LLM Retrieval Engine V4.3

Be Visible to AI.

Visibility OS shows how AI systems retrieve, interpret, and use your content — across RAG pipelines, answer engines, and generative search.

Chunking & Structure

Ensures your content is divided into retrieval-safe units that AI systems can understand and recall.

Embeddings & Entities

Analyzes semantic density, entity clarity, and contextual overlap inside vector space.

Retrieval → Answer Inclusion

Evaluates whether retrieved content is strong enough to be cited or synthesized in AI-generated responses.

Enterprise Retrieval Plans

Scale your Agentic Discovery presence.

Starter

$0 /mo
  • 5 Daily Scans
  • Basic Retrieval Audit
  • PDF Reports
RECOMMENDED

Professional

$49 /mo
  • 100 Daily Scans
  • DeepInfra RAG Analysis
  • Advanced Embedding Map
  • Priority Support

Enterprise

Custom
  • API Access
  • Custom Agent Training
  • Dedicated Success Manager

Frequently Asked Questions

Knowledge Base for Retrieval Intelligence

What is AI Visibility (Retrieval Optimization)?
AI Visibility refers to the likelihood of your content being successfully retrieved, interpreted, and synthesized by Large Language Models (LLMs) like ChatGPT, Claude, and Perplexity. Unlike traditional SEO which targets "clicks," Retrieval Optimization ensures your data is semantically structured to be "read" by vector databases and RAG pipelines as a primary source of truth.
How is this different from SEO?
Traditional SEO optimizes for keyword matching and blue links on a SERP. Retrieval & Visibility OS optimizes for Entity Salience (how distinct your brand/concept is to an AI) and Vector Density (how rich your semantic embeddings are). We solve for inclusion in the answer, not just ranking on the page.
Does this work for ChatGPT and RAG systems?
Yes. This platform uses the exact same retrieval logic—semantic chunking, embedding generation, and vector similarity search—that powers ChatGPT, Microsoft Copilot, and custom Enterprise RAG systems. We provide a mirror to how these "black box" systems perceive your digital footprint.
Why is my content ignored by AI?
LLMs often ignore content that is "low density" (fluff), lacks clear entity relationships (schema), or has poor chunking structure (text walls). Retrieval OS identifies these structural gaps and provides specific remediation steps to make your content "machine-readable" and highly citeable.