search and reason over all your company knowledge.
Knowledge engine with RBAC-scoped search across structured + unstructured company data. Multi-agent research, report generation, background indexing. RLHF-tuned with external memory; background agents keep it current.
- Background indexingContinuously indexes Confluence, Notion, GDrive, GitHub/GitLab, Jira, Zendesk, ServiceNow, CW logs.
- Hybrid retrievalBM25 + dense vector + graph traversal. Re-ranker over candidate set.
- RBAC + tenant isolationPer-team CMK · row/col ACLs · SSO + identity center. Every query scoped to the user's permissions.
- External memoryMem0-style long-horizon memory across sessions and tools. Per-user, per-team, per-domain memory layers.
- RLHF feedback loopUser feedback → reward model → SLM fine-tuning. Closed-loop continuous improvement per team.
- Per-team distilled SLMSmall fine-tuned model per team or domain — $0.10/Mtok class, served on Inferentia.
- Agentic deep-researchMulti-step research subagent. Semantic search · SQL · code search · report generation tools.
Background agents continuously index every source. Pipeline runs through NER + semantic chunking. Indexed into semantic + lexical + graph search. RBAC + per-team CMK applied at query time. External memory layer remembers prior conversations. Agentic query orchestrator dispatches the right tool per question.
↓ background indexing
semantic + lexical + graph search
↓
rbac · external memory · rlhf loop
↓
agentic query · search · sql · code · report
core-banking client · l1/l2/l3 support
L1, L2, L3 engineers use KBot to resolve tickets faster, generate root-cause reports, and accelerate runbook authoring. Every query RBAC-scoped; nothing leaves the client VPC.
start with a diagnose.
Two to three weeks. Five to ten lakh. A written scorecard with topology recommendation, cost ranges, and remediation plan. No commitment to build.
request diagnose