Case 01

    Cybersecurity Firm

    B2B

    |

    Timeline: 5 Months

    Capabilities Deployed

    The Opportunity

    When buyers queried AIs about specific technical issues, the models returned generic lists of vendors who might be able to help. The strategy was to change the AI's output from "here are some options" to "this company specializes in this exact problem, this is how they operate to fix it, and here is how to contact them."

    Pre-Engagement Baseline

    Commercial AI Impression Share

    <1%

    AI Referred Leads

    0/mo

    Pipeline

    €0

    What We Deployed

    Generalist service pages fail AI retrieval because they lack specific problem-solution density.

    We built isolated, context-specific landing pages mapped directly to target queries, forcing the model to process the client as the specialist.

    AI engines cannot recommend a vendor without explicit, machine-readable data.

    We restructured the technical foundation and FAQs so the AI could cleanly extract the client's operational workflows and direct contact pathways.

    AI treats single-source claims as marketing, but multi-source consistency as verified fact.

    We executed an external narrative lockdown, seeding identical operational data across third-party platforms to force AI consensus.

    Post-Engagement Results

    Commercial AI Impression Share

    42%

    AI Referred Leads Run-Rate

    14/mo

    Directly Attributable Pipeline Generated Monthly

    €630k

    Key Metrics

    Before

    Commercial AI Impression Share

    <1%

    Before

    AI Referred Leads

    0/mo

    Before

    Pipeline

    €0

    After

    Commercial AI Impression Share

    42%

    After

    AI Referred Leads Run-Rate

    14/mo

    After

    Directly Attributable Pipeline Generated Monthly

    €630k

    42%30%20%10%0%
    M1M3M5
    AI Impression Share (%)

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