Zero-Downtime
Embedding Upgrades
Infrastructure to map old embedding spaces to new ones without re-indexing petabytes of vector data.
Bridging the gap between
model generations.
Upgrading embedding models in production vector databases typically necessitates re-encoding the entire corpus and rebuilding the index, leading to significant operational disruption and computational cost.
The Solution: Our product learns a high-fidelity mapping between embedding spaces using only a small sample of paired data. By mapping new queries into the legacy embedding space, it enables the continued use of existing indices.
Avoid Full Re-indexing
Defer the massive compute cost of re-embedding billions of vectors while instantly benefiting from new model capabilities.
Zero Downtime
Maintain service availability during upgrades. No need for dual-index maintenance or complex traffic shifting.
High Fidelity
Recover 95-99% of retrieval recall compared to a full native re-index using our specialized Residual MLP strategy.