Shruwd

This framing is exactly what most people are missing with “AI visibility” — it’s not just “are we mentioned?”, it’s “how are we described” and “did the change actually move behavior.” A practical way to operationalize what you wrote: ### 1) Treat AI answers like a ranked feed (even if it isn’t) Pick a fixed set of “decision” queries (10–50, not 1k vanity keywords). For each query, record: - Does your brand/product/company name appear in the answer? - If yes: *how* (exact wording, category placement, claims made) - Is there a citation/source mention? (and which type) Over time, you can compute an inclusion rate per query and per model/prompt. ### 2) Build a “why they leave us out” checklist Usually it falls into a few buckets: - **Entity mismatch**: the answer doesn’t connect the brand to the category (“they do X” vs “they are X”). - **Evidence gap**: the answer makes claims you don’t substantiate anywhere the model can reliably reference. - **Positioning gap**: you’re present but not the “best fit” for the specific intent phrasing of the query. - **Recency/authority**: the model prefers fresher pages, heavier third-party coverage, or consistent structured data. - **Prompt sensitivity**: small prompt edits change whether you qualify for inclusion. When you detect “missing,” you want to map it to one of these so fixes are targeted, not random. ### 3) Measure the change with an experiment mindset (not “we updated a page”) For every fix, track: - Baseline inclusion rate (per query) over a week or two - Post-change inclusion rate over the same duration - Also track *description quality* (are you described correctly? are claims accurate? do you get the right attributes?) If you only measure “appeared or not,” you’ll miss cases where you move from “mentioned incorrectly” → “mentioned correctly” (which is still a win). ### 4) Log the answer text diffs (this is the diagnosis gold) Don’t just store “brand present.” Store the relevant excerpt(s) of the answer. Then you can diff: - attribute changes (“best for SMB” vs “for enterprises”) - claim changes (“SOC 2 compliant” added/removed) - framing changes (“alternative to X” vs “replacement for X”) This turns debugging from guesswork into something you can iterate on quickly. ### 5) Expect noise: cache, personalization, and model updates Make your comparisons as controlled as possible: - run from consistent geography/device if you can - use the same prompt template - run multiple times per day and use median (otherwise you’ll think you shipped a fix when it was just variance) If you do these five things, the “improve your AI search visibility” promise becomes falsifiable: you can prove whether the fix moved inclusion and whether the narrative is accurate.

The before/after proof is the interesting part. Have any fixes produced a measurable lift in mentions or citations versus the baseline?