RankGen is the first multi-tenant SaaS platform purpose-built to measure, model, and engineer how brands and professionals are discovered, understood, and recommended by Large Language Models.
Search has changed. When someone asks ChatGPT "what's the best CRM for startups?" or "who is the top cardiologist in Dubai?", Google rankings are irrelevant. AI models generate answers from their training data and live retrieval — and most brands are invisible in that layer. RankGen solves this.
The Core Problem: Over 60% of knowledge-seeking queries are now answered directly by AI assistants — without a click to a website. Traditional SEO tools (Semrush, Ahrefs, Moz) have no visibility into whether a brand appears in AI-generated answers, what context AI models associate with them, or how to influence that. RankGen is the first platform built exclusively for this layer.
Audit and score a brand's current AI visibility across 8 weighted dimensions. Identify exactly where and why AI models fail to recommend or mention the brand.
Build a structured entity profile — the "AI identity" of a brand — using the GEO Funnel framework. Teach AI what the brand is, who it serves, and why it matters.
Generate AI-optimized content, structured data, authority pages, and FAQ schemas designed to be cited by large language models across all major AI platforms.
Run live discovery tests against multiple AI models. Track mentions, sentiment, category ownership, and drift over time with automated alerts.
RankGen's dual-mode architecture serves two distinct entity types: organizations with websites and digital presence, and individual professionals whose AI visibility is built around their personal reputation and credentials.
For companies, startups, SaaS products, e-commerce brands, and any organization with a web presence. The platform crawls the live website and scores its AI discoverability.
For individual professionals — doctors, lawyers, consultants, executives, academics, financial advisors — whose AI visibility is reputation-based, not website-based. No domain required.
Why this matters: When someone asks Claude "who is the best immigration lawyer in the UAE?" or "recommend a pediatric cardiologist in Riyadh", the answer is not determined by Google rankings. It is determined by the depth, credibility, and structure of that professional's presence in AI training data. RankGen's Professional Profile Mode is the first tool designed to influence this outcome.
RankGen is not a single feature — it is a platform of interconnected intelligence and engineering tools, each targeting a specific gap in how brands interact with AI systems.
Deep-scan of a brand's web presence across 8 scored dimensions. Produces a 0–100 AI Visibility Score with dimension-by-dimension breakdown, gap report, and prioritized action list.
An 8-phase guided onboarding framework that builds the brand's complete AI entity profile — from basic definition through governance. Outputs a GEO Readiness Score (0–100).
GPT-4o-powered content generation tuned for AI citation: homepage H1s, authority pages, FAQ sections (with JSON-LD), blog titles, comparison outlines, and AI agent system prompts.
Live query execution against AI models. Generates category, comparison, and branded query sets; fires them at LLMs; parses responses for brand mentions, sentiment, and positioning.
Defines a Category Definition Statement, Brand Positioning Paragraph, Authority Page architecture, and a 30-60-90 day AI visibility roadmap for owning a specific category in AI responses.
Multi-model comparison engine. Runs identical queries across GPT-4o and GPT-4o Mini in parallel, generating consistency scores, behavior profiles, and automated drift detection alerts.
Generates a semantic knowledge graph showing how AI systems understand the brand — its connections to concepts, competitors, attributes, categories, and authority signals.
Monitors external AI mentions — tracks which queries trigger brand mentions, which platforms cite the brand, and whether the citation is primary, secondary, or comparative.
Compares a brand's defined AI identity (GEO Funnel output) against what AI models actually say. Identifies specific gaps and generates remediation recommendations.
Automatic language detection. Content generation and category strategy in 10+ languages including Arabic (RTL). Purpose-built for MENA market where Arabic GEO is a blue ocean.
Simulates how AI retrieval systems (RAG pipelines) would select and surface brand content, testing snippet optimization, structured data readability, and citation likelihood.
Generates production-ready, AI-optimized React/Vite websites: homepage, authority page, FAQ, blog, JSON-LD, Open Graph, and sitemap — ready for immediate deployment.
RankGen's scoring engine evaluates each brand across 8 weighted dimensions, combining heuristic content analysis with GPT-4o-powered semantic evaluation to produce a 0–100 AI Visibility Score.
The methodology is grounded in how LLMs select information to include in responses: models favor content that is categorically clear, structurally parseable, educationally deep, and geographically anchored. Each dimension targets one of these selection criteria.
Scores are enhanced with GPT-4o semantic analysis that evaluates tone, category alignment, and content quality beyond what heuristic rules can capture. The AI layer also generates personalized gap recommendations specific to the brand's category and geography.
The GEO Funnel is RankGen's proprietary entity-building framework. It guides brands through a structured process of defining, proving, and governing their AI identity — culminating in a GEO Readiness Score. Each phase targets a specific layer of how AI models understand and represent entities.
