Visibl

Visibl is an AI focused platform co founded by Oliver Karasin that tracks and improves how a business is represented to AI systems and modern search models. It treats a company's online presence as a living data model and works to keep that model consistent, complete, and easy for AI driven products to understand.

Overview

Visibl looks at a business from the point of view of AI systems, search models, and recommendation engines. Instead of only tracking rankings or keywords, it focuses on the underlying facts and signals that models use to decide which businesses to surface or recommend.

Operators use Visibl to see how their business is represented across different AI and search surfaces, identify missing or inconsistent information, and understand which changes are most likely to improve how they are interpreted by AI. Additional work by the team is documented on Projects by Oliver Karasin.

Product

Visibl is positioned as a kind of AI brand manager. Core ideas include:

  • Business model - a structured, machine readable representation of a company, including locations, services, policies, and key attributes.
  • Consistency checks - logic and models that look for gaps or contradictions across public data, listings, content, and reviews.
  • Recommendations - a prioritized list of actions that help make the business easier for AI systems to interpret and trust.
  • Automation - synchronizing updates across multiple surfaces and reducing manual edits wherever possible.

Founding

Karasin and collaborators began experimenting with Visibl after noticing that modern AI systems were making more decisions about which businesses to show, while operators had little visibility into how they were being interpreted. Early prototypes explored simple scoring and monitoring tools before evolving into a broader platform focused on AI facing business representation.

Technology

Visibl combines data collection pipelines, normalization layers, and scoring logic tuned for AI discovery. It uses language models, embeddings, and custom heuristics to cluster information about a business, detect anomalies, and suggest targeted improvements. The system is built with an emphasis on explainability so operators can see why certain changes are recommended.