From a delayed material to an approval-ready recovery plan.
This application family was built around a common supply chain problem: the first signal of a disruption may arrive in an inbox, while the operational effect is spread across supplier, material, production and customer records. The system uses several bounded agents to assemble that picture without giving software authority to make a commitment.
The business problem
A purchasing inbox showed the delay, but not the effect on production or customers. A manager had to assemble that picture before deciding what to do.
What changed in the workflow
Routing, impact-tracing, voice briefing and recovery agents prepare the context and options. A person approves any commitment.
What this example demonstrates
A working application family described without publishing private screens. This case explains the workflow and controls; it does not claim verified financial savings or production results.
A disruption becomes expensive while people are still assembling the picture.
The operating problem was not the inbox message by itself. It was the delay between the signal, the impact trace and an accountable recovery decision.
Purchasing requests and supplier updates entered through a shared inbox.
The consequence of a delay was not visible in one place.
Staff had to trace the exposure before they could compare recovery actions.
Any supplier, production or customer commitment still needed accountable approval.
Separate agents trace, brief and prepare. A person commits.
A realtime voice agent handles the spoken briefing. Routing and recovery agents collect evidence, trace impact and prepare options so a person can make the operational decision.
Routing agent
Reads incoming purchasing work, applies deterministic company rules first and sends only unresolved cases for constrained AI review.
Impact-tracing agent
Connects the supplier or material issue with relevant production and customer records, keeping the evidence attached to the briefing.
AI voice briefing agent
Lets the manager ask follow-up questions in a browser-based voice cockpit, with clear disclosure that the conversation is with AI.
Recovery agent
Compares available actions and compiles an approval-ready packet. It can prepare the choice, but it cannot make the commitment.
One visible path from spoken question to human review.
- 01Ask the AI voice agent about a delay or exposed commitment
- 02Trace the effect through materials, production and customers
- 03Compare recovery scenarios and purchasing actions
- 04Approve, edit or reject the action packet
A split runtime keeps the restricted desktop work separate from the voice cockpit.
PowerShell 5.1 with Outlook and Excel COM on managed Windows computers.
OpenAI Realtime API for voice and the OpenAI Responses API for bounded reasoning tasks.
WebRTC conversation in a browser interface with written evidence and action details.
Windows DPAPI for machine-level protection of sensitive local configuration.
Excel operational records and local structured files; the desktop MVP does not depend on a shared cloud database.
A browser-based voice cockpit deployed on Render, separate from the restricted desktop workflow.
Measure the preparation and control path, not a hypothetical saving.
From the first disruption signal to a usable view of affected work.
How long routine and exceptional purchasing requests wait for an owner.
Time and rework required to assemble options for approval.
Actions that reach review with their evidence, assumptions and accountable owner intact.
Start with the work, the constraint and the decision owner.
You do not need a project that matches this example. Bring the problem you want to solve, and we will assess the fit, compare possible approaches and agree on a useful first scope.
Discuss the problem