01
Live context
Resolve the account, opportunity, participants, and relevant prior history.
Applications / Tribble Scribe
The right answer, while the buyer is still talking.
Give a seller the right company answer while the customer is still talking, using approved account history, evidence, and response guidance.
- System
- Live intelligence
- Focus
- Approved context in the moment.
- State
- Low latency path
- Output
- Human controlled
APP-520Low latency path
A live assistant should use the same company knowledge and rules as every other application.
Scribe is a live work assistant we built on the Brain: answers, evidence, and next steps in real time during customer conversations, drawn from your company map instead of a generic model. We built it in about three months because the foundation already existed. It is both a working application and proof of how quickly a workflow can become software once the Brain is in place.
Scribe listens for a useful moment, finds the permitted account history and approved answer, and returns a short suggestion without exposing information the seller should not see.
The seller stays in control of what is shown or said. Reviewed interactions can improve the guidance available next time.
02
Approved answer
Attach current evidence and the owner of the underlying company claim.
03
Fast response
Choose an approved path designed to return guidance while the conversation is still happening.
04
Human presentation
Keep the person in control of what is shown, said, and preserved.
One customer question reaches the right answer while the conversation is still happening.
Account history, approved evidence, and presentation rules shape a short suggestion the seller controls.
Listen
Detect
Identify a relevant statement or question.
Brain
Resolve
Find the permitted context and approved answer.
Relay
Shape
Apply low-latency and response-length policy.
User
Present
Return a compact suggestion under human control.
Evaluate where live assistance can reuse approved company intelligence.
In one working session, we map the current applications, model spend, repeated work, and the first result worth proving. You keep the map either way.