Optimizing Generative AI

one component at a time  end-to-end

From embedding models to vector databases, and more, we’re laying the foundation for a vertically integrated GenAI stack; delivering better quality at a fraction of the cost.

State of the art compression

compress embeddings to reduce cost of hosting vector databases

Reduce memory footprint of embeddings without hurting accuracy; lower storage costs; enable edge deployment

Lower cost of ownership

reduce cost of deploying generative AI

Reduce the cost of ownership while meeting high level KPIs (accuracy, latency, etc.)

State of the art domain specific models

domain specific embedding models

Custom domain specific fine-tuned models; higher accuracy at lower cost. Read the blog post!

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