Establishes the fundamental AI identity: what the brand is, what category it owns, its core value proposition, and its target market. This is the "who we are" layer that all AI prompts and training data reference.
Defines the surrounding market context: competitive landscape, target audience, use cases, and the problems the brand solves. Gives AI models the reference frame to place the brand correctly in category comparisons.
Documents the Experience, Expertise, Authoritativeness, and Trustworthiness signals that AI models use to assess credibility. Includes key people, credentials, awards, publications, and trust indicators.
Gathers social proof, client validation, and verifiable outcome data. AI models are trained to favor brands with specific, quantifiable, third-party validated claims over generic assertions.
Defines the branded and non-branded query sets the brand should appear in when AI users ask questions. Includes primary, secondary, and long-tail natural language phrases aligned to how humans ask AI assistants.
Answer Engine Optimization: pre-defines the answers the brand should provide to the top 5–10 questions AI assistants receive about the category. These become the source material for FAQ schema and content generation.
Cross-validates the entity profile against live AI outputs. Runs discovery tests to check whether the brand appears in relevant queries, and compares actual AI responses against the desired AI identity.
Establishes ongoing AI identity management: review cadence, update protocols, and monitoring thresholds. As AI models update with new training data, the brand's entity profile requires active maintenance to retain visibility.
Completion of all 8 phases generates a GEO Readiness Score (0–100) — a composite metric that directly predicts a brand's likelihood of appearing in AI-generated answers. Each phase is weighted by its impact on AI citability. The score is recalculated on every update, giving brands a real-time indicator of their AI identity completeness.
AI brand visibility is not uniform. ChatGPT, Claude, Gemini, and Perplexity have different training data, different retrieval strategies, and different biases toward different content types. RankGen tests visibility across all major platforms simultaneously.
| Platform | Query Type Strengths | Retrieval Behavior | Key Focus for Brands | Status |
|---|---|---|---|---|
| ChatGPT (GPT-4o) | Product comparisons, B2B research, "best X for Y" queries | Training knowledge + web browsing (Bing). Favors authoritative, structured content. | JSON-LD schema, authority pages, FAQ content, educational depth | Live Testing |
| Claude (Anthropic) | Technical and enterprise research, nuanced comparisons | Training data focused. Favors long-form, citation-rich, technically accurate content. | Educational depth, credentials, technical specificity, E-E-A-T signals | Roadmap |
| Perplexity AI | Real-time research queries, trending topics | Live web search with citation. Favors indexable, crawlable HTML content over JavaScript. | Server-rendered content, semantic HTML, sitemap coverage, fresh content | Roadmap |
| Gemini (Google) | Google Workspace users, productivity-oriented queries | Google Search index + Training data. Favors content that already ranks in Google. | Google Business Profile, Google reviews, structured data, Google Search rank | Roadmap |
| Microsoft Copilot | Enterprise procurement, B2B vendor evaluation | Bing index + training. High-intent commercial queries. Favors brand consistency. | Bing Webmaster data, consistent NAP, brand authority signals | Roadmap |
GPT-4o-mini generates 3 query sets: branded queries (mentioning the brand by name), category queries (generic category questions), and comparison queries ("X vs Y" format). 8 queries per set = 24 queries per test run.
Each query is sent to live AI model APIs. Responses are captured in full, then parsed for: brand mention presence, mention position (primary/secondary/comparative), sentiment polarity, and competitor mentions.
A second AI layer (GPT-4o) analyzes each response for tone, category framing, competitive positioning, and recommendation confidence. Scores each response on a 0–100 mention quality scale.
Results are compared to previous test runs. Statistically significant changes in mention rate, sentiment, or position trigger automated drift alerts. Brands are notified when their AI visibility shifts.
RankGen serves any entity that cares about how they appear in AI-generated answers — from enterprise brands to solo professionals.
Buyers use AI to research software alternatives. A SaaS brand invisible to ChatGPT is invisible to a significant share of its buyer journey. Highest urgency segment.
Doctors, lawyers, financial advisors, consultants, and executives. Patients and clients increasingly ask AI assistants for professional recommendations. Professional Profile Mode serves this segment.
Law firms, management consultancies, accounting firms, healthcare groups. Competing on AI recommendation requires the same infrastructure as competing on Google — but the tools have not existed until now.
Arabic-language AI content is dramatically underrepresented. MENA brands that invest in Arabic GEO now face virtually zero competition for AI category ownership in their market.
Emerging brands have a rare opportunity — they can build their AI identity correctly from day one, rather than retrofitting. RankGen's GEO Funnel is purpose-built for this founding-stage investment.
Multi-brand organizations need AI visibility infrastructure at scale. RankGen's multi-tenant architecture supports multiple brands under one organization, with per-brand scoring and reporting.
