Streamkap
Streamkap is a managed platform that streams change-data-capture events from databases into destinations such as vector stores in real time so downstream embeddings stay in sync with the source.
Description
Streamkap connects databases, applications, and warehouses through change-data-capture and event streaming, moving data from source to destination with sub-50ms latency. For AI workloads it streams each insert, update, and delete from the source into a vector database, triggering an embedding update so retrieval data does not go stale. It processes only the rows that changed rather than re-embedding the whole dataset in batch.
Solution
Streamkap is a streaming-pipeline service, not an agent loop. It captures change events from a source database, optionally transforms them in flight, and delivers them to a sink. When the sink is a vector store, each captured change drives an embedding update, so the index served to a downstream agent or RAG system reflects the source as it changes.
Primary use cases
- real-time CDC pipelines from databases to vector stores
- keeping embeddings and RAG context current with source data
- streaming ETL into warehouses and applications
- incremental embedding updates for recommendation and agent context
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