A knowledge engine with RBAC-scoped search across your structured and unstructured data. Multi-agent research, report generation, background indexing — RLHF-tuned with external memory, kept current by background agents.
Background agents continuously index every source. The pipeline runs through NER + semantic chunking, indexed into semantic + lexical + graph search. RBAC + per-team CMK are applied at query time. An external memory layer remembers prior conversations. An agentic query orchestrator dispatches the right tool per question.
confluence · github · jira · drive · cw logs
↓ background indexing
semantic + lexical + graph search
↓
rbac · external memory · rlhf loop
↓
agentic query · search · sql · code · report
L1, L2, and L3 engineers use Nous to resolve tickets faster, generate root-cause reports, and accelerate runbook authoring. Every query is RBAC-scoped; nothing leaves the client VPC.
See customers →Two to three weeks. $6K–$12K. A written scorecard with topology recommendation, cost ranges, and remediation plan. No commitment to build.