Generative AI: Logistics

Generative AI in Logistics

AI that reads the paperwork, triages the exceptions and answers the where is my delivery call, using live data from your TMS, WMS and telematics rather than guesses. Route optimisation stays with proven solvers; language models handle the documents, emails and conversations around them. Senior engineers own the design, AI coding agents carry the volume, and every line is reviewed by a human.

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Key highlights

The fastest paybacks in logistics are freight document extraction, exception triage and status enquiries answered from live tracking data.
Agents read from your TMS, WMS, EDI feeds and telematics, so ETAs and statuses come from the system of record, not from the model.
Language models do not replace route optimisation. Solvers such as Google OR-Tools plan the routes; LLMs explain plans and handle the text around them.
Anything that commits money or changes a booking goes through approval rules and a dispatcher or pricing lead.
A proof of concept usually takes about 4 weeks; a production agent integrated with your TMS usually takes 10 to 16 weeks.

Why logistics AI projects stall, and what is worth solving

Logistics runs on documents, emails and phone calls wrapped around real time data. AI projects stall when they ignore one side of that, or ask a language model to do a solver's job.

Freight documents keyed by hand. Bills of lading, CMR notes, commercial invoices, packing lists and PODs arrive as scans and email attachments. Each one is retyped into the TMS, and one wrong weight or HS code holds a shipment at customs.

An ops inbox full of exceptions. Carrier delay notices, address problems, failed deliveries and damaged goods arrive by email, EDI and phone. Someone reads every one to decide which three actually matter today.

Status calls that live data could answer. Customers call because the tracking page is stale. A chatbot without access to telematics and EDI 214 events cannot help, so the call still lands on an agent.

Language models asked to plan routes. An LLM cannot reliably solve a vehicle routing problem with time windows and capacity limits. Projects that try produce confident but infeasible plans and lose the planners' trust.

Spot quotes and tenders by spreadsheet. Quote requests arrive as free text emails and tenders as spreadsheets with hundreds of lanes. Pricing teams copy lanes by hand and the fastest competitor wins the load.

Pilots that never reach the TMS. A proof of concept extracts data from ten sample PDFs, then stalls because nobody planned the integration, the audit trail or the conversation about driver data with the works council.

Use cases

Generative AI use cases we build for logistics

Each use case connects to your systems of record, runs against a golden dataset of your own documents and messages, and keeps a person in charge of commitments.

Freight document extraction

OCR with AWS Textract or Google Document AI plus LLM extraction into typed schemas for bills of lading, CMR notes, commercial invoices, packing lists and PODs. Fields are validated against the booking and EDI data, low confidence fields go to a review queue, and HS code suggestions go to a licensed broker for confirmation.

Exception management agent

A tool using agent reads carrier emails, EDI 214 status messages and telematics events, classifies each exception by impact, drafts the customer notification and proposes a rebooking or redelivery. The dispatcher approves with one click.

Customer and driver voice and chat agents

Where is my delivery, redelivery and slot changes answered on web chat, WhatsApp or the phone from live tracking data. Voice agents run on FreeSWITCH with streaming speech to text and text to speech, and hand over to a human with the full context.

Quote and tender copilot

Extracts lanes, weights and service levels from quote emails and tender spreadsheets, prices them against your rate cards and TMS history, and drafts the response. A pricing lead approves every number before it is sent.

Operations copilot over TMS and WMS

Planners and managers ask which drops on a route are at risk or why dwell time rose at a depot, and get a text to SQL answer over TMS, WMS and TimescaleDB telematics data with the query shown. Read only by default.

Forecasting and planning with an LLM front end

Volume forecasts from gradient boosted models and routes from Google OR-Tools stay the source of truth. An LLM explains why the plan changed and lets planners adjust constraints in plain language, which the solver then checks.

