AI Automation for Logistics & Supply Chain
Logistics teams spend hours re-keying orders, chasing shipment updates and answering 'where is my order?' messages. AI agents can read those documents and messages, update your systems and flag problems before they become delays.
This is an example project. It shows how we would approach this kind of work in supply chain & logistics; it does not describe a specific client. Results depend on your process and data, and we agree on how to measure them with you before starting.
Common Challenges
- Orders, delivery notes and invoices arrive in different formats and are typed in by hand.
- Customers and partners ask for shipment status all day by email and phone.
- Exceptions such as late carriers or stock shortages are spotted too late.
- Planning relies on spreadsheets that are out of date by the time they are shared.
Our Approach
We connect a Nexus workflow agent to your warehouse and transport systems (WMS, TMS, ERP, email) so it can extract data from documents, keep records in sync, answer status questions and raise alerts on exceptions, with a person approving anything unusual.
- Document intake: read orders, delivery notes and invoices and enter them into your systems.
- Shipment status answers by email or chat, pulled live from your TMS.
- Exception alerts for late carriers, missing stock or mismatched paperwork.
- Daily operations summaries in plain language for managers.
Agents used: Nexus workflow automation agent, Orbis business analytics agent
Typical Rollout
Discovery
Map the current process, systems and data, agree on the baseline numbers and how success will be measured.
Pilot build
Build and connect the agent for one scope: one document type or one customer-service inbox. Set permissions, guardrails and hand-off rules.
Supervised pilot
Run on real cases with a person reviewing the agent's work, tuning it until results are reliable.
Review and scale
Compare results with the baseline and extend to more teams, sites or tasks only if the pilot clearly pays off.
Timings are indicative and depend on your systems and scope.
What We Measure
We record these before the pilot starts and compare them afterwards, so you can see whether it worked.
- Hours of manual data entry per week
- Data entry error rate
- Time to answer status requests
- Exceptions caught before customer impact
Explore This for Your Business
Tell us about one process that takes too much of your team's time. We'll tell you honestly whether AI can help and what a pilot would look like.