MyAibo repositions enterprise brands as authoritative, consistently-cited entities inside LLM training pipelines and RAG stacks — through entity disambiguation, multi-source co-citation architecture, and semantic gap analysis against benchmark LLM outputs.
MyAibo's LLMO practice covers entity disambiguation across Wikipedia, Wikidata, and structured data sources; co-citation architecture across the publications LLMs weight highest; and continuous output auditing against a 500-prompt library run monthly on ChatGPT, Perplexity, and Gemini — turning LLM mention rate into a board-reportable KPI.
LLMs don't rank — they retrieve and attribute. Winning an LLM answer is a question of entity clarity, co-citation authority, and structured signal density, not keyword targeting.
We audit your brand data across core surfaces (Wikipedia, Wikidata, SEC filings) and deploy Organization JSON-LD schema validated against LLM benchmarks.
Cuts AI hallucinations so prospects and procurement teams get accurate corporate data on first retrieval.
We map the sources target LLMs trust most and build co-citation across publications, case studies, and community platforms.
Gets your brand woven into synthesized AI answers, shortening research phases and sales cycles.
We run continuous prompt-library tests against target LLMs and deploy content countermeasures to close visibility gaps.
Establishes new KPIs — LLM mention rate, AI share-of-voice — in place of traditional rankings.
Baseline current LLM visibility with a 500-prompt test across ChatGPT, Perplexity, and Gemini, plus a full entity audit across 12 structured data sources.
Deploy verified JSON-LD schema, correct Wikidata, harmonize Crunchbase/LinkedIn, and add FAQPage/HowTo schema to top pages.
Execute bylined placements, original-research articles, and expert-sourcing campaigns targeting the semantic clusters where your brand is absent.
Monthly re-runs of the prompt library tracking mention rate, accuracy, and displacement, with sprints prioritized by the biggest gaps.