Real-Time Analytics
Sub-second dashboards and data products on petabytes of data at any concurrency
Data Warehousing
Sub-second analytics on open lakehouse formats with no vendor lock-in
Observability in the AI Era
The most cost-effective alternative to Elasticsearch observability
Context Engineering
Hybrid search and fresh context for RAG, agents, and LLMs
CocoIndex is a real-time data indexing framework that watches your sources, transforms records as they change, and writes them into a serving layer. Paired with VeloDB, you get an always-fresh index ready for SQL, search, and AI workloads — defined as code.
CocoIndex lets you describe an indexing pipeline declaratively: where the data comes from, how to transform it, and where it lands. The framework handles incremental updates, dependency tracking, and re-indexing so your downstream view stays in sync without batch reruns.
Define sources, transformations, and the VeloDB sink in a single declarative spec — version it, review it, and ship it like any other code.
CocoIndex tracks dependencies and only reprocesses what changed, so VeloDB always reflects the latest state without expensive full rebuilds.
Embed, chunk, enrich, and classify records in-pipeline before they land in VeloDB — ready for RAG, search, and agent workloads.
Whether the consumer is a SQL dashboard, a search UI, or an AI agent, they query the same VeloDB index — no separate stores to keep aligned.
Continuously index docs, tickets, and code into VeloDB with embeddings — give agents fresh, governed context to work from.
Combine vector and full-text indexes in VeloDB, kept up to date by CocoIndex pipelines, and serve them through a single SQL surface.
Treat VeloDB tables as continuously-maintained views over upstream sources — no scheduled jobs, no stale data.
Tell us about your stack and use case. Our team will help you stand up VeloDB with CocoIndex for your environment.