I want to stop losing money on supports that never came back.
An AI chat proposes a register, but it does not track real movements, delivery proof and expected credits.
Bruno tracks movements, prepares follow-ups and flags gaps to bill or regularize.
Deposits become visible before becoming silent loss.
Bruno makes this useful by connecting daily interruptions to durable follow-up: message, task, follow-up, approval and memory.
Mobilized foundations- Connected tools: calendar, email, spreadsheets, Drive, lightweight CRM or team messaging feed the file without retyping.
- Action memory: each request keeps its customer, context, received documents, follow-ups and next step.
- Human control: Bruno prepares, sorts and follows up, while sensitive messages and committing actions stay approved.
- Field gain: the goal is not to produce a long report, but to move a real request forward without retyping.
Apply the "Deposits, customers, carriers and credits tracked" use case in my company context.
- The priority deliverables are produced: Return register, Gaps to follow up, Expected credits, Delivery proof.
- Assumptions, risks and required human approvals are explicit.
- The result is reusable by the team, as in the Terroirs Frais, a regional food wholesaler example.
Info to clarify
- What is the exact context of your company, customer or project?
- Which documents, data or conversations can Bruno use as a starting point?
- Which deliverables do you want first, and which ones can wait?
- Who must approve the result before any external action is launched?
After three deliveries, eight pallets and several returnable crates are missing. The carrier says they picked them up, but the customer did not sign the matching note.
Bruno matches notes, returns and expected credits, then prepares customer and carrier follow-ups. The manager sees gaps worth billing or regularizing.
Created agents
- Deposits agent
- Transport agent
- Credits agent
Delivered assets
- Return register
- Gaps to follow up
- Expected credits