The SEO tool market (Semrush, Ahrefs, Moz, Clearscope) is built entirely around Google's page ranking algorithm. None of these tools have visibility into how LLMs select, weight, and cite content. RankGen operates in a fundamentally different layer.
| Capability | Traditional SEO Tools | RankGen |
|---|---|---|
| AI mention tracking | ✗ Not possible — built for search engines | ✓ Core feature — live testing across AI platforms |
| LLM-specific scoring | ✗ Scores correlate with Google ranking, not AI citability | ✓ 8-dimension scoring model designed for LLM selection logic |
| Entity profile building | ✗ No concept of AI entity identity | ✓ GEO Funnel — structured 8-phase entity construction |
| Professional profiles | ✗ Website-only. No individual professional mode | ✓ Dedicated professional mode for doctors, lawyers, consultants |
| Arabic / MENA market | ✗ Marginal Arabic support in most tools | ✓ Full Arabic RTL support, MENA GEO strategy, Arabic content generation |
| AI content generation | ⚠ Generic AI writers (not LLM-citability focused) | ✓ Content generated specifically to be cited by AI models |
| Multi-model comparison | ✗ No LLM behavior comparison capability | ✓ Parallel multi-model testing, drift detection, behavior profiling |
| Knowledge graph construction | ⚠ Keyword clustering, not semantic entity graphs | ✓ AI-generated semantic knowledge graph of brand entity relationships |
| Answer engine optimization | ✗ Featured snippet optimization only (Google) | ✓ AEO framework — Q&A pairs optimized for AI assistant citations |
| Category ownership strategy | ⚠ Keyword topic clusters only | ✓ Category Definition Statements + 30-60-90 day AI visibility roadmap |
The Market Timing Argument: The window to establish AI brand visibility is analogous to 2004–2008 in SEO — early movers who build their AI entity infrastructure now will own their categories in AI responses for years. Brands that wait will find that competitors have already been cited thousands of times, creating a training data advantage that is extremely difficult to overcome.
RankGen is built as a production-grade, multi-tenant SaaS application with a modern, scalable stack. All data is tenant-isolated. The platform integrates GPT-4o for semantic analysis and content generation, with a clear path to multi-model expansion.
12+ relational models: organizations, users, brands, audits, generatedContent, querySets, discoveryTests, testResponses, discoveryScores, knowledgeGraphs, retrievalSimulations, answerSimulations, entityProfiles. All scoped by organization_id.
RankGen operates on a tiered subscription model with per-seat and per-usage components. The platform serves both self-serve SMBs and managed enterprise accounts, with a high-margin AI operations model.
Solo brand owners, individual professionals, early-stage startups. Core audit, GEO Funnel access, limited discovery testing. Self-serve onboarding.
Growing brands and professional service firms. Full audit suite, unlimited content generation, multi-model testing, category ownership tools, team seats.
Agencies and multi-brand enterprises. Multiple brand workspaces, custom query sets, API access, white-label options, dedicated onboarding, and SLA.
AI operations cost per audit run is low and declining. The incremental cost of serving an additional brand is marginal. Content generation is the primary variable cost, offset by per-generation pricing. Network effects emerge as aggregate anonymized brand data improves scoring model accuracy across the platform.
RankGen's long-term vision is to become the global operating system for AI brand presence — the platform every brand uses to manage how AI systems understand and represent them, in the same way they use Google Search Console today for web search.
Establish category leadership in MENA GEO market. 500+ brands onboarded. Arabic + English parity. Partnerships with regional business councils.
Multi-model testing live across all 5 platforms. Professional Profile Mode scaled. Agency tier launched. First enterprise contracts. Series A positioning.
API-first architecture for integration into agency stacks. AI identity management becomes a recurring board-level metric. RankGen Score becomes an industry standard.
Every brand audited, every discovery test run, and every GEO Funnel completed adds to RankGen's aggregate dataset on AI visibility signals. This data trains better scoring models that competitors cannot replicate.
As more brands in a category use RankGen, the platform develops richer comparative data. Category benchmarks, competitive scoring, and gap analysis improve with scale — creating a compounding accuracy advantage.
The GEO Funnel framework, 8-dimension scoring model, and entity profiling methodology represent significant R&D that creates a durable advantage. These are not features — they are a platform architecture requiring years to replicate.
As AI platforms release enterprise APIs (Perplexity, Claude, Gemini), RankGen is positioned to be the first B2B aggregator — the single platform where brands manage visibility across all AI assistants simultaneously.
Every brand in the world will eventually need to manage how AI systems understand and represent them. This is not a feature of a marketing tool — it is a new infrastructure category. RankGen is building that infrastructure today, while the market is still forming and the competitive moat is still buildable.