Customer stories & deployment resources

Raincurve is an in-network runtime layer that ingests your engineering history, builds a site-specific context graph and reasoning model, and intercepts architectural failures before they reach production — without replacing your existing observability stack or frontier models.

Observability tools are post-facto: they alert after latency spikes, errors, or outages have already impacted users. Raincurve operates pre-facto, cross-referencing proposed changes against your organization's topology, dependencies, and incident history before code ships.

No external data retention is required. Raincurve runs entirely in-network within your security perimeter and uses your existing models. Your documentation, graphs, and operational history stay inside your environment.

Most organizations go from first call to in-network deployment in under 14 days. The engine maps to your specific topology, internal tool schemas, and ingestion boundaries during that window.

Raincurve shows which context is being used for each evaluation — which graph nodes, retrieval paths, memory slots, and dependency edges informed a given prediction. Teams get auditable reasoning, not opaque model output.

Raincurve is model-agnostic. It orchestrates context into your existing frontier models via MCP-exposed knowledge graphs, GraphRAG, and pruning layers — tailoring inference to your company without forcing a model swap.