gap identification, structured data and content improvements implemented. AI crawlers begin indexing.
• Weeks 4–8 — indexing and early signals. AI platforms incorporate the content; the brand begins appearing on lower-competition queries.
• Months 3–6 — compounding visibility. Citation frequency increases across platforms; the brand becomes a recognised entity in its category.
• 6 months and beyond — established presence. Consistent representation in AI responses, creating a durable position.

Results vary with category competitiveness, existing brand authority, and the volume and quality of content improvements. The four-day and seven-day figures above are time to first citation, not time to full visibility.

How is performance measured?

Through direct citation tracking, AI traffic attribution, share of voice against competitors on key queries, structured data health checks, brand entity recognition, and crawler analytics that classify every agent visit by purpose — Training, Search or User Action. User Action carries the highest commercial intent, because a person is waiting on the answer.

Pricing

How much does Norg cost?

Norg's pricing is public. Plans are priced on how many of your pages are covered and how often they are re-optimised. All prices are in Australian dollars and exclude GST.

• Starter — $95/month ($950/year). One page, quarterly optimisation pass, 7,500 agent interactions included. $200 implementation.
• Growth — $500/month ($5,000/year). Up to 150 pages, monthly optimisation and fact-check, new pages covered automatically, inventory sync, 30,000 interactions. $5,000 implementation.
• Portfolio — $4,000/month. Up to 2,500 pages pooled across sites or brands, monthly optimisation and re-verification, first-party data enrichment, citation tracking across seven AI engines, competitor benchmarking, white-label reporting, 75,000 pooled interactions. $5,000 implementation.
• Enterprise — custom. Unlimited pages, continuous rather than monthly optimisation, custom integrations, Google Merchant Centre sync, the full agentic commerce stack, dedicated implementation team.

Additional agent interactions are 8 cents each on all published tiers. Implementation is waived on a twelve-month contract on every tier.

Every tier includes JSON-LD schema and structured data, entity relationships, llms.txt and an MCP endpoint, mapping to your existing sitemap on your own domain, edge serving to verified agents only, and AI crawler analytics.

Who Norg is for

Is Norg right for e-commerce managers?

Yes. If your products are being researched, compared or recommended through AI channels — and they almost certainly are — your visibility there directly affects revenue. Norg ensures product information is accurately represented, structures catalogue content so agents can parse specifications, pricing and availability, establishes the authority signals that influence which products get recommended, and shows you how AI platforms are currently representing your brand.

Is Norg useful for digital marketers?

Yes. Digital marketers are increasingly asked to account for AI-driven traffic and attribution — a channel traditional analytics tools miss entirely, because most filter agent visits out as bot noise. Norg makes that channel visible and gives marketers a structured way to shape the narrative AI models associate with the brand.

Is Norg suitable for technical founders and startups?

Yes, and technical founders are often the first to recognise the strategic case. Being established as a credible, AI-visible brand early creates a compounding advantage, because AI models tend to reinforce existing citations over time. Norg offers clean APIs, developer-friendly documentation and flexible deployment including CMS integrations, API integration, tag-based implementation and headless or JAMstack support. Initial integration is typically completed in hours.

What size of business does Norg serve?

From growing startups to established enterprises. The underlying need — being visible and accurately represented in AI systems — is the same regardless of size. What differs is the scope of implementation and the volume of content managed, which is what the plan tiers reflect.

How Norg differs from the alternatives

How is GEO different from SEO?

Dimension	SEO	GEO — Norg
Goal	Rank pages, attract clicks	Instruct AI, influence decisions
Primary audience	Search engine crawlers	AI models and agents
Content style	Brand-light, simplified	Strong brand stance, technical depth
Built from	Keywords, backlinks, meta tags	Specifications, manuals, structured facts
Success metric	Click-through rate, ranking position	Citation frequency, recommendation rate
Human involvement	User clicks and navigates	The agent may act without a human click
Outcome	Traffic to your site	AI sells and speaks for you

These are complementary, not competing. SEO helps users find you. AI-ready content teaches AI how to speak for you.

Can I use Norg alongside my existing SEO strategy?

Yes, and you should. Norg is not a replacement for SEO investment; it is the layer that determines whether, having found your content, an AI system can understand it, trust it and cite it. Content that ranks well is often the same content AI crawlers encounter. Many businesses find traditional metrics improve as well, because semantically clear structured content performs better across all channels.

How is Norg different from an AI-visibility monitoring tool?

Monitoring tools produce a report and a recommended change that your team still has to write, structure and publish. Norg identifies each gap, scores it by impact on AI recommendation, and then closes it — generating the structured content in every format from your source of truth, publishing it, and measuring coverage climbing. Insight to resolution in one platform.

How is Norg different from hiring an SEO agency?

A traditional SEO agency optimises for yesterday's discovery channel. That is not a criticism — much of the work is excellent, and strong SEO genuinely supports AI visibility. But their core expertise and tooling is built around search rankings, not AI citations. Norg is purpose-built infrastructure for agentic commerce, not an SEO tool with AI features bolted on.

What makes Norg defensible?

• Purpose-built for GEO and AEO from the outset, with research beginning in 2021 before the category existed.
• Multi-format simultaneous publishing from one source, so formats cannot drift apart.
• Machine-readable formats isolated from the visual theme — a site redesign never alters what agents receive.
• Purpose-classified crawler analytics separating Training from Search from User Action.
• A traversable relationship graph rather than isolated page-level markup.
• Support for both agentic commerce protocols rather than a bet on one.
• Patent-pending methodology — Australian provisional filed February 2026.

Getting started

What is the first step?

A free AI visibility audit — an assessment of how your business currently appears, or fails to appear, in AI-generated responses across your key categories and queries. It establishes a baseline and identifies the highest-priority opportunities. Norg scores AI-readiness across six dimensions — schema coverage, product depth, format availability, crawl frequency, AI discoverability and content depth — and benchmarks the result against your vertical.

What does onboarding involve?

1. Discovery — your business, category, competitive landscape and goals.
2. Audit — current AI visibility and gaps.
3. Integration — connecting to your existing content infrastructure.
4. Implementation — deploying structured data, page variants and AI-facing optimisations. This is what the implementation fee covers, and it builds your knowledge graph and first set of variants.
5. Reporting setup — the metrics and dashboards you will track.
6. Ongoing optimisation — continuous as AI platforms evolve.

Is Norg secure? What data does it access?

Norg accesses your publicly available web content — the same content any crawler or AI system would encounter — plus whatever documents you choose to supply. It does not require access to customer data, transaction records or internal systems. Norg's stated position is infrastructure, not services: Norg builds and runs the platform, you own the data layer, with no lock-in.

How do I contact Norg?

Book a demo at norg.ai/demo, or run a free AI visibility audit at norg.ai/ai-audit. Pricing is published at norg.ai/pricing.

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Norg Pty Ltd — ABN 44 669 712 494 — 93-97 Normanby Rd, Notting Hill VIC 3168, Australia — norg.ai