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Operations software · AWS

One workspace for deliveries that need attention.

A platform designed for dispatch teams, drivers, and operations managers. The system brings together shipment tracking, route visibility, proof of delivery, exception handling, and reporting.

A possible implementation uses Next.js · NestJS · Python ML service · PostgreSQL. Illustrative example, not a client engagement.

Sample workspace · Logistics
2
On schedule
1
At risk
1
Delayed
0
Resolved

Select a delivery to open its exception panel.

Who it serves

  • ▸Dispatchers coordinating live routes
  • ▸Drivers updating stops and uploading proof of delivery
  • ▸Operations managers reviewing patterns and performance

The operational challenge

Updates arrive from drivers, customers, and tracking feeds in separate places. A dispatcher spends the morning stitching them together, and a late shipment is often noticed after it matters.

A guided product journey

  1. 01

    Too many disconnected updates

    The operations overview consolidates active deliveries, delayed shipments, and open exceptions.

  2. 02

    One view of routes and exceptions

    The route board shows a map, route list, filters, assigned drivers, and stop sequence together.

  3. 03

    An at-risk shipment, with context

    The shipment record shows its status timeline, customer instructions, documents, and predicted arrival.

  4. 04

    Evidence reviewed, response assigned

    An exception drawer opens without losing route context, with ownership, priority, notes, and a next action.

  5. 05

    The decision is recorded

    Resolution history feeds analytics on delay patterns and exception categories.

Capabilities

Operations overview

Active deliveries, delayed shipments, exceptions, regional performance

Route board

Map, route list, filters, assigned drivers, stop sequence

Shipment record

Status timeline, customer instructions, documents, predicted arrival

Exception inbox

Ownership, priority, notes, next action, resolution history

Driver view

Assigned stops, status updates, proof-of-delivery uploads

Analytics

Delay patterns, exception categories, delivery trends

Administration

Users, roles, service regions, alert rules

Applied AI

  • ▸ETA prediction from historical delivery and route signals.
  • ▸Exception summaries that bring recent events into a short dispatcher briefing.
  • ▸Image triage that flags a proof-of-delivery photo for human review when it may show a problem.

An AI flag is a review prompt and an ETA is a prediction. The person on the team makes the operational decision.

How the system is designed

Experience

  • Next.js frontend

Services

  • NestJS APIs
  • Python ETA service
  • LLM summary integration

Data

  • PostgreSQL on Amazon RDS
  • Amazon S3 (delivery files)
  • Background queue
  • ▸Prediction runs as its own Python service so models can change without touching the core API.
  • ▸Background processing keeps uploads and summaries off the dispatcher's critical path.
  • ▸Containerized services; hosting chosen for the actual workload during scoping.

Where people stay in control

  • ▸Every AI output is labelled and can be dismissed
  • ▸Role-based access by region and function
  • ▸Event history and owner on every detail page

Building an operations platform? Let's talk.

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