Turn inbound logistics documents into structured operational data with AI-assisted classification, extraction and validation connected to TMS, WMS and finance systems.
Who this is for
Freight forwarders and 3PLs processing high volumes of BOL, POD and invoices
Back-office teams re-keying partner documents from email and portals
Operations leaders scaling document throughput without proportional headcount
Organizations preparing document automation before broader AI agent rollout
What this solves
- 01
High manual volume across document types and partners
- 02
Inconsistent formats from carriers, warehouses and customers
- 03
Slow exception routing when required fields are missing
- 04
Limited traceability from source document to system record
What we can build first
Multi-channel document intake from email, upload and SFTP
AI classification by document type: BOL, POD, invoice, customs and forms
Field extraction with confidence scoring and validation rules
Exception review queues with side-by-side document and extracted data
Structured output to TMS, WMS, ERP and document storage
Volume, accuracy and processing time monitoring dashboards
How 4RTY helps
Process mapping
Product design
UX and UI
Technical architecture
Development
Integrations
Launch support
Documentation
Systems we integrate with
Delivery & scale path
MVP
Start with a focused release
- Multi-channel document intake from email, upload and SFTP
- AI classification by document type: BOL, POD, invoice, customs and forms
- Field extraction with confidence scoring and validation rules
- Exception review queues with side-by-side document and extracted data
Scale
Expand after launch
- Structured output to TMS, WMS, ERP and document storage
- Volume, accuracy and processing time monitoring dashboards
FAQ
Common questions
Which logistics documents can you process?
Common examples include BOL, POD, invoices, customs documents, delivery notes and operational forms, depending on your formats and validation rules.
Do humans still review extracted data?
Yes. 4RTY designs review paths for low-confidence extractions and exceptions so automation supports logistics companies rather than bypassing them.
How do you measure extraction accuracy?
Pipelines track field-level confidence, logistics company correction rates and straight-through processing volume so teams can tune rules and models over time.
Can this connect to our existing TMS and finance tools?
Yes. Approved extractions post through APIs, CSV or EDI patterns aligned to how your back office already enters data.