Records disagree
The same shipment exists differently in the WMS, the TMS and the invoice.
Forecasting, routing and warehouse automation
AI Data for Supply Chain and Logistics
Logistics models live on messy operational records and warehouse camera feeds. We make both trainable.
Making Operational Data Trainable
Supply chain data is rarely collected for machine learning — it is collected to run a business. Documents are inconsistent, events are recorded late, and the same product has four names across three systems. Preparing that for a model is most of the work, and it is the work we do.
Where Supply Chain & Logistics AI Programmes Struggle
Documents are unstructured
Bills of lading, invoices and customs paperwork arrive as scans, in many formats.
Warehouse vision is cluttered
Occlusion, stacking and reflective packaging make detection genuinely hard.
Demand history has holes
Stockouts, promotions and one-off disruptions distort exactly the periods a forecast learns from.
AI Use Cases We Support
Document extraction
Entity and table extraction from shipping, customs and invoice documents.
Warehouse detection
Pallet, carton and SKU detection for automated inventory counting.
Damage inspection
Labelled damage classes for automated claims triage.
Route and ETA modelling
Structured trip data annotated for delay causes.
Demand forecasting sets
Cleaned, event-annotated history with anomalies flagged rather than deleted.
Loading and dock analytics
Activity and dwell-time labelling from yard and dock cameras.
Data Types We Handle
- Scanned documents Multi-format OCR with field-level annotation.
- Warehouse video Fixed and mobile camera capture, object and activity labelled.
- Transactional records Order, shipment and inventory series normalised across systems.
- Telematics Vehicle position and status series annotated for events.
How We Run the Programme
- Reconcile before labelling Conflicting records are resolved to one truth first, with the rule written down.
- Format-aware extraction Templates per document family, with a general fallback for the tail.
- Anomalies flagged, not removed A stockout is signal. Deleting it teaches the model the wrong lesson.
- Operational sign-off The people who run the process review the taxonomy before volume starts.
Build Supply Chain & Logistics AI on Data You Can Trust
Tell us what you are building and we will come back with a plan, a sample and a price.