Every decision an agent makes draws on two separate sources, and both are retrieved before it says a word. The knowledge base is what your business knows: policies, products, pricing rules, scoped per company, number or agent and ranked by priority. Vector memory is what has actually happened with this person: previous interactions, promises made, notes your staff added. Ask about the delivery problem last month and it finds the right conversation, whether or not you use the words that were used at the time.
Scoping and priority are what stop a knowledge base becoming a soup.
Company-wide, or narrowed to one phone number, one chat widget or one agent. Different front doors, different material.
The critical items surface first rather than competing equally with everything else you have ever written.
Semantic retrieval rather than keyword matching, so a customer asking in their own words still finds it.
Articles, FAQs, policies, product guides and troubleshooting kept as distinct kinds rather than one pile.
Searched by meaning across a vector database, and attached to the contact rather than the call.
Previous interactions, promises made and outstanding issues, carried across channels and across months rather than reset at the end of each call.
Notes a person writes on a contact are read by the agent alongside the automatic summaries. Human and machine keep one record, not two.
Both sources are yours to edit. Fix the document or the note and the next conversation is different. No model tuning, no waiting on us.