Incremental engine for long-horizon agents.
Enterprise agents fail when their data lies. CocoIndex keeps codebases, meetings, Slack, docs & tickets continuously indexed — so production agents read the world as it is, not as it was yesterday.
The missing data layer for live AI context.
AI agents fail without a live connection to evolving data systems. Surveys cite data readiness as the #1 blocker for AI adoption — 46% of teams are blocked on integration, 42% on data access and quality.
data readiness
CocoIndex is an incremental engine for long-horizon agents.
Data transformation for any engineer, designed for AI workloads — with a smart incremental engine for always-fresh, explainable data.
Don't rebuild the compute engine.
Maintaining your own incremental compute engine with continuously updated sources normally takes 10–20 engineers at least 6 months, plus ongoing maintenance. CocoIndex ships it out of the box.
10–20 engineers. 6+ months.
CDC event handling, lineage tracking, schema evolution, stale-data cleanup, change propagation across joins and lookups, partial reprocessing, backfill — every piece you build yourself, then maintain forever.
A few lines of Python. Day-zero production.
Declare the transformation. The engine handles the delta, schema, lineage, retries, backfill, and parallelism — at any source or corpus scale.
Built for enterprise scale.
Incremental compute is the only way to keep large corpora fresh without re-embedding them every cycle. CocoIndex scales from a single repo to petabyte-scale stores — parallel by default, delta-only by design.
Process once. Reconcile forever.
When a source changes, CocoIndex identifies the affected records, propagates the change across joins and lookups, updates the target, and retires stale rows — without touching anything that didn't change.
Built on a Rust engine.
The core is Rust — production-grade from day zero. Parallel chunking, zero-copy transforms where possible, and failure isolation so one bad record doesn't stall the flow.
CocoIndex Code supercharges the coding agent for all your teams with a shared index.
Rebuild-per-developer works fine for one laptop repo. At enterprise scale — many repos, millions of files, hundreds of agents — every engineer re-embedding the same code burns compute and drifts out of sync. CocoIndex Code runs as a persistent daemon so the index is built once and served to the whole team.
Index once. Serve many.
A 100-engineer team re-embeds the repo once, not 100 times. The Rust daemon runs in your VPC; every MCP client, Claude session, and CLI call queries the same fresh index — one embed bill, one source of truth, no drift between laptops.
Cross-repo context.
Point the daemon at services, libraries, infra, and schemas together. Agents see callers in sister repos — blast radius is a query, not six GitHub tabs of spelunking.
Dedicated deployment.
VPC or on-prem with managed sync against private repos, SSO, and team-scoped indexes. Read the CocoIndex Code page or talk to us about your corpus.
One index. Every branch. Every PR, instantly in context.
A 10,000-engineer org runs thousands of branches in flight on any given day — feature work, release cuts, long-lived forks. Re-embedding the full corpus for each one is compute you can't afford and freshness you can't rely on. CocoIndex treats each branch as a delta layered on top of the shared main index: only the files that actually differ are re-chunked, re-embedded, and queried through an overlay.
Rebuild once, query from every branch.
Main is indexed once and served to the whole org. Every branch — feature, release, hotfix — reads the same base and layers only its own changed chunks on top.
Cost scales with delta, not with branches.
A typical PR touches a handful of files. That's all CocoIndex re-chunks and re-embeds. A thousand branches open simultaneously doesn't multiply your embedding bill — it just accumulates small deltas.
PR agents see the right code, every time.
Review agents query the branch as if it had its own full index. Under the hood, reads union the main base with the branch's delta — so blast-radius, call graphs, and vector search reflect the PR's actual state, not stale main.
Cleanup is automatic.
When a branch merges or closes, its delta retires with it. No orphaned vectors piling up in your store. Lineage is preserved — every chunk traces back to its branch + commit.
Production, on your terms.
On-prem & VPC
Deploy entirely inside your cloud. Data never leaves your perimeter.
SSO & RBAC
SAML / OIDC single sign-on with role-based access control on flows, sources, and targets.
Audit & lineage
Every record in the target is traceable to a source byte, code version, and timestamp.
Branch overlay
Every feature branch queries the shared main index plus its own delta — no per-branch re-embed, no stale PR context.
Custom integrations
First-party connectors for proprietary sources, sinks, and models. We write them with you.
Dedicated support
Direct channel to the engineering team. Response SLAs tuned to your production profile.
Roadmap influence
Prioritized input into the open-source roadmap and dedicated enterprise-only features.
Bring your own models
Plug in private embeddings, LLMs, and rerankers. Swap models per flow; keys stay in your KMS.
Let's ship live context for your agents.
Talk to us about your data, scale, and deployment model. We'll help you get from demo to production.