Update README.md

Author: ponythewhite

README.md

@@ -35,16 +35,6 @@ Key competitive advantages of Cleora:
 * **quality of results outperforming or competitive** with other embedding frameworks like [PyTorch-BigGraph](https://ai.facebook.com/blog/open-sourcing-pytorch-biggraph-for-faster-embeddings-of-extremely-large-graphs/), GOSH, DeepWalk, LINE
 * can embed extremely large graphs & hypergraphs on a single machine
 
-**Cleora Enterprise** is now available for selected customers. Key improvements in addition to this open-source version:
-* performance optimizations: 10x faster embedding times
-* latest research: significantly improved embedding quality
-* new feature: item attributes support
-* new feature: multimodal fusion of multiple graphs, text and image embeddings
-* new feature: compressed embeddings in various formats (spherical, hyperbolic, sparse)
-
-For details contact us at cleora@synerise.com
-
-
 Embedding times - example:
 
 <table>
@@ -172,6 +162,16 @@ The technical properties described above imply good production-readiness of Cleo
 
 More information can be found in [the full documentation](https://cleora.readthedocs.io/).
 
+## Cleora Enterprise
+**Cleora Enterprise** is now available for selected customers. Key improvements in addition to this open-source version:
+* performance optimizations: 10x faster embedding times
+* latest research: significantly improved embedding quality
+* new feature: item attributes support
+* new feature: multimodal fusion of multiple graphs, text and image embeddings
+* new feature: compressed embeddings in various formats (spherical, hyperbolic, sparse)
+
+For details contact us at cleora@synerise.com
+
 ## Cite
 
 Please cite [our paper](https://arxiv.org/abs/2102.02302) (and the respective papers of the methods used) if you use this code in your own work: