Active shipments past their promise
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Sector AI 02 / Logistics · RelayPoint AI
Deliveries move.
Your decisions should keep up.
An operations agent that connects shipment records, delivery exceptions, dispatch capacity and customer communication. See what needs attention, inspect the evidence and prepare a controlled next step.
The starting operating picture · fictional demo baseline
Future deliveries with ETA risk
Delivered shipments missing POD
On time · 21 of 24 delivered
48 records = 20 active + 24 delivered + 4 cancelled. On-time performance excludes active and cancelled shipments. These are fictional starting conditions, not improvements achieved for a client.
The operating challenge
A late delivery rarely belongs to just one team.
Dispatch sees vehicle availability. Customer service sees a missed promise. Management sees the daily delivery number. The useful question is which shipment needs attention, what its record actually says and which next step the right person can approve.
Demonstration scope: A fictional operator serving Lagos and selected interstate lanes. The system compares recorded schedules and shipment states. It does not provide live GPS, traffic prediction, automated route optimisation or a guaranteed arrival time.
Interactive operating desk
Choose the exception.
Follow it to the shipment.
Explore the frozen starting records below. Queue filters change the records shown, while the headline figures remain the full baseline. Open the live agent to ask broader questions, draft updates and simulate an approved allocation.
Loading the operating records…
| Shipment | Route | Status | Promise · WAT | Recorded ETA · WAT | Exception | Inspect |
|---|
Shipment record
Select Inspect to follow a shipment.
The record will show the reported handling note, proof of delivery and the next question the operating team should investigate.
Overdue = active and promise before the fixed clock. At risk = active, promise has not passed, and recorded ETA is later than the promise. Missing POD = delivered with no proof-of-delivery reference. A recorded handling note is evidence to investigate, not a verified causal finding.
Guided tool preview
From a management question to a usable operating answer.
These prepared examples use the same tools and baseline as the live agent. The website preview is deterministic. Live AI selects approved tools against your private demo session.
Prepared tool answer · fictional records
What needs management attention?
Loading the verified example…
Why it matters
Connect the signal to the next decision.
Inspect tool evidence
No language model runs in this prepared preview. Customer updates remain drafts. Open the live agent for conversational tool use and a human-confirmed action lab.
Controlled actions
An allocation should survive a check before it becomes an action.
Check the proposed pairing
The dispatch tool checks shipment status, lane, maintenance status and remaining vehicle weight. Proposing an allocation changes no record.
Keep a person in control
Only a dispatch or operations-manager role can allocate. The Action Lab requires an explicit review checkbox; the AI has no commit tool.
Commit with a trace
The tool rechecks capacity and rejects stale proposals. A successful simulated allocation updates the session and records the actor, revision and before/after state.
Try it: Open RelayPoint AI → Action Lab. Prepare RLY-0038 with VEH-01, review the proposal and confirm the simulated allocation. Inspect the audit trail. No real vehicle is dispatched and no customer message is sent.
Business value
Less hunting for context.
Clearer next steps.
The demonstration brings together the records people already use: delivery promises, shipment statuses, exception notes, vehicle availability and proof of delivery. The opportunity is a more consistent recovery queue, better-informed customer updates and fewer invalid allocations.
A client implementation would connect approved dispatch and service systems, validate data quality and measure results against a baseline. It would add production identity, operating permissions, driver-hours, vehicle volume, route constraints and integration monitoring where required.
Discuss your logistics operation ↗Evidence and boundaries
Built to be inspected.
RelayPoint Logistics is fictional. The reproducible fixture has 48 shipments and five vehicle records. Recorded ETA risk is a schedule comparison, not a machine-learning forecast. Weight and lane eligibility are partial dispatch checks, not a complete route or fleet optimisation model.
Demo roles show tool-level access controls. Role switching is not production authentication. Session resets discard simulated allocations; the website preview stays fixed. Customer messages are drafts and the model cannot commit an allocation.
Agentic AI for logistics operations
Make the next operating decision easier to act on.
Start with the workflow, records and decisions that matter to your team. ONE Light Analytics can build an AI operating layer around them.