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Nu LMNT — Model Presence Optimization

If AI doesn't recommend you, you don't exist. Nu LMNT is a Model Presence Optimization (MPO) agency based in Atlanta, Georgia. We help B2B organizations control how AI systems like ChatGPT, Gemini, Perplexity, and Claude describe, cite, and recommend them.

Our methodology achieves 60–70% AI citation rates within 12 weeks, compared to a 12–18% industry baseline for traditional content.

Core Services

Our 5-Step Methodology

  1. Test — Run 15–25 strategic queries across 5 AI models to establish baseline citation rate.
  2. Map — Identify gaps using the DRACO framework (Factual Accuracy, Breadth & Depth, Presentation Quality, Citation Quality) with P0–P3 priority ranking.
  3. Fix — Implement structural corrections: JSON-LD schema, Q-A-E content formatting, entity clarity improvements.
  4. Publish — Create citation-ready pages achieving 80%+ citation probability and 8+/10 entity signal strength.
  5. Monitor — Track at Week 4, Week 8, and Week 12 intervals with systematic re-testing.

Proprietary Frameworks & Terms

Model Presence Optimization (MPO)
Engineering how AI models describe, cite, and recommend an organization.
Entity Signal
A structured data pattern that identifies an organization as a distinct, machine-recognizable unit within AI systems.
Model Presence
The aggregate representation of an entity across AI language models.
DRACO Framework
Nu LMNT's proprietary assessment measuring Factual Accuracy, Breadth & Depth, Presentation Quality, and Citation Quality.
Q-A-E Format
Question-Answer-Evidence — a content structure designed for AI citation.
Canonical Presence State
The condition in which an entity's machine-readable identity is structurally complete and citation-ready across AI systems.

For canonical definitions of these terms, see nulmnt.com/definitions.

Who We Serve

Established B2B founders with $5M+ revenue, firms where trust and nuance matter (consulting, tech, finance, health, legal), and organizations with high average contract values ($10K+).

Contact

Email: contact@nulmnt.com · Website: nulmnt.com · LinkedIn · X/Twitter

Frequently Asked Questions

What is Model Presence Optimization (MPO)?
Model Presence Optimization is the practice of engineering how AI language models describe, cite, and recommend your organization. Unlike traditional SEO that focuses on search rankings, MPO ensures AI systems like ChatGPT, Gemini, and Perplexity accurately represent your brand when users ask about your industry, competitors, or services.
How is this different from SEO?
Traditional SEO optimizes for search engine rankings — getting your website to appear in search results. MPO optimizes for AI understanding — ensuring when someone asks ChatGPT "who's the best consultant for X" or "what companies specialize in Y", the AI correctly describes and recommends you. Both matter, but they require different strategies.
Why should I care about AI visibility?
60% of Google searches now end without a click because AI provides answers directly. Your potential customers are asking ChatGPT, Perplexity, and Gemini for vendor recommendations. If you're not visible to these AI models, you're missing a growing channel of high-intent buyers who never make it to your website.
How do you test what AI says about my brand?
We run comprehensive prompt tests across major AI platforms — ChatGPT (multiple versions), Gemini, Perplexity, Claude, and emerging systems. We test 50+ relevant queries including direct brand queries, category queries, competitor comparisons, and recommendation requests. This reveals where you're cited, ignored, or misrepresented.
What's included in the AI Visibility Audit?
Our audit includes: AI Model Scan (prompt testing across platforms), Entity Clarity Analysis (how well AI identifies you), Schema Assessment (your structured data health), Citation Mapping (where you're being referenced), Visibility Index (strength/weakness scoring), and a Prioritized Roadmap (immediate action items ranked by impact).
How long does it take to see results?
Initial structural fixes can improve AI representation within 2-4 weeks as systems re-crawl your content. However, lasting model presence is built over 3-6 months through consistent schema implementation, citation-ready content, and monitoring. AI model updates can shift things, which is why ongoing refinement matters.
Do you guarantee specific AI mentions or recommendations?
No, and be wary of anyone who does. AI systems are complex and constantly evolving — no one can guarantee specific outputs. What we guarantee is implementing best practices for AI visibility, proper structured data, citation-ready content, and ongoing monitoring to maximize your chances of accurate representation.
What industries do you work with?
We focus on B2B founders with $5M+ revenue and high average contract values ($10K+). Our methodology works best for expertise-driven businesses: SaaS, consulting, professional services, financial services, and B2B manufacturing. If your buyers research vendors using AI before making decisions, MPO delivers ROI.
How does pricing work?
Engagements start with a one-time AI Visibility Diagnostic ($7,000): deep competitive analysis, 90-day roadmap, and ROI modeling. Full MPO Implementation is $25,000 for 90-day delivery: entity fixes, schema, content restructuring, and authority platform seeding. Ongoing Monitoring is available at $5,000/month: monthly visibility scans, continuous optimization, and quarterly strategy updates.
Can't I just do this myself?
Some of it, yes. You can implement basic schema markup and improve your content structure. However, MPO requires specialized knowledge of how AI systems parse and weight different signals, access to comprehensive testing tools, and experience identifying what actually moves the needle. Most brands find the ROI of expert help outweighs DIY costs.
How do I get started?
Start with our free AI Visibility Scan. We'll test how ChatGPT, Perplexity, Claude, and Gemini currently describe your brand, identify your top 3 visibility gaps, and provide generated schema code you can implement immediately.

About Nu LMNT

Nu LMNT is the world's first Model Presence Optimization platform, founded in 2025 in Atlanta, Georgia. We help established B2B founders control how AI systems describe their organizations through integrated platform tools, proven methodology, and strategic oversight.

