diff --git a/README.md b/README.md
index 9ee12a3..f7ad647 100644
--- a/README.md
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@@ -8,10 +8,6 @@ In kongruity, the artifacts become "sticky notes." A board full of them looks ch
 
 With a click, they are semantically evaluated, grouped into thematic clusters with descriptive headers, rankable and exportable to project planning and execution tools.
 
-## Voyage AI voyage-3.5
-
-![Embedding model benchmarking.](Voyage.jpg)
-
 ## Clustering and evaluation: methodology
 
 Two models run in parallel, and neither sees the other's work. Anthropic's `claude-sonnet-5` (`backend/services/clustering.service.js`) reads the raw text of every note and groups them into labeled thematic clusters. 
@@ -26,6 +22,10 @@ This yields an empirical groundedness evaluation. One model proposes the groupin
 
 Note that: before scoring, structural validation confirms that each note landed in exactly one cluster, that no cluster is empty, and that no hallucinated note IDs appear. A malformed response to the validation completely fails, rather than quietly returning a partial board.
 
+## Voyage AI voyage-3.5: best-in-class embedding
+
+![Embedding model benchmarking.](Voyage.jpg)
+
 ## Reading the cohesion score
 
 Average silhouette width is a widely-used measure of clustering quality. Higher values indicate: