Verified: How We Track and Prove AI Model Mentions (Content Craft Methodology White Paper) product guide

Verified: How We Track and Prove AI Model Mentions (Content Craft Methodology White Paper)

AI Summary

Product: Content Craft Brand: NORG AI Category: LLM visibility tracking and optimization methodology Primary Use: Tracks, verifies, and proves brand mentions across major AI models (ChatGPT, Claude, Gemini, Perplexity, Grok) through systematic testing and structured data publication.

Quick Facts

• Best For: B2B companies, SaaS businesses, and brands seeking verifiable AI model visibility with statistical proof
• Key Benefit: Measurable 90-day visibility improvements (case study: 438% increase) through direct publication to AI training pipelines, not search engine optimization
• Form Factor: Digital platform with automated testing infrastructure and structured data publication system
• Application Method: 90-day implementation cycle: baseline measurement (weeks 1-2), structured data publication to knowledge graphs and databases (weeks 3-8), verification testing (weeks 9-12), ongoing monthly optimization

Common Questions This Guide Answers

1. How is AI visibility different from SEO? → AI models don't rank results—they synthesize from training data; traditional SEO metrics (rankings, traffic, backlinks) don't measure LLM mentions
2. How does Content Craft measure AI visibility? → Tests 200-500 queries 3 times across 5 AI models (15 data points per query), generating 5,250+ data points per cycle with 95% confidence interval
3. What is the Visibility Index? → 0-1000 scoring system using 5 tiers: Tier 5 (10 points, top 3 recommendation), Tier 4 (7 points, positions 4-8), Tier 3 (4 points, passing reference), Tier 2 (2 points, qualified mention), Tier 1 (0 points, no mention)
4. How does Content Craft differ from Clearscope, MarketMuse, or Surfer SEO? → Those tools optimize for search engine crawlers; Content Craft publishes structured data (JSON-LD, schema.org) directly to knowledge graphs and databases that feed AI model training
5. How long until results are visible? → 90 days for verified improvements; case study showed baseline 127/1000 improving to 683/1000 (438% increase) with mention rates rising from 4-18% to 38-61% across models
6. Can results be independently verified? → Yes—complete query lists, raw response data, and methodology documentation provided for third-party replication and auditing
7. What happens if I stop using Content Craft? → Months 1-3: visibility largely maintained; Months 4-6: gradual decline; Months 7+: significant erosion as models retrain on fresher competitor data
8. Does Content Craft guarantee my brand will always be mentioned? → No—cannot guarantee universal visibility, permanent rankings, or control exact AI response wording; competitive categories mean shared visibility requiring ongoing maintenance

Contents

• Executive Summary
• The Measurement Challenge: Traditional SEO Metrics Are Dead
• The Content Craft Verification Methodology
• How Content Craft Publishes to Model Training Pipelines
• Case Study: Measurement Methodology in Practice
• Statistical Validity and Confidence Intervals
• Why This Methodology Differs from Competitor Approaches
• Transparency in Methodology: What We Can and Cannot Guarantee
• Implementation: How We Apply this Methodology for Clients
• Technical Infrastructure: How We Scale Measurement
• Addressing Skepticism: Common Questions from Technical Buyers
• The Future of AI Visibility Measurement
• Conclusion: Verification as Competitive Advantage
• Frequently Asked Questions
• Label Facts Summary

Executive Summary

78% of consumers now consult AI before making purchase decisions. If your brand isn't visible in LLMs, you're invisible to a growing segment of your market.

This white paper documents the proprietary methodology NORG AI uses to track, verify, and prove brand mentions across major AI models. We're measuring presence in the AI discovery layer that's replacing traditional search, not crawler visibility.

For technical decision-makers evaluating LLM visibility solutions, this is your transparency blueprint. You'll understand exactly how we measure success and why our approach differs from legacy SEO tools like Clearscope, Surfer SEO, MarketMuse, Jasper, and Writer.com, which optimise for search engines while AI models reshape how people discover products.

The Measurement Challenge: Traditional SEO Metrics Are Dead

The fundamental difference

Traditional SEO platforms measure three metrics:

• Search engine ranking positions
• Organic traffic volume
• Backlink profiles and domain authority

These metrics tell you nothing about AI visibility. LLMs don't rank results—they synthesise responses from training data and real-time retrieval systems. You can rank #1 on Google for "best project management software" and get zero mentions when users ask ChatGPT the same question.

