Rust-native indexing for AI data pipelines
CocoIndex is an open-source data transformation framework built in Rust — designed to construct and incrementally maintain high-performance indexes for AI and LLM applications without the overhead of full re-indexing pipelines.
Continuous indexing path
Index once. Stay current. Ship faster.
CocoIndex is an open-source data transformation and indexing framework designed from the ground up for AI workloads. Built in Rust, it gives developers the primitives to define data transformations declaratively and execute them incrementally — so indexes stay synchronized with source data without the cost of full rebuilds.
For AI and LLM applications, the freshness of the underlying index is directly correlated with the quality of the model's responses. CocoIndex eliminates the staleness window by treating indexing as a continuous, event-driven process rather than a periodic batch job.
The Rust runtime delivers predictable sub-millisecond overhead per transformation step — critical in agent pipelines where accumulated latency degrades user experience and system throughput.
Incremental Indexing
CocoIndex tracks which source records changed and only re-processes those records through the transformation pipeline — eliminating redundant compute and keeping index latency proportional to change volume, not dataset size.
Rust-native execution
Zero-overhead abstractions, memory safety without GC pauses, and a threading model optimized for concurrent transformation workloads — the same philosophy that drives LaserData's core engine.
RAG, Semantic Search, Agent Memory
Retrieval-augmented generation, vector search over live data, and persistent agent memory stores — all requiring indexes that never go stale and always reflect the current state of the world.
Why LaserData + CocoIndex
The combination creates a continuous, low-latency path from raw events to AI-ready indexes — at Rust speed, end to end.
LaserData as the Event Source
CocoIndex consumes change events from LaserData streams to keep AI indexes always current — reacting to new data in real time rather than running expensive full re-indexing jobs on a schedule.
Rust All the Way Down
Both LaserData and CocoIndex are built in Rust. The shared execution model means low overhead across the entire pipeline — from stream ingestion to index construction — with predictable tail latencies.
Incremental Index Maintenance
CocoIndex's incremental architecture pairs naturally with LaserData's streaming delivery — only changed records trigger re-indexing, so AI applications stay fresh without the latency spikes of batch rebuilds.
Agent-Ready Data Pipelines
Modern AI agents require up-to-date context to act reliably. The LaserData + CocoIndex stack gives agents a continuously refreshed index over live operational data — closing the loop between what happened and what the agent knows.
The AI data pipeline, end to end
Events stream from LaserData into CocoIndex, triggering incremental re-indexing — fresh context for every AI query, with zero batch windows.
Power your AI pipeline
Talk to us about connecting LaserData streams to CocoIndex for always-fresh AI indexes in production.