A practice built around one question.
How do AI systems find, describe, and recommend you — and how do you improve your standing? Everything here is built to answer that with evidence, not opinion.
How the work gets done.
Across ChatGPT, Gemini, Perplexity, and Claude, not a single tool's export — a rigorous analysis you can defend.
The public material the models actually lean on when they describe you.
How you stand against a validated competitive set, measured rather than guessed.
Judged the way a real Wikipedia reviewer would — before you spend anything on a draft.
Calibrated judgment you can act on: evidence first, opinion second.
Brandon Kahn
Buyers don't just Google you anymore — they ask ChatGPT, Gemini, Perplexity, and Claude, and those answers are deciding deals before you ever hear about them. Most companies have never seen what AI says about them. I close that blind spot: I test the engines the way your buyers use them, trace which sources they trust, and hand you evidence you can act on — directly, with no layers in between.
Every engagement is hands-on: multi-engine prompt testing, source and citation analysis, and research into how AI systems retrieve, describe, and compare companies.
Connect on LinkedIn →No one can guarantee what a third-party model says — anyone promising rankings is guessing. What you get is rigorous analysis, objective recommendations, and a plan that measurably improves your position.
For Wikipedia, the assessment comes before any draft. If the evidence to qualify isn't there yet, I'll say so plainly, and explain what would change it.
I diagnose, assess, and write; your team or vendors publish and implement. The work complements your existing partners rather than competing with them.
If that's how you like to work, let's talk.
A short intro call is the fastest way to see whether it's a fit. No prep, no pressure.
Book an intro call