◈ Measured, not projected
Real sites. Real GEO-score movement.
Every number here is a before→after our own engine recorded — the fix was generated, shipped, and then proven by re-measurement. No projections, no cherry-picked vanity metrics.
Case 01 · B2B marketing site
From invisible to top-quartile — GEO 71 → 84
Strong technical bones (crawler 97, render 94), but two subscores were keeping it out of AI answers. We fixed exactly those — and re-measured.
71GEO
Before
→
84GEO
After
+13GEO points, one round of fixes
overall score, measured on re-crawl
overall score, measured on re-crawl
Subscore movement — where the points came from
Structured data 20%
38→88 +50
Entity clarity 10%
29→57 +28
Crawler access 30%
97→97 —
Renderability 15%
94→94 —
Extractability 25%
69→69 —
beforeafter
The fixes, generated and shipped: a rich SoftwareApplication schema node + a foundingDate on the Organization — no invented pricing, no fake ratings, just facts that were true. Entity groundwork toward real off-site profiles and dense
sameAs. On the next re-crawl the engine measured the result: schema 38 → 88, entity 29 → 57, overall 71 → 84.Case 02 · Independent content site
Already good content, now structured for AI — GEO 76 → 91
A site publishing strong material that wasn't structured for AI extraction. A round of schema, entity, and answer-first content fixes moved it into the top tier — measured before and after, the same way.
76GEO
Before
→
91GEO
After
+15GEO points
schema · entity · answer-first content
schema · entity · answer-first content
How both happened — the same loop every time: scan (find where the AI-visibility points are) → ship the generated fixes (on-site schema + entity, never fabricated facts) → re-measure and prove the realized change. No guarantees up front — the lift was measured after the work.
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