Research
Raincurve sits on top of your existing stack — combining context graphs, reasoning layers, and runtime memory so AI understands your organization, not just your prompts.
The research areas below inform how Raincurve builds metacognitive, context-aware runtime engines. Each is a “slap on top” primitive: no rip-and-replace, no sending proprietary topology outside your perimeter.
01
Instead of flat vector chunks, Raincurve models entities and relationships for multi-hop queries — answering which customers were impacted by services that depend on X, or what root causes recur across incidents. Graph-augmented retrieval dramatically outperforms plain RAG on dependency-aware questions where text co-occurrence is not enough.
02
Structural dependencies — imports, inheritance, instantiation, service edges — are extracted into queryable graphs and exposed to agents at task time through MCP. Index-first, agentic search, and pre-computed context architectures all converge on the same goal: agents that understand how your code and infrastructure actually connect.
03
A third path between RAG and full LoRA: memory layers with millions of slots where only top-activated entries update per new fact. Customer-specific knowledge can be absorbed permanently with far less catastrophic forgetting than traditional finetuning approaches.
04
Doc-to-LoRA and Text-to-LoRA generate adapters directly from documentation or task descriptions. Continual-learning constraints prevent new updates from overwriting prior customer adapters — enabling stack-specific tailoring without repeated gradient runs.
05
A dedicated neural long-term memory module sits alongside attention: short-term context in the window, persistent organizational knowledge in memory. Years of operational history can be encoded for deep test-time reasoning without blowing context windows or token budgets.
06
Parametric memory in weights is permanent muscle memory; latent, injected memory is an ephemeral snapshot per task. Bolt-on memory systems accumulate org-specific knowledge across sessions — the layer Raincurve uses to bridge stateless agents and site-specific runtime behavior.
07
Org-specific conventions for AI agents maintained as living artifacts — detecting drift between what agents produce and what your standards require. Tailoring as maintained config, not one-off retrieval, keeps runtime behavior aligned as the stack evolves.
08
Kernel-level observation of syscalls, network events, and process behavior with no instrumentation changes. Agents receive causally grounded runtime context instead of lagged logs — the agentless wedge for understanding what actually happened at execution time.
09
Simulation models packaged as software that run alongside operations, using live data to predict upcoming failures and edge cases beyond as-designed behavior. Event-driven loops update twin state, estimate failure probability, and trigger action before production impact.
10
Learned simulators of your system used to evaluate behavior before deployment — the same primitive as robotics world models, applied to software runtime. Policy and change evaluation compress from hours of live testing to minutes of simulation-grounded reasoning.
11
Surgical encoding of specific facts directly into model weights without full fine-tuning — e.g., injecting that your payment service retries three times then dead-letters. Parametric facts complement graph retrieval for stable, high-confidence organizational rules.
These research primitives compose into Raincurve's production pipeline. See how they work together in Technology →