When a teammate resolves a conversation the AI could not, Waaru captures that resolution for review and can use the approved answer as context for a similar future intent. Your team can reduce repeated escalations without manual retraining or fine-tuning.
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Follow one escalation from the missing answer to a teammate's resolution, then into the workspace knowledge used for a similar future request.
Fabric care question
Agent asks a teammate because no approved answer exists.
Point the AI at your website, upload PDFs, paste FAQs, or sync your Shopify catalog. After indexing and review, the Company Brain can provide approved context for pricing, policies, product specifications, and business hours.
When the AI escalates and a human resolves the conversation, the resolution is captured, classified by intent, and indexed in the workspace's resolution store. The next time the same intent appears, the AI checks both the static Brain and the resolution store before replying.
The underlying model is not fine-tuned - resolutions become retrieval-augmented context. No training run to schedule, no drift to monitor, instant rollback if a resolution turns out wrong. Workspace-isolated; resolutions never train shared models.
Track which resolved intents return to a person and which are handled from approved context. Improvement should appear as fewer repeated escalations for the same question, not as an invented benchmark.
No. Resolutions become retrieval-augmented context for the AI, not weights in a model. This means instant updates, instant rollback, and workspace isolation. No training run.
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