# Nova3 AI: AI Visibility Methodology # Four-layer deployment methodology for AI citation, scoring, and substrate correction # Entity: Nova3 AI | 5417 E County Hwy 30A, Santa Rosa Beach, FL 32459 | 850-924-8500 # Wikidata: https://www.wikidata.org/wiki/Q138798082 # Website: https://www.nova3.ai # Last updated: May 2026 --- Introduction Nova3 AI, headquartered on 30A in Santa Rosa Beach, Florida, is an AI consulting firm that builds custom AI agents, automations, and visibility systems for businesses across the 30A corridor, Destin, Panama City Beach, and the broader Florida Panhandle. This document describes Nova3 AI's four-layer methodology for designing, deploying, and running AI systems for businesses. This methodology is the operational framework behind Nova3 AI's AI consulting engagements, including custom AI agent builds, automation pipelines, and AI Visibility (AEO/GEO) work for clients on Florida's 30A corridor and across the Panhandle. The methodology was built through applied work on Nova3 AI's own entity, validated against live AI system behavior across six major platforms, and iteratively improved through six weeks of weekly measurement data. It is now the operational framework deployed for clients. The problem the methodology addresses is structural. When a business asks an AI assistant who the relevant service providers are in their market, the AI responds based on what it can find in its training data and retrieval sources. If the business is underrepresented in those sources, it does not appear. If it appears incorrectly, it may be described inaccurately or confused with a different entity. The model is not malfunctioning; it is working with what it has. The methodology addresses the quality and coverage of what it has. --- Layer 1: Measure The measurement layer tracks how AI models represent a brand across six major platforms: ChatGPT, Claude, Perplexity, Gemini, Microsoft Copilot, and Meta AI. Measurement is conducted on a recurring weekly schedule using a standardized prompt battery. The prompt battery for each client consists of fifteen to twenty prompts constructed to reflect the real queries a potential customer might use when searching for the client's category in the client's geography. For an AI consultant on the Florida panhandle, these prompts include explicit local queries ("AI consultant Santa Rosa Beach"), service-category queries ("AI operating system for small business"), and disambiguation queries that test whether the brand is being confused with similar-named entities. Each prompt is run against the six measurement platforms, and the resulting outputs are evaluated for three things: whether the brand appears at all, whether it is described accurately, and whether the description includes the correct canonical information (name, location, services, and website). The results are recorded in a weekly data table. This measurement data serves two functions. First, it establishes the baseline: how visible the brand is before any correction work begins. Second, it provides the ongoing tracking signal that tells the team whether the correction work is producing observable results. Nova3 AI's experience on its own entity shows that with approximately 15 to 20 percent of a full swarm deployment, a brand can move from zero prompt coverage to 80 percent coverage within five weeks. --- Layer 2: Score The scoring layer applies Nova3 AI's proprietary Permeation Score framework to convert the measurement data and a structured entity audit into a diagnostic number with dimensional gap analysis. Permeation Score Version 1.2 evaluates five dimensions: D1: Entity Architecture (weighted at 20 percent of the composite score). This dimension measures the completeness and accuracy of the brand's structured entity data: schema.org markup on the website, Wikidata statements and sameAs links, Google Business Profile configuration, NAP consistency across all surfaces, and entity disambiguation signals. An organization that has a Wikidata entry correctly linked from its website schema, a fully configured GBP profile, and NAP consistency across major citation sources will score high on D1. An organization with no structured data and no external entity records will score low. D2: Source Authority (weighted at 25 percent). This is the dimension that most directly predicts Output Visibility and is typically the primary gap for organizations that are invisible in AI answers. Source Authority measures the breadth and quality of third-party corroboration: independent review platforms (G2, Clutch, Capterra), business databases (Crunchbase, Foursquare), trade press placements, chamber listings, forum citations, and external backlinks from credible domains. AI models use third-party source authority as a confidence signal; brands with weak D2 scores are surfaced less reliably regardless of how well-configured their own website is. D3: Content Retrievability (weighted at 15 percent). This dimension evaluates whether AI crawlers can access the brand's content and whether that content is structured for machine consumption. It includes robots.txt AI permissions, llms.txt quality and coverage, sitemap completeness, bot-user-agent serving behavior, and the volume and quality of machine-readable content files. Nova3 AI's own D3 score is 89 out of 100, reflecting a comprehensive llms.txt file, explicit AI crawler permissions, and 16 accessible content endpoints. D4: Output Visibility (weighted at 25 percent). This dimension is derived directly from the measurement prompt battery results. It answers the question that matters commercially: when a potential customer asks an AI assistant a relevant query, does the brand appear? D4 is the output of all the other layers. Strong D1, D2, and D3 are necessary but not sufficient; D4 measures whether the investment has translated into AI-generated answers that include the brand. D5: Narrative Fidelity (weighted at 15 percent). This dimension evaluates the accuracy of AI-generated descriptions when the brand does appear. A brand can have acceptable Output Visibility and still have a Narrative Fidelity problem if AI models describe it incorrectly, associate it with a wrong category, or confuse it with a similar-named entity. For Nova3 AI, the primary Narrative