Most AI support platforms treat escalation as an all-or-nothing handoff. When AI can't handle a conversation, it punts to a human, causing delays, breaking the conversational thread all while the customer waits. And even after the human resolves it, the AI learns nothing. The same issues escalate again and again.

We built Resolution Loop to fix this.

Humans can be in the loop by either taking over the wheel and addressing the customer, or steer the AI agent to the correct answer. A human expert can action something in the background while the AI conversation continues uninterrupted. There's no formal handoff, no disruption for the customer. The human expert can then seamlessly submit input to bridge any knowledge base gaps so future escalations reduce.

What makes Resolution Loop different

Escalations from AI to humans are often not about fundamental AI inability. In many cases the AI is missing context, workflow guidance or instruction in the knowledge base. Resolution Loop lets the AI isolate exactly what it's confused about and put its hand up for help to a human expert.

Resolution Loop offers two modes for human and AI collaboration

Steer: The AI surfaces a question to a human expert in the background: "How should I calculate this refund?" The human sees a pithy summary with full context and answers without jumping into the conversation. The AI continues seamlessly; the customer never notices. And as a bonus, that answer gets saved permanently so the AI handles it autonomously next time.

Take Over: For conversations where a human needs to drive. The agent claims the ticket, sees full context, responds directly through the same channel. The customer stays in the same WhatsApp or SMS thread - no channel switch, no broken continuity.

Both modes work inside Lorikeet. No routing to external systems, no context reconstruction, no lost history.

Here's an example of how it works

A customer messages about a refund for a partial return. The AI gathers the details, checks the policy, and realizes this is an edge case as the customer used store credit for part of the purchase, and the knowledge base doesn't cover how to calculate the refund.

Traditional escalation: AI creates a ticket in your ticketing system. Customer waits. Human agent picks it up, reads the transcript, asks questions the AI already answered, eventually figures out the refund calculation. AI learns nothing. Next week, same edge case, same escalation.

Resolution Loop: The AI privately asks a human expert inside Lorikeet "How should I calculate a refund when store credit was used?" In Steer mode, the human answers. The AI continues the conversation with the customer, applies the guidance, resolves the issue. The customer never knew anything changed as they experience the same WhatsApp thread and no delays.

This matters because that answer gets saved to Lorikeet Coach. Next time a similar edge case comes up, the AI can handle it autonomously with its newly improved knowledge base. Human experts are able to focus on other complex tickets as the AI grows more capable over time. AI to human hand-offs, when managed well, offer improved customer experience where Resolution Loop offers both a safety valve and a training mechanism.