Audit, track, and optimize your digital presence across AI engines like ChatGPT, Perplexity, Claude, and Google AI Overviews.
Overview
AEO GEO AI (also broadly leveraged through dedicated toolkits like GrackerAI and Arvow) is a next-generation generative search visibility platform designed for modern B2B SaaS, tech startups, and digital marketing agencies. The software systematically tracks whether your brand is being actively cited or omitted by frontier large language models, evaluates your algorithmic topical authority, and delivers structural recommendations to help content teams transform standard website real estate into highly extractable, non-commodity assets that AI systems trust and recommend.
Founded year:2026
Team size:2-10
Popularity:Gaining rapid adoption across technical sectors like cybersecurity and dev tools where buyers rely heavily on AI to research product stacks.
HQ:San Francisco, CA, USA
Status:Active
Funding status:Funded
Revenue source:Subscriptions
Customer type:B2B
Funding:Backed by strategic technology seed funds and software automation accelerators.
Pricing:Subscription
Tech stack:Next.js, Python, PostgreSQL, LangChain, OpenAI API, Anthropic Claude API
Platform:Web • API
Integrations:Google Search Console, HubSpot, Google Analytics 4, Slack, ChatGPT, Claude
Founder story
Conceived during the massive shift from classic list-based search interfaces to direct answer-engine interfaces, AEO GEO AI was built by an agile team of search infrastructure engineers and growth marketers. They recognized that standard keyword-stuffing methodologies were failing as LLMs routinely prioritized clear, well-structured comparison charts and expert first-party perspectives over legacy high-backlink domain footprints.
What it does
Monitors brand citation frequency and placement across major conversational AI platforms in real time
Calculates unified AI Visibility Scores to benchmark brand presence against industry competitors
Identifies contextual content gaps where LLMs draw from rival platforms to answer high-intent queries
Recommends structural, schema-level changes to increase probability of extraction by AI search crawlers
Tracks the transformation of traffic signals from direct featured snippet wins to down-funnel pipeline revenue stages
Who it's for
SEO Directors
Content Growth Managers
Product Marketing Leads
Digital Marketing Agencies
Brand PR Teams
Why it works
Algorithmic Tracking: Measuring mentions directly inside generative answers targets the real areas where modern buyer discovery occurs
Actionable Gaps: Revealing exactly which source documents competitors use to secure citations maps out clear structural blueprints
Non-Commodity Validation: Steering content engines toward deep first-party data structures mirrors the precise quality standards models require
Pipeline Attribution: Connecting AI search engine citation wins to CRM stages eliminates reliance on misleading superficial visibility metrics.
Growth strategies
Providing free personalized AI Search Visibility Audit snapshots to immediately reveal brand presence blind spots
Publishing comprehensive engineering white papers detailing the technical mechanics behind RAG extraction and query fan- out models
Offering heavily discounted product tiers to early- stage technology startups looking to gain a foothold in competitive fields
Building native Model Context Protocol (MCP) server connectors to allow marketing teams to pull live visibility metrics inside LLMs
Alternatives
Comparison overview
Focuses on deep generative visibility citation patterns instead of old keyword positions monitored by legacy search software
Delivers concrete text optimization instructions rather than simple abstract algorithmic scorecards
Tracks multi-model responses spanning Perplexity, Claude, and Gemini rather than tracking Google implementations exclusively
Integrates full lifecycle funnel indicators that map specific conversational mentions directly down to actual revenue pipelines