Classer vs dmx

Production-grade models for query understanding, retrieval, and ranking, fine-tuned to your catalog, served at millisecond latency, integrated with your existing search...

dmx is an open source AI-native engineering harness. It runs as an MCP server inside Cursor, Claude Code, Copilot, or any IDE that speaks the Model Context Protocol, and...

About Classer

Production-grade models for query understanding, retrieval, and ranking, fine-tuned to your catalog, served at millisecond latency, integrated with your existing search infrastructure in days. Run the full pipeline or drop any one stage behind your existing stack. Route on what you extract: a part number sends the query straight to exact lexical lookup. Retrieve by meaning, not vocabulary, a query and a product can match with no words in common. Let retrieval cast a wide net, then put 27 billion parameters on reading the catch, ranking for purchase intent, not textual similarity.

About dmx

dmx is an open source AI-native engineering harness. It runs as an MCP server inside Cursor, Claude Code, Copilot, or any IDE that speaks the Model Context Protocol, and wraps your AI workflows in structured, verifiable loops. The model is fast and often impressive, but the output is inconsistent. Quality depends on how you prompted, whether you remembered to run tests, whether the spec was clear before the first line of code was written. There's just you and the model and whatever happened to work that day.