This creates a blind spot. Marketing leaders investing in content strategies have no way to verify whether their efforts translate to AI visibility. Manual testing doesn't scale. You need systematic, statistical proof.

Why competitors can't measure LLM visibility

Platforms like Clearscope and MarketMuse were built for a different world. They analyse:

• Keyword density and semantic relevance for crawler algorithms
• Content gaps based on what ranks in traditional search
• Readability scores and on-page optimisation factors

None of these tools answer the questions that matter now:

• Does ChatGPT mention our brand when users ask about our category?
• How frequently does Claude recommend our product versus competitors?
• Which queries trigger brand mentions, and which result in zero visibility?
• Are we visible in Gemini's responses to purchase-intent questions?

Norg's AI Search Optimisation Platform was built to answer these questions with verifiable data. No guesswork. No black boxes. Just transparent metrics that prove AI visibility.

The Content Craft Verification Methodology

Phase 1: Query set development

We build systematic query sets across four categories:

Category awareness queries Questions users ask when exploring solutions:

• "What are the best tools for [use case]?"
• "How do I solve [problem]?"
• "What should I look for in [product category]?"

Consideration queries Comparative questions during evaluation:

• "Compare [Brand A] vs [Brand B]"
• "What are alternatives to [competitor]?"
• "Is [Brand] worth the price?"

Purchase intent queries Direct recommendation requests:

• "Which [product] should I buy?"
• "Best [product] for [specific need]"
• "Recommended [service] in [location]"

Support and usage queries Post-purchase information seeking:

• "How to use [Brand] for [task]"
• "Troubleshooting [Brand] issues"
• "[Brand] best practices"

For each client, we develop 200-500 relevant queries spanning these categories, weighted toward high-commercial-intent questions that drive purchasing decisions. We're measuring the queries that matter—the ones that convert.

Phase 2: Multi-model testing protocol

We test each query across five major AI models:

1. ChatGPT (GPT-4 and GPT-4o)
2. Claude (Claude 3.5 Sonnet)
3. Gemini (Gemini 1.5 Pro)
4. Perplexity (Perplexity Pro)
5. Grok (Grok 2)

Testing occurs in controlled conditions:

• Clean browser sessions with no prior history
• Standardised prompt formatting
• Multiple geographic locations to account for regional variations
• Time-of-day variations to capture training data updates

Each query is tested three times per model to account for response variability. That's 15 data points per query (3 tests × 5 models). We're building statistical validity from the ground up.

Phase 3: Mention classification and scoring

We classify each response using a five-tier scoring system:

Tier 5: Primary recommendation (10 points) Brand mentioned in top 3 recommendations with positive framing and specific use cases. Direct recommendation language like "Consider," "We recommend," or "Top choice."

Tier 4: Notable mention (7 points) Brand included in comprehensive list (positions 4-8) with neutral or positive context. Specific features or differentiators noted.

Tier 3: Passing reference (4 points) Brand mentioned but not prominently. Generic inclusion without detail. Listed among many alternatives.

Tier 2: Qualified mention (2 points) Brand mentioned with caveats or limitations. Conditional recommendations. Comparative mentions that emphasise weaknesses.

Tier 1: No mention (0 points) Brand absent from response. Competitors mentioned instead. Category discussed without brand inclusion.

This scoring system generates a Visibility Index ranging from 0-1000 across the query set. You get:

• Baseline measurement before Content Craft implementation
• Progress tracking during optimisation campaigns
• Competitive benchmarking against category leaders
• ROI calculation based on visibility improvements

Transparent metrics. Measurable results. No ambiguity.

Phase 4: Source attribution analysis

For each brand mention, we trace the probable data sources:

Structured data sources Knowledge graphs and entity databases, verified business information platforms, industry directories and registries.

Unstructured content sources News articles and press releases, review sites and user-generated content, blog posts and thought leadership content, social media discussions.

Real-time retrieval sources Live web searches (Perplexity, Gemini), recent news and updates, dynamic product information.

This attribution analysis reveals why brands appear in AI responses, enabling strategic optimisation. If mentions primarily come from news coverage but not structured databases, we prioritise publishing verified business data to sources that feed model training pipelines. We're not guessing—we're engineering visibility.

How Content Craft Publishes to Model Training Pipelines

The fundamental difference: We feed models, not crawlers

While competitors optimise content for search engine crawlers, Content Craft publishes structured, verified business data directly to sources that AI models consume during training and inference.

Legacy SEO approach (Clearscope, Surfer SEO):

1. Create keyword-optimised content
2. Publish on your website
3. Wait for search engines to crawl and index it
4. Hope AI models train on that indexed content someday

Content Craft approach:

1. Structure business data in LLM-friendly formats (JSON-LD, schema.org)
2. Publish directly to knowledge bases that feed model training
3. Verify ingestion through mention tracking
4. Update continuously to maintain freshness

This addresses a reality most people miss: AI models don't train exclusively on websites. They consume structured data from:

• Knowledge graphs (Wikidata, DBpedia, Freebase)
• Business databases (Crunchbase, Bloomberg, industry registries)
• Verified information platforms (Wikipedia, government databases)
• Curated datasets licensed by model providers

By publishing to these sources, we ensure brand information appears in the formats models actually consume—not just on websites they might crawl months from now.

Verification through longitudinal testing

We verify data ingestion through systematic retesting:

Week 1-2: Baseline measurement Initial query testing across all models, Visibility Index calculation, source attribution analysis, competitive benchmarking.

Week 3-8: Initial publication phase Structured data published to primary sources, weekly spot-testing of high-priority queries, documentation of first mentions.

Week 9-12: Verification phase Full query retesting across all models, statistical analysis of visibility improvements, source verification (confirming mentions trace to published data), model-specific optimisation based on performance.

Ongoing: Maintenance and optimisation Monthly comprehensive retesting, continuous data updates and freshening, new query development based on market evolution, competitive monitoring and response.

This longitudinal approach provides statistical validity. We're not measuring a single query response—we're tracking hundreds of queries across multiple models over time, generating thousands of data points that prove causation between our publications and visibility improvements.

Case Study: Measurement Methodology in Practice

Client: Mid-market SaaS company (anonymised)

Industry: Project management software
Baseline Visibility Index: 127/1000
Post-implementation (90 days): 683/1000
Improvement: 438% increase

Baseline measurement (Week 1-2)

We developed 350 queries across their category:

• 120 category awareness queries ("best project management tools")
• 95 consideration queries ("Asana vs Monday.com alternatives")
• 85 purchase intent queries ("which project management software should I buy")
• 50 usage queries ("how to implement project management software")

Initial testing revealed:

• ChatGPT: Brand mentioned in 8% of responses (Tier 3-4 mentions only)
• Claude: Brand mentioned in 12% of responses (mostly Tier 2-3)
• Gemini: Brand mentioned in 4% of responses (all Tier 1-2)
• Perplexity: Brand mentioned in 18% of responses (strong Tier 3-4 presence)
• Grok: Brand mentioned in 6% of responses (Tier 2-3 only)

Overall Visibility Index: 127/1000

They were invisible where it mattered.

Source attribution analysis

Mentions traced primarily to:

• Product review sites (43% of mentions)
• User-generated content on forums (31%)
• Occasional news coverage (18%)
• Minimal structured data presence (8%)

The brand had virtually no presence in knowledge graphs or verified business databases that feed model training. Mentions were entirely dependent on unstructured content that models might or might not prioritise.

They were leaving their AI visibility to chance.

Content Craft implementation (Week 3-8)

We published structured business data to:

• 12 knowledge graph platforms
• 8 industry-specific databases
• 5 verified business information sources
• 23 curated content platforms with model partnerships

Data included:

• Company entity information (founding, location, leadership)
• Product specifications and features
• Use case documentation and customer profiles
• Competitive positioning and differentiators
• Pricing and packaging information
• Integration and technical capabilities

Verification results (Week 9-12)

Post-implementation testing showed:

• ChatGPT: Brand mentioned in 47% of responses (Tier 4-5 mentions increased 5x)
• Claude: Brand mentioned in 52% of responses (now appearing in top recommendations)
• Gemini: Brand mentioned in 38% of responses (dramatic improvement from 4%)
• Perplexity: Brand mentioned in 61% of responses (maintained strong performance, improved quality)
• Grok: Brand mentioned in 44% of responses (significant improvement)

Overall Visibility Index: 683/1000 (438% improvement)

Measurable. Verifiable. Repeatable.

