AI Visibility

Answer Engine Optimization, Done as a Service

We are an AEO and SEO agency for mid-market B2B. AI Visibility is whether your company gets named when a buyer asks an AI assistant who does what you do, and that answer usually names three or four companies. We find out whether you are one of them, why not, and what changes it. You get a report a person built and walks you through, not a dashboard and a login.

The Shortlist You Never See

A buyer asks an assistant who builds commerce for manufacturers, or who handles compliance for a company their size. They get a short list and a summary. That exchange never reaches your analytics, never appears as a session, and never shows up as a lost deal. Nothing in your reporting will tell you it happened.

Ranking does not settle it either. A page can sit in the top five organically and appear in none of the generated answers for the same question, because engines assemble answers from sources that state things outright. We have measured that pattern on our own domain. You cannot fix what nobody measured, and almost nobody is measuring this yet.

Measure It, Explain It, Fix It

  • Citation baseline: your real buyer questions run across four engines, recording who gets cited, how often, and in place of whom.
  • Cause analysis: absence has four causes needing different fixes: no page on the topic, a page engines cannot parse, a name collision with another company, or a source set you were never part of.
  • Structure and entity work: schema, structured answers, and a resolvable identity, so engines can tell who you are and quote you accurately.
  • Content built to be cited: specific, checkable, numbers-first pages, because engines cite sources that answer the question outright.

Baseline, Diagnose, Build, Re-measure

The sequence we ran on ourselves before we sold it to anyone. We agree the questions your buyers actually ask and run them across the engines; we name which of the four causes explains your absence; we build the structure, entity work and pages that answer directly, prioritised by what is winnable rather than by search volume; then we re-run the same battery on a cadence so movement is a number rather than a feeling.

Proof: We Ran It On Ourselves, Then On a Client

Katalor opened its doors in February 2026, so when we scored our own domain across our buyers' questions we were starting from almost nothing: near-zero citation on questions we already rank for organically, and another company holding our name in the answers. On a client's platform we watched the other side of it. When we rebuilt MyWhiteBoards, an OptiMA company, AI crawlers were not reading the old site at all. Within two days of launch, twenty engines were, and AI-referred visits have climbed every month since.

From 0 to 20 AI engines reading the client site within two days of launch; 23,506 client catalog pages taken in by AI crawlers; 90 to 165 AI-referred visits a month from June to August, counted from server logs and analytics, which agree within a few percent. Our own measurement battery runs four engines today. Read the MyWhiteBoards case study.

Start with a Free Evaluation: we run a short battery on your own buyer questions and show you what comes back before you commit to anything. No deck, no obligation.