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Industries · Logistics, Transport & Supply Chain

AI for Logistics

Aaga builds AI and software for logistics companies, 3PLs, freight forwarders, fleet operators and shippers. We predict ETAs, read shipping documents, answer tracking questions automatically and bring computer vision into the warehouse, all connected to your TMS, WMS and ERP.

  • Predict delivery ETAs from GPS, traffic and history, and warn customers before delays
  • Extract data from invoices, bills of lading, e-way bills and proofs of delivery
  • Answer 'where is my shipment' on WhatsApp, chat and phone without waiting for an agent
  • Use cameras to count, read labels and spot damage in the warehouse
Tracking questions answered
24/7
One lane, depot or document type first
Pilot
Reply time on business days
1 day

Talk to Our Logistics AI Team

Tell us about your network, systems and the bottleneck you want to fix. A senior engineer replies within one business day.

We reply within one business day. Your details stay private.

The Problem

Logistics Challenges We Help Solve

Logistics runs on tight margins, many handoffs and a lot of paper. Small delays and errors add up across every shipment.

  • Unreliable ETAs

    Static ETAs ignore traffic, dwell time and driver patterns, so customers and warehouses plan around the wrong arrival times.

  • Paperwork and re-keying

    Invoices, bills of lading, airway bills, packing lists, e-way bills and PODs arrive as PDFs, scans and photos that staff type in by hand.

  • Tracking calls and emails

    Control towers and customer service spend hours answering 'where is my shipment' from shippers, consignees and drivers.

  • Warehouse errors

    Miscounts, mislabeled pallets and unrecorded damage cause disputes, claims and inventory mismatches.

  • Fragmented systems

    TMS, WMS, ERP, telematics, carrier portals and customer systems rarely share data in real time.

AI Use Cases

Where AI Works in Logistics Operations

Each use case runs on data you already generate: GPS pings, scans, documents and messages.

  • ETA prediction

    Machine learning models predict arrival times from GPS, traffic, historical lane performance and stop dwell times, and update them en route.

  • Route and load planning

    Plan multi-stop routes and loads against time windows, vehicle capacity and driver hours, and re-plan when orders change.

  • Logistics document AI

    Extract and validate data from invoices, bills of lading, customs paperwork, e-way bills and POD photos into your TMS or ERP.

  • Tracking bots

    WhatsApp, chat and voice assistants that give live shipment status, ETAs and POD copies, and log exceptions.

  • Warehouse vision

    Cameras count cartons and pallets, read labels and barcodes, check dimensions and record damage at dock doors.

  • Exception management agents

    Agents watch for delays, failed deliveries and missing documents, then alert the right team and notify the customer.

What We Build

Logistics AI and Software We Build

Each solution links to the service behind it. Most operators start with documents or tracking.

How We Deliver

From Operations Review to Live Pilot

  1. Operations and data review

    We map shipment flow, documents, systems and data sources such as telematics, scans and carrier feeds.

  2. Pick the pilot

    We choose one lane, depot, customer or document type with clear volume and a measurable outcome.

  3. Build and integrate

    We build the model, agent or vision system and connect it to your TMS, WMS, ERP and messaging channels.

  4. Run in parallel

    It runs alongside your current process so your team can compare ETAs, extracted data or counts before relying on it.

  5. Go live and scale

    We switch over, monitor accuracy and exceptions, and extend to more lanes, sites or customers.

What Is AI for Logistics?

AI for logistics is the use of machine learning, document AI, conversational agents and computer vision to make freight, fleet and warehouse operations faster and more accurate. It predicts when shipments will arrive, reads the paperwork that comes with them, answers tracking questions and checks what actually arrived at the dock. The aim is fewer phone calls, less re-keying, fewer disputes and better planning.

Aaga is an AI-native engineering company. We build the models and agents together with the integrations and software they need, so AI works inside your TMS, WMS and ERP rather than beside them.

Which Logistics Problems Should AI Solve First?

Start where volume is high and the data already exists.

