AI for logistics that reads the paperwork and sees the delay coming
Shipping document extraction straight into the TMS, delay and exception prediction for the operations desk, freight audit and customer tracking assistants, for freight forwarders, carriers, 3PLs and shippers.
Logistics margins hide in manual data entry, late surprises and disputes. Each use case below removes one of those.
Shipping document extraction
Bills of lading, commercial invoices, packing lists, customs declarations and proofs of delivery read into structured fields and validated against the booking. Touches the TMS, the customs system and the document store. Mismatches are flagged before they become delays at the border.
Delay and exception prediction
A probability of late delivery per shipment, with reasons, from tracking events, lane history, carrier performance and seasonality. Touches the TMS and telematics feeds. The desk works the risky shipments first. See predictive analytics.
Operations desk agent
Drafts carrier chases, updates customers, creates claim records and summarises a shipment's history on request, with the desk approving actions. Touches email, carrier portals and the TMS. See AI agents and automation.
Freight invoice audit
Carrier invoices matched to contracted rates, accessorials and the shipment record, with discrepancies queued for dispute. Touches the ERP and rate management. See invoice processing.
Warehouse demand and labour forecasting
Inbound and outbound volume forecasts by day and shift so staffing and dock scheduling match reality. Touches the WMS and the workforce system. Planners adjust; the model explains its assumptions.
Customer tracking assistant
Where is my shipment, what is the new ETA, what documents are missing: answered from live data in the customer portal or by email, escalated to an account manager when needed. Touches the TMS and the portal. See customer support AI.
Compliance and data
Customs accuracy, driver data and safety-critical decisions
What we design for in logistics
Customs and dangerous-goods declarations carry legal consequences for errors, so extracted values that feed a declaration are reviewed and the source document is linked. Telematics and driver data are personal data under GDPR and UK GDPR; forecasting models use them in aggregate and access is restricted. Commercial data crosses borders and partner boundaries, so tenant and partner isolation is enforced in the data layer. Routing, loading and safety decisions remain with qualified people. This is engineering guidance, not legal advice.
We log every extracted field with its confidence and source image, keep partner data separated, and give the desk an override on every prediction. The AI data privacy and secure RAG architecture pages describe the controls.
Reference architecture
Flagship workflow: documents in, predictions out, one desk in control
Carrier documents arrive by email, portal or scan. Extraction turns them into structured fields and validates them against the booking; clean records update the TMS automatically, mismatches go straight to the operations desk.
The TMS record, enriched with tracking events, telematics and external signals, feeds the delay and exception model. It returns a risk score and reasons per shipment. The desk sees a ranked list and an agent that drafts the chase, the customer update or the claim, ready to approve.
Every automated update and every approved action is written back to the TMS with its provenance, so disputes can be settled from the record. We usually deliver the extraction and the agent first, then the model once enough event history is flowing. The AI data engineering page covers the pipeline work.
Automation handles the clean majority; the desk handles the exceptions with the evidence already assembled.
Track record
What we have built that applies
Field and fleet mobile apps
Driver, courier and field-service apps with offline capture, barcode scanning, photos, signatures and GPS, part of the 1500+ apps delivered since 2006.
Eventify's check-in, badge printing and lead-retrieval scanning run on venue Wi-Fi that drops out, then sync. Warehouse and yard environments are no kinder.
Our documents come as scans, photos and emails from hundreds of carriers. Can AI cope?
That variability is exactly where LLM-based extraction beats template OCR. The model reads a bill of lading or a proof of delivery regardless of layout, returns structured fields with a confidence score and flags what it is unsure about. We measure accuracy on your real document mix first. See document processing.
How good can ETA prediction be?
Better than the carrier's promised date, which is the only bar that matters to the ops desk. The model learns from your history of lanes, carriers, seasons and tracking events and gives a probability of delay with the main reasons. It improves as more events flow in and we monitor it for drift.
Can an AI agent act with carriers and customers, not just alert us?
Yes, within boundaries. Typical actions are drafting a carrier chase email, updating the customer's expected date and creating a claim record, each either automatic or one-click approved by the desk. The agent never books or cancels freight without a person.
What systems do you integrate with?
Transport and warehouse management systems, ERP, carrier APIs and portals, telematics feeds and customer portals. Where a carrier only offers email, the agent reads and writes email. Integration work is scoped in the first two weeks.
Ready to make AI work in production?
Tell us about your systems, your data and your constraints. We reply within one business day with a straight answer and a suggested next step.