Evergreen Knowledge Nexus

The Problem

A support AI is only as accurate as its knowledge. In fast-moving businesses — where products, policies, and pricing change daily — static knowledge bases become stale within hours. The consequence: agents citing outdated policy, AI scoring interactions against rules that no longer apply, and compliance violations based on yesterday's handbook. The solution required a system that treated knowledge as a continuous stream, not a periodic batch.

Architecture

Event-Driven Webhook Architecture

Instead of scheduled crawling (which is slow and creates replication lag), the system subscribes to change events from the content sources — CMS, Confluence, Zendesk. Any edit by the 100+ knowledge editors triggers an incremental update to the vector embeddings within seconds.

• Change events are queued, deduplicated, and processed asynchronously — preventing update storms from degrading scoring latency.
• Each chunk is re-embedded on change, preserving chunk integrity and semantic coherence across updates.

Semantic Versioning for Knowledge

• Every knowledge chunk carries a valid_from and valid_to timestamp.
• QA scoring queries the vector store at the policy version active at the time of the interaction — not the current version — enabling retroactive audits to remain accurate even months after a policy change.

Multi-Tenant Namespace Isolation

• A Multi-Tenant Vector Index strategy ensures that a policy update in the India-Fintech namespace cannot bleed into UK-Retail scoring.
• Namespace boundaries are enforced at query time, not just ingestion time — preventing cross-market hallucinations even under schema drift.

Graph-Augmented RAG

• Beyond flat vector search, a Knowledge Graph maps the relationships between entities: Product A → Feature B → Legal Policy C.
• This allows the AI to understand the implications of a policy update — flagging downstream impacts automatically rather than requiring manual cross-referencing.
• An LLM-based Consistency Checker detects when a new editor's update contradicts an existing global policy, alerting the Principal QA Lead before the change goes live.

Cross-Lingual Alignment

Multilingual embeddings (Cohere Embed Multilingual, E5-multilingual) allow Hindi or Spanish conversations to be scored against English-language policies without maintaining parallel translated policy stores. A single source of truth, scored against in any language.

Results