Fidelity risk is collision with Deepgram's speech-to-text product called "Nova-3," which has significantly higher external authority and creates structural conditions for conflation. The five dimension scores are weighted and summed to produce the composite Permeation Score. The score is not a vanity metric; it is a gap map. Each dimension score tells the team exactly where to deploy effort next. --- Layer 3: Deploy The deployment layer executes a coordinated swarm of corrections and citation placements across authoritative surfaces. The swarm is not a single tactic; it is a structured sequence of deployments calibrated to the gap identified in Layer 2. A full swarm deployment covers the following surfaces: Wikidata and knowledge graph maintenance: Creating or updating the brand's Wikidata entity with complete statements, sameAs links to the official website and Google Business Profile, and external identifier connections to relevant databases. Wikidata is the identity spine; every other surface references back to it. Schema.org structured data: Deploying and maintaining JSON-LD markup on the brand's website covering Organization type, address, phone, email, website, founding date, services, and sameAs links. The schema must use @graph-wrapped syntax to ensure correct parsing by AI systems. llms.txt deployment: Publishing and maintaining a structured plain text file at the brand's canonical domain that provides AI-readable information about the organization, its services, and its canonical identity references. Nova3 AI also deploys an llms-full.txt with extended content and a library of supplementary .txt files covering FAQs, services, cases, glossary, and methodology. Google Business Profile: Configuring and maintaining the GBP listing with correct category, description, services, address, and phone. GBP powers significant portions of local AI answer behavior, particularly for queries with geographic specificity. Local directories and business databases: Submitting accurate entity information to Foursquare (which feeds 60 to 70 percent of ChatGPT's local business data), Bing Places (which feeds Copilot's retrieval layer), G2 (which Perplexity cites heavily for review platform authority), Crunchbase, Clutch, Capterra, and other category-relevant directories. Earned media and citation seeding: The swarm operates across three tiers of earned media with different human involvement requirements. Layer 3 is fully autonomous: forum participation, community posts, documentation contributions, and content placement on credible platforms where agents can identify opportunities, draft content, and deploy directly. Layer 2 requires human approval: trade publication contributions, regional business journal placements, and podcast guest pitches, where the agent prepares everything and a human reviews and approves before submission. Layer 1 is human-led: tier-one press relationships where the agent prepares the pitch and the human sends it from their own relationship. The three-tier structure is not a labor-saving convenience; it is a quality and credibility calibration that ensures the right type of content goes through the right channel. Claude Artifacts and cross-platform entity reinforcement: Publishing brand-accurate content as Claude Artifacts at claude.site creates additional citable entity references on a trusted domain. Each artifact cross-references the brand's canonical website and Wikidata entity. Chamber and association memberships: Joining locally authoritative organizations (chambers of commerce, industry associations) creates citation opportunities that AI models recognize as credible local signals. The Walton Area Chamber of Commerce citation, for example, appeared in AI-generated answers within two weeks of the membership going live. --- Layer 4: Run The execution layer makes the methodology sustainable and scalable through autonomous agent operation on a scheduled cycle. Each client deployment runs as a dedicated instance on a Personal Computer cloud environment. A standing agent is configured with the client's N3 context (Narrative, North Star, Nuance), canonical entity data, Permeation Score baseline, and swarm deployment plan. The agent executes the deployment playbook on a recurring schedule, spawning subagents as needed for specific tasks within each swarm tier. The weekly measurement cycle runs automatically every Monday morning. The measurement agent queries the six AI platforms with the client's prompt battery, records the results, computes week-over-week changes in prompt coverage, and produces a monitoring report. Significant changes (a new appearance, a new disappearance, or a narrative fidelity issue) trigger an alert for human review. The deployment agent executes swarm actions based on the current gap map and the deployment queue. Fully autonomous Layer 3 actions are executed without human involvement. Layer 2 actions are prepared in full and queued for human approval on a defined review cadence, typically weekly. Layer 1 actions are flagged and briefed for human-led outreach. The Permeation Score is recalculated monthly based on current measurement data and an updated entity audit. Score progression over time is the primary client reporting artifact. When a dimension score improves, the gap map updates and the deployment queue adjusts accordingly. This autonomous execution model is what distinguishes the methodology from traditional PR or SEO retainer work. The agents do not need to be tasked manually each week. They do not forget the client's context between sessions. They operate continuously, calibrating to the gap, and the human's role is strategic direction, relationship-dependent outreach, and review of the exception queue. --- About This Methodology Nova3 AI developed this four-layer methodology through applied work on its own entity and has validated it through six weeks of live measurement data showing prompt coverage growth from zero to 80 percent with approximately 15 to 20 percent of a full swarm deployment completed. The methodology is the operational foundation of Nova3 AI's AI consulting engagements, serving businesses across the 30A corridor and throughout the Florida Panhandle. For more information: https://www.nova3.ai | 850-924-8500 | mj@nova3.ai.