AI Overview

Ticket0 uses a hybrid AI pipeline designed for low cost, good accuracy, and safe handoff.

High-level flow

When a new ticket or chat message arrives, the system runs this sequence:

1. Fast understanding (Gemini Flash / Haiku-class tasks)
2. Python context assembly (knowledge base, customer context, similar tickets, language)
3. Final response generation (Sonnet-quality drafting)
4. Confidence scoring + action decision (show draft, human review, or human required)

What happens on inbound ticket email

1. Ticket is created and classified (category, intent, urgency, confidence).
2. AI tool calls gather context from KB and prior conversations.
3. The agent drafts a response using workspace persona + learned team voice patterns.
4. A confidence score is computed from retrieval quality, classification confidence, sentiment, topic risk, and pattern matches.
5. Depending on thresholds, the draft is shown for one-click send or suppressed for full human review.

What happens on chat widget messages

1. Customer message is stored in the conversation.
2. AI generates a response or triggers escalation when confidence is low.
3. If escalation is needed, the user is offered escalation in-chat.
4. If confirmed, a support ticket is created/updated and queued for a human operator.

Design goals

• Keep routine responses fast and low-cost
• Keep high-risk conversations in human hands
• Be explicit when confidence is low
• Use operator feedback as the learning signal over time

Continue with Zero-config learning and What the AI will and won't do.