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VisibAI
Check and fix your business visibility in AI answers.
What it does
Runs automated industry-specific queries across multiple major AI engines to evaluate brand mention frequency and citation share. Calculates a comprehensive 0-100 visibility score benchmarked directly against key competitors in the same market category. Generates actionable fix files including robots.txt configurations, llms.txt, and structured schema markup blocks. Delivers professional PDF reports and white-label insights for agencies managing multiple client portfolios.
Why it works
Freemium and flexible one-off audit pricing options eliminate mandatory recurring subscription barriers for low-frequency users. Comprehensive multi-engine tracking across six leading LLMs provides a holistic view of true AI brand presence. Actionable technical asset generation lets teams deploy fixes like schema and robots.txt on the same day. White-label agency positioning enables digital marketing firms to upsell AI search audits seamlessly.
Category: Marketing & Growth
Team Size: 1
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As of 2026
Marketing & Growth
Revenue: 0-$100k ARR
Check and fix your business visibility in AI answers.
Overview
VisibAI is an AI visibility platform that audits how brands appear across major conversational search engines like ChatGPT, Perplexity, Claude, and Gemini. It runs automated industry-specific queries to calculate a visibility score from 0 to 100, revealing competitor share-of-voice gaps. The platform delivers a prioritized fix list alongside ready-to-deploy technical files like robots.txt and schema markup. Small businesses, SaaS companies, and marketing agencies use it to master Generative Engine Optimization.
Funding status: Bootstrapped
Revenue source: Subscriptions
Customer type: B2B
Pricing: Freemium
Tech stack: Cloud infrastructure, LLM APIs, Next.js, TypeScript
Platform: Web
Integrations: ChatGPT, Perplexity, Claude, Gemini, Mistral, You.com
Founder story
VisibAI was founded in 2026 by Francesco Bianchi, based in Czechia. Drawing from extensive experience selling SaaS software across Europe, the founder observed a major shift in search behavior from Google toward conversational AI assistants. Recognizing that companies lacked visibility into whether AI tools recommended their products, he built VisibAI to make generative engine optimization measurable and actionable.
What it does
- Runs automated industry-specific queries across multiple major AI engines to evaluate brand mention frequency and citation share
- Calculates a comprehensive 0-100 visibility score benchmarked directly against key competitors in the same market category
- Generates actionable fix files including robots.txt configurations, llms.txt, and structured schema markup blocks
- Delivers professional PDF reports and white-label insights for agencies managing multiple client portfolios.
Who it's for
- Marketing Agencies
- SaaS Founders
- E-Commerce Brand Owners
- SEO Professionals
Why it works
- Freemium and flexible one-off audit pricing options eliminate mandatory recurring subscription barriers for low-frequency users
- Comprehensive multi-engine tracking across six leading LLMs provides a holistic view of true AI brand presence
- Actionable technical asset generation lets teams deploy fixes like schema and robots.txt on the same day
- White-label agency positioning enables digital marketing firms to upsell AI search audits seamlessly.
Growth strategies
- Product- led growth through free first-time AI visibility audits with no credit card required
- Agency channel partnerships utilizing white- label reporting features to drive multi-client adoption
- Targeted digital marketing addressing the shift from traditional search engines to generative AI discovery
- Community building and launch visibility across prominent startup discovery channels like Product Hunt
Alternatives
Comparison overview
- [VisibAI offers accessible one-off audits and multi-engine tracking across six conversational platforms without mandatory enterprise subscriptions n/][Alternative platforms like Semrush and Otterly.ai typically focus on subscription-only models with restricted platform coverage n/]