Logistics — AI, data & automation
Use vision and connected workflows to support parcel sorting and dispatch decisions.
A practical delivery view
Sort to the next step
Parcel auto-sorting routes parcels to the relevant dispatch unit. Camera placement and parcel variability are important design inputs.
- Parcel recognition
- Dispatch-unit routing
- Camera and process review
Handle the exceptions
Real operations include unreadable labels and unusual parcels. Define what is escalated to people before judging the system by average throughput.
- Exception workflow
- Human review
- Representative parcel evaluation
Connect dispatch information
The model is one part of the workflow. Outputs need to connect to the operation receiving the parcel.
- Data foundations
- Application integration
- Operational handoffs
Where logistics operations lose time
A sorting floor runs on speed and accuracy, and both drop when volumes peak. Manual sorting depends on trained people reading labels quickly for hours. A parcel sent to the wrong dispatch unit costs a second handling and a late delivery. Around the floor, tracking data sits in separate systems, so supervisors learn about a backlog after it has formed. These are the problems where vision and connected data can help.
- Manual sorting at peak volume
- Mis-sorts and repeat handling
- Backlogs noticed late
- Tracking data in separate systems
What we have built
Our computer vision work for logistics includes auto-sorting of parcels to their dispatch unit. The same body of vision work, built for logistics, construction, manufacturing and retail, also covers restricted-area monitoring and inventory monitoring. On the data side, we build ETL pipelines that bring operational data from different systems into one place, so that sorting and dispatch information can be used together.
- Parcel auto-sorting to dispatch units
- Restricted-area monitoring
- Inventory monitoring
- Pipelines that join operational data
Start with one line or one hub
A vision project starts with the physical site. On a short call we ask about parcel types, label formats, conveyor speed, lighting and where cameras could go. Sample footage from one line usually tells us more than a long requirements document. From there we agree a pilot on one line or one hub, with clear rules for exceptions. For wider planning, the free AI readiness report is available on this website.
- Site, parcel and label review
- Sample footage from one line
- Pilot with agreed exception rules
Questions that come up
How does parcel auto-sorting with computer vision work?
Cameras capture each parcel as it moves along the line. A vision model identifies the parcel and its destination, and the system directs it to the matching dispatch unit. Parcels the model cannot read with confidence are sent to a person.
What happens with damaged or unreadable labels?
They become exceptions. Before launch, you decide what counts as unreadable and where those parcels go, usually a manual station. Measuring the exception rate on your own parcels is part of the pilot, so you know it before a wider rollout.
Can it connect to our warehouse or dispatch software?
A sorting decision is only useful if the receiving system knows about it. Integration with your existing software is scoped per project, based on the interfaces it offers. We review that before agreeing the work.
Do we need new cameras or hardware?
It depends on the site. Some existing cameras are suitable, and some positions or lighting conditions need changes. We review the current setup and sample footage first, then tell you plainly what is needed.
Let’s talk about your next step.
The workflow, the data and what needs to change.