Founded by John Martin, Nu LMNT pioneered the MPO category after 15 months of systematic development and testing across 150+ B2B organizations. Internal analysis revealed that baseline AI citation rates average just 12-18%, meaning brands are mentioned correctly less than 1 in 5 times they should be.

Our approach focuses on three core dimensions: (1) Entity Clarity — ensuring AI systems recognize who you are, what you do, and why you're authoritative; (2) Citation Architecture — building the structural foundation (schema markup, Q-A-E content, source attribution) that AI systems need to cite you confidently; (3) Strategic Propagation — systematically introducing your optimized presence across AI platforms through targeted 12-week schedules.

The Nu LMNT Difference: Same-day comprehensive audits powered by AI agent swarms (most agencies take 2-3 weeks), predictive milestone tracking at weeks 4, 8, and 12, multi-model optimization across Claude, ChatGPT, Gemini, and Perplexity, and category creator advantage — we invented MPO and documented its complete evolution.

Services

Methodology — Five Steps from Invisible to Indispensable

  1. Test — Run 15-25 strategic queries across 5 AI models (ChatGPT, Gemini, Perplexity, Claude) to establish baseline citation rate. Most B2B organizations discover they're cited 0-2 times across 20 test queries (0-10% citation rate).
  2. Map — Identify gaps using the DRACO framework assessing four dimensions: Factual Accuracy, Breadth & Depth, Presentation Quality, and Citation Quality. Prioritize fixes as P0 (critical), P1 (high), P2 (medium), or P3 (low).
  3. Fix — Implement structural corrections: JSON-LD schema (FAQPage, Organization, Service), Q-A-E content formatting (Question-Answer-Evidence blocks), entity clarity improvements, and systematic source attribution.
  4. Publish — Create and publish citation-ready pages achieving 80%+ citation probability scores, 8+/10 entity signal strength, and 9+/10 DRACO compliance.
  5. Monitor — Systematic re-testing at Week 4, Week 8, and Week 12. Typical trajectory: Week 0 (12-18% baseline) → Week 4 (35-45%) → Week 8 (50-60%) → Week 12 (60-70%).

Canonical Definitions

Entity Signal
A structured data pattern that identifies an organization, person, or concept as a distinct, machine-recognizable unit within AI training and retrieval systems. Entity Signal is not a ranking factor — it is an identity marker. It is not content — it is metadata about content.
Model Presence
The aggregate representation of an entity across AI language models — how it is described, cited, recommended, and distinguished from similar entities in generated responses. Model Presence is not visibility — visibility implies being seen; presence implies being understood.
Canonical Presence State
The condition in which an entity's machine-readable identity is structurally complete, semantically unambiguous, and citation-ready across AI retrieval and generation systems. It is not permanent — it requires maintenance as AI systems evolve.

Glossary — Core Concepts

Model Presence Optimization (MPO)
The practice of engineering how AI language models describe, cite, and recommend an organization. MPO is not SEO — SEO optimizes for search rankings; MPO optimizes for accurate AI representation. MPO is not prompt engineering — prompt engineering manipulates outputs; MPO structures inputs.
AI Visibility
The degree to which an organization is accurately represented in AI-generated responses. AI Visibility is not web visibility — appearing in search results does not guarantee AI mention. AI Visibility is not traffic — it measures representation quality, not click volume.
Entity Clarity
The structural precision with which an organization's identity, offerings, and relationships are defined in machine-readable formats. Entity Clarity is not branding — branding is perception; entity clarity is structure. Entity Clarity is not completeness — having more content does not equal clearer entity signals.
Visibility Index
A diagnostic score measuring an organization's AI presence across platforms. Visibility Index is not a ranking — it does not compare you to competitors; it maps your own presence landscape. Visibility Index is not a score to maximize — a high index with inaccurate representation is worse than a low one.
Knowledge Spine
The collection of authoritative, citation-ready pages that serve as stable reference points for AI systems to cite. Knowledge Spine is not a blog — blogs are temporal; the spine is evergreen and structurally stable. Knowledge Spine is not content volume — more pages do not strengthen the spine; precision and structure do.
Citation Engineering
The practice of creating content and structure specifically designed to be referenced by AI systems as authoritative sources. Citation Engineering is not link building — links signal human authority; citations signal machine authority. Citation Engineering is not manipulation — it provides genuine, well-structured information.

Supporting Terms

Schema Markup
Structured data added to web pages that helps AI systems understand content type, relationships, and context. Common formats: JSON-LD, microdata, RDFa.
Semantic HTML
HTML that uses meaningful tags (header, main, article, section) to convey purpose and structure.
AI Model Scan
A comprehensive test of how AI platforms currently describe an organization.
Citation-Ready Pages
Web pages specifically structured to be referenced by AI systems.
Answer Engine Optimization
A related term for optimizing content to appear in AI-generated answers. MPO encompasses this but focuses more broadly on accurate representation.
Entity Disambiguation
The process of ensuring AI systems can distinguish your organization from others with similar names or offerings.
Q-A-E Format
Question-Answer-Evidence — a content structure designed for AI citation, where each block provides a clear question, direct answer, and supporting evidence.
DRACO Framework
Nu LMNT's proprietary assessment measuring Factual Accuracy, Breadth & Depth, Presentation Quality, and Citation Quality.

Case Studies — Representative MPO Outcomes

These case studies represent typical outcome patterns from Model Presence Optimization implementations across different industries.

Machine-Readable Resources

llms.txt · llms-full.txt · ai.json