diff --git a/README.md b/README.md
index d38c103..a482814 100644
--- a/README.md
+++ b/README.md
@@ -8,11 +8,15 @@ 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. 
 
-At the same time, Voyage AI's voyage-3 model (`backend/services/embedding.service.js`) converts each note's text into a numeric representation of its semantic meaning aka vector. 
+At the same time, Voyage AI's voyage-3.5 model (`backend/services/embedding.service.js`) converts each note's text into a numeric representation of its semantic meaning aka vector. 
 
 Once the LLM returns, kongruity scores that grouping (`backend/services/validation.service.js`) using an established silhouette coefficient, with cosine distance rather than Euclidean as the proximity metric. 
 
diff --git a/Voyage.jpg b/Voyage.jpg
new file mode 100644
index 0000000..6b85176
Binary files /dev/null and b/Voyage.jpg differ
diff --git a/backend/services/embedding.service.js b/backend/services/embedding.service.js
index e41cb36..51a7bba 100644
--- a/backend/services/embedding.service.js
+++ b/backend/services/embedding.service.js
@@ -27,7 +27,7 @@ export const embedNotes = async (notes) => {
             for await (const chunk of batches) {
                 const response = await client.embed({
                     input: chunk.map((n) => n.text),
-                    model: "voyage-3",
+                    model: "voyage-3.5",
                 });
 
                 response.data.forEach((item, i) => {