Which LLMs do you benchmark against?

ChatGPT, Perplexity, and Gemini are tested monthly as standard; other models can be added to the prompt library on request.

Do we need existing press coverage for this to work?

It helps, but the entity-audit phase is designed to find and use whatever independent coverage already exists — including sources you may not know are being indexed.

GEO · LLM Optimization Company

LLM Optimization Services That Make Your Brand the Answer, Not a Footnote

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.

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500

500-prompt library run monthly on ChatGPT, Perplexity, and Gemini

The problem

Why it breaks.

Why Is Your Brand Getting Cited by Competitors' LLM Responses Instead of Your Own?

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.

What we do

The work, in detail.

Entity Disambiguation & Knowledge Graph Alignment

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.

Co-Citation Architecture & Authority Network Engineering

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.

LLM Output Auditing & Competitive Mention Displacement

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.

Our framework

Four phases, week by week.

PHASE 01Weeks 1–2

Semantic Mapping & Entity Audit

Baseline current LLM visibility with a 500-prompt test across ChatGPT, Perplexity, and Gemini, plus a full entity audit across 12 structured data sources.

PHASE 02Weeks 3–5

Entity Alignment & Schema Deployment

Deploy verified JSON-LD schema, correct Wikidata, harmonize Crunchbase/LinkedIn, and add FAQPage/HowTo schema to top pages.

PHASE 03Weeks 6–10

Co-Citation & Authority Placement

Execute bylined placements, original-research articles, and expert-sourcing campaigns targeting the semantic clusters where your brand is absent.

PHASE 04Ongoing

Output Monitoring & Competitive Displacement

Monthly re-runs of the prompt library tracking mention rate, accuracy, and displacement, with sprints prioritized by the biggest gaps.

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