Intro to Fibr AI

Fibr AI is an enterprise-grade platform that turns every URL into an intelligent agent, enabling real-time personalization, experimentation, and actionable insights across landing pages, campaigns, and audiences. By combining AI-driven recommendations with rigorous analytics, Fibr helps marketing teams optimize conversions, reduce friction, and accelerate campaign performance—all while meeting the highest standards of security, privacy, and compliance (SOC 2, ISO 27001, GDPR, CCPA, HIPAA).

Fibr bridges the gap between agility and assurance, empowering growth teams to act fast without compromising compliance or data integrity.

Why does Fibr exist?

Challenges	Reality	How Fibr solves it
Personalized upstream, static downstream	Campaigns know channel, intent, and cohort, but post-click websites do not adapt, and often fail to recognize non-human visitors.	Treats each URL as an autonomous, learning unit that personalizes experiences and adapts in real time to visitor context and traffic source.
No internal learning across pages	Traditional sites are closed systems where every page is an island with no cross-page intelligence or feedback loop.	Builds a continuous internal learning loop so URLs share insights, run experiments, and auto-apply winners to improve outcomes over time.
Not agent ready for LLMs and crawlers	Pages lack semantic structure, markup, and response logic, leading models to misread content and reduce visibility.	Optimizes structure and DOM and embeds page-level context to make URLs readable, referenceable, and better ranked by LLMs and crawlers.
No external awareness of campaigns or competition	Websites are blind to ads, cohorts, and competitive shifts, producing low-context outputs for high-intent traffic.	Connects analytics, ads, CDPs and CRMs, and privacy tools to build a context library powering adaptive personalization and testing.
Experimentation does not scale	Despite large spend, most enterprises run fewer than 12 tests per year due to silos and manual workflows.	Automates hypothesis generation, A/B testing, and implementation with multi-model significance to increase test velocity and impact.
Consumer and AI agents fail to complete tasks	Auth, CAPTCHAs, inconsistent flows, and non-actionable elements block agent-led actions.	Prepares URLs for agent-led interactions by optimizing schema and flows, improving task completion reliability for agents.