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AI Visibility for a Leading Real-Estate Investment Firm

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Client

A leading institutional real-estate investment and development firm specializing in a specific high-value asset class. The company relies heavily on data, research, and underwriting models to make smarter acquisitions, accelerate valuations, and improve operational performance across its portfolio.

Challenge

Despite strong traditional SEO performance, the client had near-zero visibility inside AI models. Their research pages, market insights, and asset-class expertise were being crawled by LLMs but:

  • Not recognized as structured or authoritative
  • Not linked to clear entities, locations, or asset categories
  • Not cited or surfaced in ChatGPT, Gemini, Copilot, or Perplexity answers
  • Inconsistent terminology and fragmented content diluted trust signals

Result: their teams produced high-quality analysis, but AI systems couldn’t interpret or prioritize it, leading to lost reach and competitive disadvantage in AI-driven discovery.

What We Did

We deployed a combined AEO/GEO + data engineering solution focused on structure, entities, and machine readability:

1. Entity-first content architecture

  • Mapped all core entities: asset class → subtypes → markets → investment criteria
  • Unified terminology across research, insights, and portfolio pages
  • Eliminated contradictions and ambiguities that confuse LLMs

2. Schema & structured markup

  • Implemented entity-rich schema across 40+ high-value pages
  • Added machine-readable metadata for properties, markets, amenities, valuations, and risk factors
  • Built a consistent taxonomy and knowledge graph alignment

3. Crawlability & content engineering

  • Rebuilt critical pages for clarity, hierarchy, and semantic structure
  • Removed indexing blockers, duplicated structures, and outdated templates
  • Optimized internal linkage for machine understanding, not just human UX

4. AI Visibility Data Pipeline

  • Built automated checks for entity coverage, schema integrity, and AI-readiness
  • Deployed a monitoring system to track content ingestion across LLM crawlers

Results (within 60 days)

AI Visibility

  • From 0–2% visibility in AI answers → up to 28–34% inclusion in responses for queries related to their asset class
  • Their market insights began appearing as cited or paraphrased sources in ChatGPT and Perplexity
  • LLMs shifted from generic public data to their definitions, frameworks, and terminology

Search Performance

  • +40% growth in impressions for entity-rich pages
  • +24% increase in organic visits to high-value research content
  • Dramatic improvement in long-tail queries tied to asset-class expertise

Knowledge Authority

  • LLMs began treating the firm’s content as a primary point of truth in their niche category
  • Internal teams reported significantly better AI-powered research outputs due to improved model understanding of their materials
  • The firm gained a competitive visibility advantage, as competitors lacked structured, entity-aligned content

If you’d like to understand how your own website can become a trusted, AI-visible knowledge source across ChatGPT, Gemini, Copilot, and Perplexity, we’ll walk you through your current visibility, structural gaps, and fastest improvement opportunities during our free assessment call.

Book Your AI Visibility Assessment

The project was done in 2025

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