 
              Enables customers to find the products they're looking for more quickly, leading to higher satisfaction rates and increased sales.
High Precision and Recall. Outperforms other embedding models by a wide margin.
Really Fast, no GPU needed! Up to 180 embeddings per second on a single CPU core.
Flexible experience.Designed for Mobile, Tablet, and Desktop result pages.
Deploy in your stack with zero lock-in. Build your store on your terms, not someone else's expensive API.
Easily plug it in to your existing site-search with Mighty connectors!
Example for 10,000 query requests, using only one CPU core.
Add as many cores as needed for linear scalability!
Latency: Returns vectors in 8 milliseconds
Throughput: 136 queries per second
Summary:
  Total:  73.2846 secs
  Slowest:  0.0119 secs
  Fastest:  0.0071 secs
  Average:  0.0073 secs
  Requests/sec: 136.4544
  
  Total data: 619100001 bytes
  Size/request: 61910 bytes
Response time histogram:
  0.007 [1]   |
  0.008 [9358]|■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■
  0.008 [637] |■■■
  0.009 [2]   |
  0.009 [0]   |
  0.010 [0]   |
  0.010 [0]   |
  0.010 [0]   |
  0.011 [0]   |
  0.011 [1]   |
  0.012 [1]   |
Latency distribution:
  10% in 0.0073 secs
  25% in 0.0073 secs
  50% in 0.0073 secs
  75% in 0.0073 secs
  90% in 0.0074 secs
  95% in 0.0076 secs
  99% in 0.0078 secs
Details (average, fastest, slowest):
  DNS+dialup: 0.0000 secs, 0.0071 secs, 0.0119 secs
  DNS-lookup: 0.0000 secs, 0.0000 secs, 0.0005 secs
  req write:  0.0000 secs, 0.0000 secs, 0.0011 secs
  resp wait:  0.0073 secs, 0.0071 secs, 0.0109 secs
  resp read:  0.0000 secs, 0.0000 secs, 0.0007 secs
Status code distribution:
  [200] 10000 responses
            
           
            
          | Model | ndcg@1 | ndcg@4 | 
|---|---|---|
| max.io/ecommerce-encoder-v01 (Ours) | 0.77 | 0.73 | 
| sentence-transformers/all-MiniLM-L6-v2 | 0.68 | 0.65 | 
*nDCG for Amazon-Science ESCI ranking benchmark.
Bundle it with a Mighty cross-encoder for even better results!