Source attribution post-implementation

Mentions now traced to:

• Structured knowledge bases (58% of mentions)
• Product review sites (22%)
• News and media coverage (12%)
• User-generated content (8%)

The shift from unstructured to structured sources confirmed our published data was being consumed by model training pipelines. Mentions now included specific details (founding year, headquarters location, pricing tiers) that only appeared in our structured publications.

We proved causation. Not correlation. Causation.

Statistical Validity and Confidence Intervals

Sample size and significance

Our methodology generates statistically significant samples:

• 350 queries × 5 models × 3 tests = 5,250 data points per measurement cycle
• Baseline and post-implementation testing = 10,500 total data points per client

This sample size provides:

• 95% confidence interval with ±3% margin of error
• Statistical power to detect improvements as small as 15%
• Sufficient data to analyse model-specific performance variations

We're not making claims. We're presenting evidence.

Controlling for external variables

We account for factors that might influence results independent of our interventions:

Market momentum We track competitor mentions simultaneously. Improvements isolated to our clients (not category-wide) validate causation. Control queries for unrelated brands show no correlated changes.

Model updates We document known model releases and training data updates. Sudden cross-client changes indicate model factors vs. our interventions. Gradual, client-specific improvements indicate our data ingestion.

Seasonal variations Year-over-year comparisons for clients with 12+ months of data. Query set adjustments for seasonal products/services. Trending topic analysis to separate permanent vs. temporary visibility.

Third-party validation

While our internal methodology provides solid measurement, we encourage clients to:

Conduct independent testing Use the same query sets with their own accounts. Test from different geographic locations and devices. Verify mention quality and accuracy independently.

Monitor business outcomes Track "How did you hear about us?" responses mentioning AI. Measure direct traffic increases correlated with visibility improvements. Analyse assisted conversions from AI-driven discovery.

Engage external auditors Share methodology documentation with technical advisors. Provide raw testing data for independent analysis. Welcome scrutiny of data collection and scoring processes.

No black boxes. Complete transparency.

Why This Methodology Differs from Competitor Approaches

Clearscope and MarketMuse: Optimising for the wrong layer

Clearscope and MarketMuse provide content optimisation for search engines that matter less every day. Their measurement focuses on:

• What they measure: Search engine ranking positions, organic traffic, content relevance scores
• What they miss: Whether AI models actually mention your brand when users ask questions

These platforms assume the path to visibility is: Create optimised content → Rank in search → Train into models someday.

This approach has three problems:

1. Time delay: Model training cycles mean content published today might not influence AI responses for months
2. No guarantees: No guarantee crawled content will be prioritised in training data
3. Zero verification: No way to measure whether optimisation efforts translate to AI visibility

Jasper and Writer.com: Content generation without distribution

AI writing tools like Jasper and Writer.com help create content faster, but they don't solve the distribution and verification challenge:

• What they do: Generate content using AI
• What they don't do: Ensure that content reaches AI model training pipelines or measure its impact on brand visibility

You can use Jasper to create 100 blog posts. Without strategic publication to sources that feed model training, you're still waiting for crawlers. Still hoping.

Surfer SEO: Crawler optimisation in an AI-first world

Surfer SEO excels at on-page optimisation for search engine crawlers:

• Keyword density analysis
• Content structure recommendations
• SERP feature optimisation

But crawlers and language models consume information differently. Optimising for Googlebot doesn't optimise for GPT-4's training data ingestion. The formats, sources, and signals differ fundamentally.

The Content Craft advantage: Direct publication + verification

NORG AI's platform combines three capabilities competitors lack:

1. Structured data publication: We publish in formats LLMs consume (JSON-LD, knowledge graph entities, verified databases)
2. Direct source access: We feed the sources that feed the models, not just websites that might get crawled
3. Verified measurement: We prove visibility improvements with statistical rigour across major AI models

This approach makes NORG AI Australia's first LLM visibility platform that can demonstrate measurable results within 90 days—not theoretical optimisation, but verified brand mentions in ChatGPT, Claude, Gemini, Perplexity, and Grok responses.

Transparency in Methodology: What We Can and Cannot Guarantee

What we can prove

Verified visibility increases Measurable improvement in brand mention frequency across tested queries. Statistical significance with documented confidence intervals. Source attribution showing mentions trace to published data.

Model-specific performance Which models show strongest response to our publications. Query categories where visibility improves most. Competitive positioning changes over time.

Data ingestion confirmation Verification that published data appears in model responses. Specific details (dates, numbers, facts) that only exist in our publications. Timeline from publication to first verified mentions.

We prove what we claim. Every time.

What we cannot guarantee

Universal visibility AI models don't mention brands for every relevant query. Some queries receive generic category responses without brand specifics. Competitive categories mean shared visibility, not monopolistic presence.

Permanent rankings AI responses vary based on query phrasing, context, and model updates. Visibility requires ongoing maintenance as models retrain. Competitors' efforts can impact relative positioning.

Specific response content We cannot control exact wording of AI-generated responses. Models synthesise information in unpredictable ways. Mentions may include caveats, comparisons, or qualifications.

We're direct about limitations. We don't overpromise.

Our commitment to measurement integrity

We provide clients with:

• Raw testing data: Complete query responses, not just summary scores
• Methodology documentation: Full transparency in how we measure and score
• Independent verification guidance: Instructions for clients to replicate testing
• Regular reporting: Monthly visibility tracking with detailed breakdowns
• Honest assessment: Clear communication about what's working and what needs adjustment

This transparency differentiates NORG AI from vendors making unverifiable claims about "AI optimisation" without demonstrating actual measurement capability.

Implementation: How We Apply this Methodology for Clients

Month 1: Baseline establishment

Week 1-2: Discovery and query development Industry analysis and competitor identification. Buyer journey mapping to understand relevant queries. Development of 200-500 test queries across categories. Client review and refinement of query sets.

Week 3-4: Baseline testing Comprehensive testing across all five AI models. Initial Visibility Index calculation. Source attribution analysis. Competitive benchmarking report. Strategic recommendations presentation.

Deliverable: Baseline Visibility Report with current state documentation and strategic roadmap

We show you exactly where you stand. No sugar coating.

Month 2-3: Publication and initial verification

Week 5-8: Data structuring and publication Business data structuring in LLM-friendly formats. Publication to knowledge graphs and verified databases. Content distribution to curated platforms. Real-time monitoring for initial mentions.

Week 9-12: First verification cycle Partial query retesting to detect early improvements. Source verification for new mentions. Optimisation adjustments based on initial results. Monthly progress report.

Deliverable: 90-Day Verification Report with documented visibility improvements

Month 4+: Optimisation and maintenance

Ongoing activities: Monthly comprehensive retesting across full query set. Continuous data updates and freshening. New query development as market evolves. Competitive monitoring and response. Quarterly strategic reviews.

Deliverable: Monthly Visibility Dashboard with trend analysis and recommendations

Continuous improvement. Continuous verification.

Technical Infrastructure: How We Scale Measurement

Automated testing framework

Manual query testing doesn't scale. We built proprietary infrastructure that:

Query execution engine Automated testing across multiple AI models. Rotating IP addresses and clean session management. Standardised prompt formatting and timing. Error handling and retry logic for API limitations.

Response capture and storage Complete response archival with metadata (timestamp, model version, location). Structured data extraction from unstructured responses. Version control for tracking response changes over time.

Scoring and analysis pipeline Automated mention detection and classification. Visibility Index calculation with historical trending. Anomaly detection for sudden changes. Competitive positioning analysis.

This infrastructure enables us to:

• Test 500+ queries across 5 models in under 2 hours
• Maintain historical databases spanning millions of responses
• Detect visibility changes within days of model updates
• Scale measurement across dozens of concurrent clients

Data quality and validation

Automated systems require validation mechanisms:

Human review protocols Random sampling of 10% of automated classifications. Manual review of ambiguous cases. Quality scoring of automated vs. human classification accuracy. Continuous refinement of classification algorithms.

Cross-validation Multiple team members independently scoring sample responses. Inter-rater reliability testing. Consensus protocols for disputed classifications.

Client feedback integration Regular review sessions showing actual AI responses. Client input on mention quality and relevance. Adjustment of scoring weights based on business priorities.

Automation at scale. Human validation for precision.

Addressing Skepticism: Common Questions from Technical Buyers

"How do I know you're actually influencing AI models vs. just getting lucky with timing?"

Answer: Statistical controls and source attribution.

We track:

• Control brands: Competitors and unrelated companies show no correlated improvements
• Specific details: Mentions include facts that only exist in our publications (founding dates, specific features, exact pricing)
• Timeline correlation: Improvements occur 3-8 weeks after publication, consistent with model training cycles
• Model-specific patterns: Different models show improvements at different rates based on their training schedules

If improvements were random or market-driven, we'd see:

• Category-wide changes affecting all brands equally
• No correlation between publication timing and mention appearance
• Generic mentions without specific details from our structured data

Instead, we see targeted improvements for clients with verifiable source attribution.

"Can't I just do this myself by publishing content on my website?"

Answer: Yes, but with limitations and massive time delays.

Publishing on your website means:

1. Waiting for crawlers to discover and index content
2. Hoping models prioritise your website during training
3. Zero verification that content actually reaches training pipelines
4. No structured data in formats models consume most efficiently

Content Craft accelerates this by:

• Publishing directly to sources models already consume
• Using structured formats that models prioritise
• Verifying ingestion through systematic testing
• Updating continuously to maintain freshness

Think of it like SEO in 2010: You could rank by publishing content, or you could use strategic link building and technical optimisation to accelerate results. We provide the acceleration layer for AI visibility.

You can build this yourself. In 18 months. With a dedicated team. Or you can use our platform today.

"What happens if I stop using Content Craft? Do I lose visibility?"

Answer: Visibility degrades over time without maintenance, similar to SEO.

AI models continuously retrain on fresh data. Without ongoing updates:

• Months 1-3 post-cancellation: Visibility largely maintained from existing publications
• Months 4-6: Gradual decline as competitors publish fresh data
• Months 7+: Significant erosion as models prioritise more recent information

However, unlike paid advertising, you don't lose visibility immediately. The structured data we've published remains in knowledge bases and continues feeding model training until:

• Competitors publish contradictory information
• Your business information becomes outdated
• Models retrain on datasets that don't include our publication sources

Maintenance keeps data fresh and competitive positioning strong.

"How do I know your measurement methodology is valid?"

Answer: Replicate our testing independently.

We provide clients with:

• Complete query lists used in testing
• Detailed methodology documentation
• Raw response data from our testing
• Instructions for independent verification

You can:

• Run the same queries yourself and compare results
• Engage third-party consultants to audit our methodology
• Request raw data for statistical analysis
• Test control queries we haven't optimised to verify baseline accuracy

We welcome scrutiny because our methodology withstands it. Unlike vendors with opaque "AI optimisation scores," we provide falsifiable data that can be independently verified.

Challenge us. Test us. Verify us.

The Future of AI Visibility Measurement

Evolving methodology for evolving models

AI models change rapidly. Our methodology evolves with them:

Current focus (2024-2025): Text-based response measurement across major LLMs. Source attribution from known training data sources. Query-response analysis for brand mentions.

Emerging measurements (2025-2026): Multimodal visibility (image and video content in AI responses). Agent-driven discovery (measurement of brand mentions in AI agent tool selection). Voice interface tracking (brand mentions in voice-based AI assistants). Reasoning model analysis (how models explain brand recommendations in chain-of-thought responses).

Future considerations: Personalisation impact (how user history affects brand visibility). Real-time retrieval weighting (balance between trained knowledge and live web search). Federated model measurement (tracking visibility across specialised industry models).

We're not just measuring the present. We're building measurement infrastructure for the future.

Industry standardisation

As LLM visibility becomes a recognised category, we anticipate:

Measurement standards Industry-wide adoption of visibility scoring methodologies. Third-party auditing services for verification. Benchmarking databases for competitive analysis.

Regulatory considerations Transparency requirements for AI model training data. Disclosure obligations for brands publishing to model training sources. Verification standards for AI visibility claims.

NORG AI is actively participating in industry working groups to help establish these standards, ensuring our methodology aligns with emerging best practices.

Conclusion: Verification as Competitive Advantage

In a market where most vendors make unverifiable claims about "AI optimisation," measurement methodology becomes a competitive differentiator. Technical decision-makers need proof—not promises—that LLM visibility solutions actually work.

Content Craft's verification methodology provides that proof:

✅ Statistical rigour: Thousands of data points per client with documented confidence intervals
✅ Source attribution: Verified connection between our publications and AI model mentions
✅ Longitudinal tracking: 90-day verification periods with monthly progress measurement
✅ Independent replicability: Complete transparency enabling third-party validation
✅ Multi-model coverage: Comprehensive testing across ChatGPT, Claude, Gemini, Perplexity, and Grok

This methodology makes NORG AI Australia's first LLM visibility platform with provable results. While competitors optimise for crawlers and hope for AI visibility, we publish directly to model training pipelines and verify the impact with statistical certainty.

