The client: a US logistics technology company
Our client is a US logistics technology company that connects shippers with many parcel carriers across the United States, so a shipper can reach the whole country through one platform instead of managing every carrier separately. Each parcel that moves through the network creates data at every step: the shipper's manifest, pickup and scan events from each carrier, handovers between first, middle and last mile, proof of delivery, and finally a carrier invoice. The client brought TechTose in to help build the platform that turns all of that into one picture, and to make AI a working part of daily logistics operations.
Why parcel data across many carriers is hard to trust
Parcel networks are messy by nature. Every carrier has its own systems, its own tracking codes and its own invoice format, and a single parcel can pass through several carriers before it reaches the door. With that information scattered, operations teams spent their day stitching together the truth: where a parcel really is, why it's late, which carrier is slipping, and whether an invoice matches what was actually delivered. Problems surfaced only after a delivery date had already been missed, and finance teams reconciled carrier invoices by hand.
What we built: an AI logistics platform from data to decisions
Unified shipment data exchange
Standard ways in for every participant: shippers send manifests, and carriers publish tracking and scan events through APIs, webhooks or SFTP. Each carrier's native status codes are mapped to one shared set, so a scan means the same thing whichever carrier made it.
Live map: couriers and parcels in motion
The heart of the operations portal: a live Mapbox map of couriers and parcels moving across the network, from intake and sort to transit and delivery, with carrier performance, on-time delivery and lost or delayed parcels alongside.
Parcel tracking and history
One page per parcel with its full route across carriers: every scan and handover, time spent at each stop, the estimated delivery time and proof of delivery, on a timeline and on a map.
Real-time alerts and AI exception triage
Delays, missed scans and at-risk parcels raise alerts as they happen. AI ranks them by urgency and suggests the next action, such as moving a delayed parcel to another carrier on the same lane, which the team can accept or ignore.
AI investigation assistant
A message centre where operations and finance teams ask questions in plain English, such as why a parcel is late or who to contact at a carrier. Built on the OpenAI API and LangChain, it answers from shipment, tracking and invoice data and drafts the follow-up email.
Invoice management and reconciliation
Carriers submit parcel-level invoices in a standard CSV format over SFTP. The platform checks each charge against what was actually shipped and delivered, validates carrier invoices and produces consistent invoices for shippers.
Network analytics and forecasting
Profitability by zip code, network volume forecasting, labour planning and route planning, plus regional demand forecasts built from the package and scan events carriers publish.
Fraud and anomaly signals
Models that look across every participating carrier for suspicious or unusual patterns, alongside delivery insights such as drop-off locations and recipient preferences, surfaced inside the workflows where they matter.
How TechTose helped build the AI logistics platform
We helped build the platform across front end, back end, data and AI: a Next.js front end with live maps in Mapbox GL, Node.js and Python FastAPI services, Python machine-learning models, a data lake with PostgreSQL and Redis for data, AWS infrastructure on Docker and Kubernetes, and charts in D3.js. We started with the data foundation, because AI is only as good as the data under it. Code, product and client details stayed confidential under the NDA throughout.
How AI is used in logistics operations
AI runs through the platform's daily work. Exception triage decides which problems need attention first and proposes a fix, such as switching carriers for a parcel at risk of missing its delivery date. The investigation assistant uses large language models through the OpenAI API and LangChain to answer questions over live shipment, tracking and invoice data and to draft messages to carriers. Forecasting models predict network volume and regional demand so capacity, labour and routes can be planned ahead, and anomaly models flag fraud signals across carriers. People stay in charge: every recommendation can be accepted or ignored.
Where this AI logistics platform stands
Our work on the platform was completed in 2025, and the client's own team now runs and develops it.
What this AI logistics platform includes
- Shipment, tracking and invoice data from every participating carrier in one data layer
- Operations teams see problems as they happen, with a suggested fix, instead of after a delivery date is missed
- Operations and finance teams investigate a parcel or an invoice by asking in plain English
- Carrier invoices checked against real shipments, with consistent invoicing for shippers
Why the client and product aren't named
Many companies hire us to build the product their business depends on. Before we see a line of code, they ask us to keep it private, and we agree. So this page describes the work and the problem it solved, but not who it was for.
How we handle confidential work
- We sign your NDA before you share code, data or plans.
- Your code and accounts are yours. We work inside them, and our access ends when the work does.
- We never name a client, product or person publicly without written permission.
- We describe the work, not the client, as we have on this page.
Want the details? On a call we can walk you through this project, within what the NDA allows.
Questions about this AI logistics project
Why isn't the client named?
We signed an NDA, so the client's name, its product and its code stay confidential. We protect every client's work the same way. If you'd like to know more, book a call and we'll share what the NDA allows.
How is AI used in logistics and parcel delivery?
In this platform, AI does four jobs: it triages shipment exceptions and suggests the next action, answers operations and finance questions in plain English through a large-language-model assistant, forecasts network volume and regional demand, and flags fraud and unusual patterns across carriers. Every recommendation stays a suggestion that people accept or ignore.
How do you combine tracking data from many different carriers?
Each carrier publishes tracking and scan events in its own codes. The platform takes them in through APIs, webhooks or SFTP and maps every native code to one shared set of statuses, so a parcel's journey reads the same way across every carrier it passes through.
What did TechTose build for this logistics platform?
TechTose's work covered unified shipment data exchange, an operations portal and parcel tracking, real-time alerts with AI exception triage, an AI investigation assistant, invoice reconciliation, network analytics and forecasting, and fraud and anomaly signals, across front end, back end, data and AI.
Does TechTose build AI software for logistics companies?
Yes. This project took a logistics platform from scattered carrier data to AI-assisted operations. Most projects start with a free Scan of your app, code or idea, which comes back as a written report in 48 hours.