Interactive GEO Tools

Comprehensive multi-mode assessment covering crawlability, content readiness, and authority signals. Includes a KPI diagnostic mode that maps symptom patterns to probable root causes across all three streams.

Deep-dive assessment for a single stream. Select Content, Technical, or Business and evaluate phase-specific capabilities with prioritized action items. Useful after the general diagnostic identifies a weak stream.

Determine which of the methodology's four implementation phases your organization is ready for. Evaluates prerequisites across streams so you invest effort at the appropriate maturity level.

Assess the six bi-directional handoff flows between streams. Identifies coordination decay—the root cause the methodology was built to address—before it degrades output quality.

Evaluate any page against a 45-point scoring system before publication. Covers research-validated techniques from the Princeton GEO study (quotation addition, statistics integration, source citations) alongside GEO-16 structural factors and E-E-A-T signals.

Score content against all 16 pillars identified in UC Berkeley's GEO-16 framework (Kumar & Palkhouski, 2025). Covers six categories: Metadata & Freshness, Semantic HTML, Structured Data, Evidence & Citations, Authority, and Content Quality.

Comprehensive audit of Experience, Expertise, Authoritativeness, and Trust signals as they apply to AI citation readiness. Includes expanded signal sets with tiered scoring across author credentials, organizational authority, and trust indicators.

Determine the optimal content structure for any piece based on the customer's query type, job dimensions (functional, emotional, social), and journey stage. Maps Jobs-to-Be-Done to specific content formats that align with how AI systems categorize intent.

Plan your content architecture using the hub-and-spoke model. Define pillar pages (hubs) that establish topical authority and map supporting content (spokes) that covers specific subtopics—all interconnected to maximize AI entity recognition.

Identify which products or services deserve hero-level GEO investment using multi-criteria ABC analysis adapted for AI citation optimization. Prevents the common mistake of spreading effort equally across a full catalog.

Generate JSON-LD structured data for Organization, Product, Person, FAQPage, HowTo, and other schema types. Built on Schema.org V29.4 with complete property coverage. Includes @id cross-referencing for connecting entities across pages into a coherent knowledge graph.

Configure robots.txt directives optimized for the AI crawler ecosystem. Distinguish between search/attribution crawlers (OAI-SearchBot, PerplexityBot) that drive citations and training crawlers (GPTBot, ClaudeBot) that build model knowledge—each requiring a different access strategy.

Determine the optimal rendering strategy—SSR, SSG, ISR, or dynamic rendering—based on your page types, content freshness requirements, and AI crawler behavior. Critical because 69% of AI crawlers cannot execute JavaScript, making client-side rendered content invisible.

Classify competitors into distinct categories—direct, indirect, aspirational, perceived, and emerging—and separate those you learn from (intelligence tracking) from those you measure against (SOV-AI benchmarking). A recommended first step before committing to long-term sentinel query strategies in the Sentinel Query Set Builder.

Calculate your Share of Voice in AI with position weighting. Enter citation data from sentinel queries across ChatGPT, Perplexity, Claude, Gemini, Grok, DeepSeek, and Copilot to see your competitive standing with weighted and unweighted breakdowns.

Measure not just whether you're cited, but how you're cited. Scores citation quality across accuracy, position (first mention vs. later), sentiment, link presence, and context richness. A high CQS means prominent, accurate citations—not just passing mentions.

Build and size your sentinel query set with statistically grounded methodology. Guides query selection across customer journey stages and category types, with detection probability calculations so you understand the confidence level your measurement set provides.

Determine the optimal number and composition of risk detection queries for your sentinel set. Uses detection probability methodology to balance coverage with practical monitoring capacity, so you catch reputation-threatening AI outputs before they spread.

SCCT-based (Situational Crisis Communication Theory) decision framework for managing AI-generated misinformation. Classifies crisis severity, routes to appropriate response protocols, provides platform-specific correction procedures, and includes a post-incident review (PIR) template. Designed around the legal precedent established in Air Canada v. Moffatt (2024), which confirmed company liability for AI-generated information.

Interactive visualization of how the methodology's concepts relate to each other. Click any node—streams, metrics, techniques, failure modes—to see its connections and understand the interdependencies that make cross-functional coordination essential.

Visual timeline of the four-phase implementation model showing what happens in each phase, which deliverables are produced, and how streams coordinate as organizational maturity increases. Serves as both a planning tool and a progress tracker.