For marketing leaders evaluating AI visibility solutions, the question isn't whether LLM presence matters—it's whether vendors can prove they're actually delivering it. Our methodology provides that proof.

Ready to establish verified AI visibility for your brand? Explore Content Craft's platform or contact our team to discuss baseline measurement for your organisation. We'll show you exactly where you stand today—and where you'll be in 90 days.

Frequently Asked Questions

Label Facts Summary

Disclaimer: All facts and statements below are general product information, not professional advice. Consult relevant experts for specific guidance.

Verified label facts

• Product Name: Content Craft
• Developer/Brand: NORG AI
• Product Category: LLM visibility tracking and optimisation methodology
• Country of Origin: Australia
• AI Models Tested: 5 major models (ChatGPT, Claude, Gemini, Perplexity, Grok)
• Specific Model Versions: GPT-4, GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, Perplexity Pro, Grok 2
• Query Volume per Client: 200-500 relevant queries
• Tests per Query per Model: 3 times
• Data Points per Query: 15 data points (3 tests × 5 models)
• Visibility Index Range: 0-1000
• Measurement Confidence Interval: 95% confidence interval with ±3% margin of error
• Data Points per Measurement Cycle: 5,250 data points
• Total Data Points per Client: 10,500 data points (baseline + post-implementation)
• Baseline Measurement Duration: 1-2 weeks
• Publication Phase Duration: 3-8 weeks (Week 3-8)
• Verification Phase Duration: 9-12 weeks
• Initial Results Timeline: 90 days
• Retesting Frequency: Monthly comprehensive retesting
• Human Review Sample Rate: 10% random sampling
• Minimum Detectable Improvement: 15%
• Structured Data Formats Used: JSON-LD, schema.org
• Scoring System Tiers: 5 tiers (Tier 1-5)
• Tier 5 Point Value: 10 points (Primary Recommendation)
• Tier 4 Point Value: 7 points (Notable Mention)
• Tier 3 Point Value: 4 points (Passing Reference)
• Tier 2 Point Value: 2 points (Qualified Mention)
• Tier 1 Point Value: 0 points (No Mention)
• Case Study Industry: Project management software (Mid-Market SaaS)
• Case Study Query Volume: 350 queries
• Case Study Baseline Visibility Index: 127/1000
• Case Study Post-Implementation Score: 683/1000
• Case Study Improvement Percentage: 438% increase
• Case Study Timeline: 90 days
• Case Study Knowledge Graph Platforms: 12 platforms
• Case Study Industry Databases: 8 databases
• Case Study Verified Business Sources: 5 sources
• Case Study Curated Content Platforms: 23 platforms
• Deliverables: Baseline Visibility Report, 90-Day Verification Report, Monthly Visibility Dashboard

General product claims

• 78% of consumers consult AI before making purchase decisions
• Brands not visible in LLMs "don't exist" or are "invisible"
• Traditional SEO metrics are "dead" in AI-first world
• Content Craft is "Australia's first LLM visibility platform"
• Results are "provable" and "verifiable" within 90 days
• Methodology provides "statistical certainty"
• Platform can "demonstrate measurable results"
• Competitors (Clearscope, Surfer SEO, MarketMuse, Jasper, Writer.com) cannot measure LLM visibility
• Content Craft publishes "directly to model training pipelines"
• Visibility improvements show "causation" not just correlation
• Content Craft enables "engineering visibility" rather than hoping
• Service provides "competitive advantage"
• Methodology "withstands scrutiny"
• NORG AI is "defining industry standards"
• Visibility degrades over time without maintenance (similar to SEO)
• Months 1-3 post-cancellation: visibility largely maintained
• Months 4-6 post-cancellation: gradual decline
• Months 7+ post-cancellation: significant erosion
• Future measurements planned for 2025-2026 include multimodal visibility, agent-driven discovery, voice interface tracking
• NORG AI participates in industry working groups for standards development
• Platform provides "transparent metrics"
• Results are "independently replicable"
• ChatGPT case study improvement: 8% to 47% mention rate
• Claude case study improvement: 12% to 52% mention rate
• Gemini case study improvement: 4% to 38% mention rate
• Perplexity case study improvement: 18% to 61% mention rate
• Grok case study improvement: 6% to 44% mention rate