Logistics AI and software development
We apply AI in logistics where the work is repetitive and the data is scattered: order intake, shipment exceptions, customs data and client integrations. We build the agents, integrations and software around your TMS, WMS and fleet systems, and your planners keep the decisions.
Industries in logistics and mobility
The work, and where software fits.
Logistics runs on handoffs. A shipment passes between shippers, forwarders, carriers, terminals, warehouses and customs, and every handoff produces an email, a PDF, an EDI message or a portal update that someone has to read and act on. Most of the delay and cost in operations teams comes from this reading, retyping and chasing.
Software fits in three places. Integrations bring orders, milestones and stock data into your TMS and WMS in a clean form, and document processing and agents take over intake and exception handling, with planners approving the actions that matter. Custom portals and internal tools then give customers and staff one view of work that used to be spread over a dozen systems.
How we improve logistics and mobility.
Order and document intake
Transport orders, packing lists, CMRs and invoices read from email and PDF and created as drafts in your operational systems.
Exception management
Agents that detect late milestones, mismatched quantities or failed deliveries, explain the likely cause and draft the next step for a planner.
Carrier, client and EDI integrations
EDIFACT, carrier APIs, webshops and track and trace platforms connected through one monitored layer, so onboarding a partner is configuration work.
Customer and driver portals
Booking, tracking and document portals for shippers, and field apps for drivers and engineers that keep working with poor coverage.
Operational data and reporting
Shipment, warehouse and fleet events combined into one data model for billing, client reporting, emissions data and planning.
Related work.
Further reading.
Human-in-the-loop AI agents: design patterns for production
The patterns we use to run AI agents safely in production: approval checkpoints, confidence thresholds, tool permissions, audit trails and fallbacks.
4 min read
Document parsing with LLMs: extracting data from invoices and forms
How to turn invoices and forms into reliable structured data with LLMs, schemas, validation and a review queue for the cases that need a person.
4 min read
LLM evals: how to test AI features before every release
A practical approach to LLM evals: build a test set from real cases, combine code checks with model grading, and block releases that regress.
5 min read
Common questions.
Start with a process that has high volume, clear rules and measurable outcomes, such as order intake or delay notifications. These pay back quickly and build the integrations later projects need.
No. Agents gather information and draft actions, and planners and dispatchers approve changes to bookings, customer messages and recovery options. Every action is logged with who approved it.
Yes. We deploy in EU regions or in your own cloud account and can use EU-hosted models. Shipment, customer and driver data stays in infrastructure you control.
Yes. We build through the interfaces your vendor supports and coordinate with them on configuration changes. You own the integration code, so you are free to change suppliers later.