Search Systems & RAG Architecture
Investigates how LLM search engines (Perplexity Sonar, Google Gemini, OpenAI Search) retrieve, rank, and cite web documents. Focuses on vector chunk extractability and context-window answerability.
CiteAura Research & Engineering Group
In strict alignment with Google Search Central's E-E-A-T quality and transparency standards, all technical guides, benchmarks, and diagnostic protocols published on CiteAura are engineered and vetted by the CiteAura Research Team—the multidisciplinary working group behind the platform's open-source GEO engine.
Rather than relying on unverified speculative claims, our publications are derived from deterministic software execution, large-scale multi-model prompt sampling, and direct analysis of search engine and AI crawler specifications.
Core Research & Engineering Disciplines
Crawler Standards & RFC 9309
Analyzes robots.txt longest-match precedence rules, edge cache invalidation, machine discovery via llms.txt, and HTTP user-agent negotiation across OpenAI, Anthropic, and Perplexity crawlers.
LLM Sampling & Evaluation
Develops reproducible prompt replay protocols, statistical variance controls, and deterministic mention-rate scoring across diverse model families and multilingual regional cohorts.
Entity Extraction & Schema Validation
Specializes in Schema.org JSON-LD graph construction, brand fact library synchronization, and structured entity disambiguation to prevent model hallucinations.
Our Engineering Principles
- Open-Source Engine Foundation: Our diagnostic framework is powered by an auditable Python engine with over 240+ deterministic unit tests, ensuring all findings are reproducible.
- Explicit Provenance: Every mention metric distinguishes between Parametric Model Knowledge, Live Grounded Web Retrieval, and Manual Product Surface sampling.
- No Speculative Ranking Guarantees: We report verifiable technical facts, crawl accessibility, and empirical citation presence—never fabricated ranking promises.