How we deliver

From one document type to the whole operation

01
Use case discovery and data readiness
We sample your real documents, exception emails and call reasons, and check access to TMS, WMS, EDI and telematics. Output: a ranked use case list with the first one scoped, measured and costed.
02
Evaluation harness and prototype
A golden dataset built from your own documents and messages with field level acceptance criteria, then a prototype tested against two or three candidate models on accuracy, latency and cost per document.
03
Guarded pilot with human in the loop
Live on one lane, depot or customer. Low confidence extractions go to a review queue, rebookings and quotes need approval, and every agent action is logged against the shipment.
04
Production rollout with monitoring
Roll out by lane or site behind a feature flag, with accuracy and cost dashboards, drift alerts, retry queues into the TMS, and quarterly model reviews.

AI governance for logistics

Logistics AI touches customs declarations, customer data and, most sensitively, the people who drive and pick. Governance is designed in from the pilot.

EU AI Act (Annex III worker management) EU Platform Work Directive GDPR and UK GDPR UAE PDPL awareness Customs declarations (CDS, ICS2, ACE) PII redaction before prompts Audit trail per shipment Self hosted model option

AI that manages workers is high risk in the EU. The EU AI Act lists AI used to allocate tasks or to monitor and evaluate workers as high risk. The EU has been revising when those obligations apply, so we design for them now rather than waiting for the final date. Gig delivery platforms also face the EU Platform Work Directive, which member states must transpose by 2 December 2026. We keep driver facing decisions explainable, logged and subject to human review.

The declarant stays responsible. AI can extract data and suggest HS codes for UK CDS, EU ICS2 or US ACE filings, but a licensed broker or your customs team confirms every declaration. Each suggested value keeps a link to the source document and the person who approved it.

Personal and commercial data stay where they should. Consignee names, addresses and driver locations are redacted before prompts where the task allows, models run under enterprise terms that exclude training on your data, and EU, UK or UAE hosting or self hosted models are available when residency demands it.

Models and tooling we use for logistics AI

Portable language models for documents and conversations, proven solvers for optimisation, and adapters for the logistics systems you already run.

Claude, GPT-5 and Gemini Llama 4 and Qwen (self hosted) LangGraph AWS Textract and Google Document AI pgvector PostgreSQL and TimescaleDB Google OR-Tools EDI X12 204 and 214 FreeSWITCH voice agents Deepgram and Whisper ElevenLabs Langfuse

Related case study

Real Time Fleet Tracking Platform for UAE Logistics

A real time fleet tracking platform and driver app for a 400 vehicle UAE operator, built in 12 weeks. See how it cut fuel use 22% and late deliveries 35%.

Read the full case study

FAQ

Common questions

How much does a generative AI project in logistics cost?

It depends on scope, so we quote from a written estimate rather than a price list. Most clients start with a fixed price proof of concept on one document type or exception flow, then move to a production build or a dedicated engineer. Model API costs are passed through at cost with per workflow caps. Our pricing page lists current entry points.

Is shipment and driver data safe with an LLM?

Yes, when it is designed for. We use enterprise model APIs that do not train on your data, redact personal data before prompts where possible, offer regional hosting or self hosted models, and give agents read only access by default. Driver location data gets retention limits and role based access.

How accurate is AI extraction on freight documents, and what about hallucinations?

Accurate enough to automate the clean majority, with the rest routed to people. We measure field level accuracy on a golden set of your own documents, validate extracted values against the booking and EDI data, and send low confidence fields to a review queue instead of guessing.

How long does a first logistics AI project take?

Usually about 4 weeks for a proof of concept and 10 to 16 weeks for a production agent integrated with your TMS. Document extraction tends to be fastest; agents that rebook or quote take longer because of approval flows.

Should we buy a logistics AI product or build our own?

Buy where the workflow is standard and build where your data and rules are the advantage. Off the shelf document capture works for common forms. When the agent needs your rate cards, your exception rules and your TMS together, a custom build on portable models usually fits better.

Can generative AI replace our route optimisation software?

No. Routing with time windows and capacity limits is a job for constraint solvers such as Google OR-Tools. We use language models around the solver: to read orders, explain plan changes and let planners express constraints in plain language.

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