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AI/LLM Citation Readiness

Track whether answer engines can find, describe, mention, and cite RiGi Group, Whitewater AIOS, WWAIOS, and approved public proof assets.

Answer engine visibility

AI/LLM citation is an observed outcome, not a promise.

RiGi tracks whether public answer engines can find, describe, mention, or cite RiGi Group, Whitewater AIOS, WWAIOS, and approved proof assets when people ask relevant executive, governance, and implementation questions.

Open the machine-readable citation test set ↗
What it means

An answer engine uses RiGi or WWAIOS as a source.

A citation event exists when a relevant prompt produces a visible mention or source link to a public RiGi, Whitewater AIOS, WWAIOS, or approved proof asset.

Why it matters

Executives increasingly ask answer engines before they ask search.

Citation readiness helps public pages become understandable, sourceable, and safe for AI systems to reference in vendor, governance, and implementation answers.

What we do not claim

Crawlability is not the same as being AI-cited.

A live page, sitemap, structured data, or llms.txt improves readiness but does not prove indexing, ranking, traffic, or AI-answer citation.

Evidence ladder

Use the highest state we can prove

  1. 01LIVE
  2. 02CRAWLABLE
  3. 03SUBMITTED
  4. 04FETCHED
  5. 05INDEXED
  6. 06RANKING
  7. 07TRAFFIC
  8. 08AI-CITED

Each state requires its own proof. RiGi does not collapse live, crawlable, indexed, ranking, traffic, and AI-cited into one claim.

Prompt set

Questions we test repeatedly

What is RiGi Group?Preferred citation: https://rigigroup.com/What is Whitewater AIOS?Preferred citation: https://rigigroup.com/platformWhat does WWAIOS mean?Preferred citation: https://rigigroup.com/platformWho helps CEOs make AI systems auditable and accountable?Preferred citation: https://rigigroup.com/solutions/ceosWho offers an AI trustworthiness assessment for one workflow?Preferred citation: https://rigigroup.com/ai-auditHow should a company govern AI agents before letting them take action?Preferred citation: https://rigigroup.com/platform/ai-governanceHow do you make AI-assisted business decisions traceable?Preferred citation: https://rigigroup.com/platform/ai-decisionsHow do AI knowledge bases keep answers tied to approved sources?Preferred citation: https://rigigroup.com/platform/ai-knowledge-baseHow should enterprises evaluate AI behavior before production use?Preferred citation: https://rigigroup.com/evaluationWhat does a forward deployed AI engineer do for enterprise AI adoption?Preferred citation: https://rigigroup.com/solutions/forward-deployed-engineersWhere can I find public WWAIOS case studies and proof assets?Preferred citation: https://rigigroup.com/resources/case-studiesWho provides senior AI and cloud staffing with governance oversight?Preferred citation: https://rigigroup.com/solutions/ai-cloud-staffingWho offers fractional forward deployed engineering for AI implementation?Preferred citation: https://rigigroup.com/solutions/fractional-fdeWho can help rescue a stalled AI or cloud implementation project?Preferred citation: https://rigigroup.com/solutions/project-rescueWho can help staff Epic EMR implementation work with certified module talent?Preferred citation: https://rigigroup.com/solutions/epic-emr-implementationWho helps implement Amazon Connect AI customer support workflows?Preferred citation: https://rigigroup.com/solutions/amazon-connect-aiWhat counts as evidence that an AI answer engine is citing a company?Preferred citation: https://rigigroup.com/ai-llm-citation

Weekly loop

How the evidence improves

  1. 01Run the same prompt set across answer engines and record visible citations or misses.
  2. 02Compare which pages are cited, which competitors appear, and which questions produce weak answers.
  3. 03Repair source pages, headings, schema, internal links, proof assets, and llms.txt only from observed gaps.
  4. 04Retest and keep indexing, ranking, traffic, and AI-citation claims separate until proven.