NER-Powered Keyword Intelligence

Know Which Keywords Help You Rank on Google
— and Get Cited by AI

GEO Bot is IcyPluto's SEO + GEO optimization tool. It scores keyword difficulty for LLM responses, maps the entities AI uses to pick citation winners, and tells your content team exactly what to change so you rank on search engines and show up in AI-generated answers.

Free analysis · No credit card required

NER

— Entity Extraction

10+

— Semantic Dimensions

<30s

— Analysis Time

app.icypluto.com/geo-bot

Keyword Difficulty Report

LLM Citation Analysis

best project management software

AsanaNotionMonday.com

AI writing assistant for teams

JasperCopy.aiWritesonic

SEO audit tool 2025

AhrefsSemrushScreaming Frog

Difficulty 0–100 based on entity density, semantic overlap, and LLM citation frequency.

Intelligence

What GEO Bot Analyses

Every signal is built around one question: what does an LLM need to see in your content before it recommends your brand over a competitor's — for this keyword, right now?

Keyword Difficulty Scoring

Scores each keyword 0–100 based on how competitive it is for both search engine ranking and AI citations factoring in entity density, semantic overlap, and real citation frequency across ChatGPT, Gemini, and Perplexity.

Named Entity Recognition (NER)

Extracts every brand, product, person, and concept that AI models associate with your target keyword. See which entities dominate the citation landscape and exactly where your brand sits.

Semantic Gap Analysis

Compares the entity and concept map of top-cited content against yours — surfacing the precise topics, terms, and relationships your pages are missing for effective AI search optimization.

LLM Content Optimization

Translates gap analysis into concrete content edits: which entities to mention, which questions to answer, and which structural signals to add — so your page earns AI citations on the very next LLM scan.

10+ Semantic Dimensions

Every signal that shapes LLM keyword authority.

Entity density scoreSemantic co-occurrenceCitation frequencyCompetitor entity coverageQuestion-answer alignmentTopical authority signalsBrand mention depthStructured data presenceConcept relationship graphLLM prompt match rate+ more

Scoring Dimensions

The 5 Meters Behind Every Score

GEO Bot runs five independent analyses on your content and the AI citation landscape. Each meter targets a different reason LLMs choose one page over another — together, they power true SEO GEO optimization.

NER

Named Entity Recognition (NER)

Identifies every brand, person, product, and concept in your content. LLMs build trust maps from entities — the more you share with top-cited pages, the stronger your citation case for that keyword.

Cosine

Cosine Similarity

Measures how semantically close your vocabulary is to the pages an LLM currently cites. A high cosine score means you're speaking the same conceptual language as citation winners — a core signal for both ChatGPT optimization and Perplexity optimization.

Monosematicity

Checks that each concept on your page carries one clear, unambiguous meaning. LLMs penalise vague phrasing because it raises hallucination risk. Precise, specific language earns more AI citations.

Schema

Structural Data Markup

Evaluates your schema.org and structured markup coverage. Machine-readable signals let LLMs verify your entities against external knowledge graphs — missing schema makes facts harder to confirm, directly hurting your AI search optimization scores.

Context

Contextual Relevance

Scores how tightly your overall narrative matches the intent behind the target keyword. Strong entity coverage fails if surrounding context drifts — relevance is the frame that makes your entities trustworthy for dual optimization.

Workflow

Enter Keyword → Analyse → Optimise → Rank

From a blank slate to a fully prioritised content plan for both SEO and GEO optimization — in under 30 seconds.

Enter a Keyword or Topic

Type any keyword, question, or topic you want to rank for in Google and LLM responses. GEO Bot runs a live sweep across major AI search platforms to see who is getting cited today — and why.

NER & Semantic Analysis

Our engine extracts every named entity and semantic concept LLMs associate with your keyword — building a full map of the citation landscape in under 30 seconds.

Get Your Difficulty Score + Content Gaps

See how hard the keyword is to win for AI citations (0–100), which entities your content is missing, and a ranked list of specific additions that move the needle fastest for dual optimization.

Optimise and Re-Check

Apply the content suggestions, re-run GEO Bot, and watch your citation rate climb. Track keyword difficulty trends over time to stay ahead of the competition — across both Google and AI search.

Dashboard

Four Views. Every Signal Covered.

From difficulty scores to actionable content edits — everything your content team needs to win AI citations and improve search engine ranking, all in one place.

Instant LLM Difficulty Scores Per Keyword

Each keyword gets a 0–100 difficulty score reflecting how competitive it is in LLM-generated responses today. Score factors include entity density among cited pages, semantic overlap with top citations, and live citation frequency across ChatGPT, Gemini, and Perplexity.

Real-time score from live LLM sweeps

Trend line: difficulty rising or falling

Bulk keyword upload via CSV

120+ Innovative Teams

Powering the next generation of marketing leaders with intelligent agents.

FAQ

Frequently Asked Questions

Deep answers to everything about SEO + GEO optimization, keyword difficulty, and LLM content strategy.

Traditional keyword difficulty tells you how hard it is to rank on Google. GEO Bot measures something different — how hard it is to earn AI citations for that keyword in LLM-generated responses. The score (0–100) is based on how densely competing pages cover relevant entities, how semantically tight the citation landscape is, and how frequently AI models already favour a dominant set of brands. It's your starting point for any serious SEO + GEO optimization strategy.

NER stands for Named Entity Recognition. It's the process of identifying every brand, person, product, and concept that AI models connect to a topic. LLMs don't rank pages the way Google does — they build entity maps and citation trust patterns. If the right entities aren't present in your content, you won't appear in AI-generated answers, regardless of your search engine ranking. NER analysis is the foundation of effective AI search optimization.

A score above 80 means the keyword is highly competitive in AI search responses. A small number of well-established brands dominate the citation landscape with deep entity coverage. Breaking in requires meaningful content restructuring — adding missing entities, improving structured data, and tightening semantic focus. It's comparable to targeting a high-difficulty keyword for search engine ranking, except the optimization levers are entirely different.

Yes. GEO Bot runs live sweeps across ChatGPT, Gemini, and Perplexity independently, surfacing keyword difficulty and citation data per model. Because different LLMs weight entities differently, content gaps can vary across platforms. This gives your team a granular, model-level view — so you can prioritise ChatGPT optimization or Perplexity optimization based on where your target audience is actually searching.

Start winning today

WIN

AI-POWERED

DISCOVERY

Join 500+ brands that track, analyze, and optimize their presence across every major AI model.

No credit card required · 14-day free trial

Know Which Keywords Help You Rank on Google
— and Get Cited by AI