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    Case Study

    +3,000% AI Visibility for a 35+ Year Digital Infrastructure FirmTurning expertise into inbound leads from LLMs

    Pyra used AI agents to package decades of expertise into search-ready, citation-friendly content that increased visibility across LLMs and drove higher-quality inbound leads—without relying on ad spend.

    Company profile
    35+ year digital infrastructure firm (anonymized); enterprise-grade clients
    Result
    +3,000% lift in AI/LLM visibility and discovery signals
    Why it mattered
    More qualified inbound conversations sourced from LLM-driven discovery

    Client Profile

    Industry: Digital Infrastructure
    Company type: 35+ year provider (anonymized)
    Market reality: Deep credibility in the real world, but weak visibility in search + AI discovery
    Constraint: Avoid naming clients publicly while still proving authority

    The Problem

    The firm had decades of delivery experience—real enterprise work, real outcomes—yet search engines and LLMs didn't "understand" that credibility. In AI-driven discovery, if the model can't cite your expertise, it won't recommend you.

    Their content footprint wasn't structured to win modern visibility: not enough topic coverage, not enough answer-friendly formatting, and not enough structured signals for LLMs.

    Why This Was Hard

    • ✗They couldn't name clients openly in most content
    • ✗The best expertise lived in people's heads—not on pages
    • ✗Most "AI content" is generic and doesn't earn trust or citations
    • ✗LLM visibility requires more than rankings—it requires structured authority

    The Pyra Approach

    Pyra used agents to extract expertise, structure it into buyer-intent pages, and publish content that is: skimmable, evidence-backed, internally linked, and formatted to be "answer-ready" for LLMs.

    1) Authority Mapping
    Identified core service themes, buyer questions, and the "missing pages" required to own the topic.
    2) Answer-First Content
    Built pages around real buyer questions with clean structure: definitions, FAQs, comparisons, and decision criteria.
    3) GEO Signals
    Increased citation-likelihood via proof blocks, structured FAQs, internal linking, and consistent terminology.
    4) Conversion Packaging
    Rebuilt content around outcomes: what changes, how fast, and what buyers can expect in a pilot.

    Results

    +3,000%
    Increase in AI/LLM visibility signals over ~90 days
    Frequent
    Discovery in LLM summaries for relevant buyer queries
    More inbound
    Conversations sourced from AI-driven research behavior
    Higher quality
    Lead mix compared to traditional search traffic

    Note: the company maintained client confidentiality while still publishing proof-grade authority signals.

    Why This Worked

    LLMs don't reward "marketing." They reward structured expertise. When your site answers the right questions with consistent terminology, clear proof points, and skimmable structure, you increase the model's confidence in citing you.

    Where This Applies

    This approach works for any organization with deep expertise but low AI visibility: B2B services, professional services, consulting firms, infrastructure providers, and specialized tech companies.

    Want the same visibility lift?

    Start with a free preview. We'll map the highest-ROI pages, build a proof-based plan, and show you what your agents would publish and automate.