Problem Data AI uses Good first pilot
Inaccurate ETAs GPS pings, lane history, stop times, traffic One lane or region
Manual document entry Invoices, BOLs, e-way bills, PODs One document type
Tracking calls and emails Shipment status, ETAs, PODs One customer or channel
Warehouse count and damage disputes Camera images at docks One dock or zone
Late exception handling Status events, delivery attempts One exception type

ETA prediction and route planning

An ETA prediction model learns from your own history: how long each lane really takes, typical dwell time at each customer, traffic patterns by day and hour, and driver behavior. It updates arrival times as GPS data comes in and flags shipments likely to miss their window, so you can warn the customer before they call. Route and load planning uses optimization with your constraints, such as delivery windows, vehicle capacity and driver hours. Our machine learning team builds and monitors these models.

Document AI for shipping paperwork

Logistics document AI is software that reads shipping and finance documents and turns them into structured data. It handles commercial invoices, packing lists, bills of lading, airway bills, delivery challans, e-way bills, freight invoices and POD photos taken on a driver's phone. Each field is checked against your rules, such as matching the PO, consignee and weight, and low-confidence fields go to a reviewer. Clean data posts straight into your TMS or ERP through our AI automation workflows.

Tracking bots and proactive alerts

A tracking bot answers "where is my shipment" on WhatsApp, web chat or phone from live data. It shares the current status, the predicted ETA and the POD once delivered. More useful still, it messages customers before they ask: when a shipment is delayed, out for delivery or delivered. For failed deliveries, it collects a new slot or address and updates the system. See our WhatsApp and AI agent development services.

Warehouse vision

Computer vision at dock doors and in aisles counts cartons and pallets, reads labels and barcodes, estimates dimensions and photographs damage with a timestamp. That evidence settles disputes quickly and keeps inventory accurate. Models run on edge devices on-site, using the same perception skills we apply in AI for robotics and on the factory floor in AI for manufacturing.

How Does AI Connect to Logistics Systems?

We integrate across the stack: TMS and WMS platforms, ERPs such as SAP or Oracle, telematics and GPS providers, carrier and shipping aggregator APIs, EDI feeds, customer portals, email inboxes where documents arrive, and messaging channels. Where a system has no API, we use EDI, secure file exchange or structured exports. Aaga's own platform provides the workflow, permission and integration building blocks, so a shipper portal or exception dashboard doesn't start from zero.

Aaga has worked in supply and distribution before, including for Van Diest Supply Company, a family-owned agribusiness company with distribution, formulation and liquid bulk terminaling operations.

Why Logistics Teams Choose Aaga

  • Senior engineers, directly. You work with the people building your models and integrations.
  • Pilot first. One lane, depot or document type before a network-wide rollout.
  • Practical AI. Every model runs in parallel with your current process before you rely on it.
  • Affordable. Lower cost than large IT services firms for comparable scope.

See all industries we serve or talk to us about custom software development for portals and driver apps.

Popular Questions

Frequently Asked Questions

AI is used for ETA prediction, route and load planning, reading shipping documents, answering tracking questions, warehouse counting and damage detection, and managing exceptions such as delays and failed deliveries. Aaga builds these and connects them to your TMS, WMS, ERP and messaging channels.

Accuracy depends on your data: GPS frequency, lane history and how consistently stops are recorded. We measure accuracy against your actual arrivals during a parallel run, so you see real results on your network before relying on the model.

Commercial invoices, packing lists, bills of lading, airway bills, delivery challans, e-way bills, rate confirmations and proof-of-delivery photos, including scans and phone photos. Extracted data is validated against your rules, and low-confidence fields go to a person for review.

Yes. A WhatsApp tracking bot answers status and ETA questions from live data, shares POD copies and sends proactive alerts on delays, using approved templates and opted-in contacts.

Not always. Existing CCTV can work for some tasks, such as dock activity. Counting, label reading and damage detection usually need cameras placed and lit for the job. We assess your site before recommending hardware.

We integrate with TMS and WMS platforms, ERPs such as SAP or Oracle, telematics and GPS providers, carrier and shipping aggregator APIs, EDI feeds and customer portals. If a system has an API, EDI or reliable export, we can usually connect it.

It depends on data availability, integrations and any hardware. After a free consultation we give you a scoped pilot plan and budget range for one lane, depot or document type.

Explore More

Fewer Calls, Fewer Errors, Better ETAs

Tell us the bottleneck that costs you most. We'll show you how AI can fix it in a scoped pilot.

Discuss My Operation