Solution guide · AI on AWS + NVIDIA
AI Document Processing for Freight and Logistics Companies, on AWS with NVIDIA NIM
Bills of lading, rate confirmations, proofs of delivery and carrier invoices still eat hours of typing. Here's how we design a private AI pipeline that reads them, checks them and posts the data to your systems.
- AWS
- NVIDIA NIM
- NVIDIA NeMo Retriever
- Amazon S3
- Your TMS
- QuickBooks or Business Central
This is a solution guide: it shows how we design and build this kind of system. It is not a client case study.
Where the paperwork piles up
Every load creates documents in different formats from different partners. Teams retype the same data into the TMS and the accounting system, and mistakes delay billing and carrier payments.
- Rate confirmations and bills of lading arrive as PDFs, scans and phone photos.
- Staff retype load numbers, addresses, weights and charges by hand.
- Invoices wait until someone finds and matches the proof of delivery.
- Carrier invoices don't match the agreed rate, and nobody notices.
- Customer data can't go into public AI tools because of contracts and privacy.
What we build
We design a document pipeline that runs in your own AWS account, so documents and data stay private.
Email and upload capture
Documents arrive by email, upload or API and are stored securely in Amazon S3.
AI reading and extraction
Vision and language models served with NVIDIA NIM read each page and pull out load numbers, parties, dates, weights and charges.
Checks against your data
Extracted fields are compared with the load in your TMS and the agreed rate. Mismatches go to a review queue.
Human review screen
Staff see the document next to the extracted data, fix anything flagged and approve with one click.
Posting to your systems
Approved data updates the TMS and creates invoices or bills in QuickBooks, Business Central or your ERP.
Search across documents
NVIDIA NeMo Retriever indexes every document, so staff can ask, 'Which PODs from this week are missing a signature?'
How a document moves
- ReceiveA carrier emails a POD photo and an invoice.
- ReadThe AI sorts each document by type and extracts the fields.
- MatchThe pipeline finds the load and compares charges with the rate confirmation.
- ReviewClean documents pass straight through. Exceptions go to a person.
- PostThe load is marked delivered, and the customer invoice and carrier bill are created.
- MeasureDashboards show volume, straight-through rate and time saved.
Our recommended stack on AWS
| Layer | Tool | What it does |
|---|---|---|
| Storage | Amazon S3 | Encrypted document storage |
| AI models | NVIDIA NIM on Amazon EKS or EC2 GPU instances | Document reading, extraction, summaries |
| Search | NVIDIA NeMo Retriever | Indexing and answers across documents |
| Workflow | AWS Step Functions and AWS Lambda | Routing, checks, retries |
| Review | Web review screen | Human approval of exceptions |
| Integration | TMS and ERP APIs | Loads, invoices, carrier bills |
NIM packages AI models as ready-to-run services. Running them in your own AWS account keeps documents inside your environment.
Typical rollout plan
- 1–2 weeksSample testWe collect a few hundred real documents and measure what the AI reads correctly.
- 3–4 weeksPipeline buildWe set up the AWS environment, models, checks and review screen.
- 2–3 weeksIntegrationWe connect your TMS and accounting system and test with live loads.
- 1 weekGo-liveWe switch on one document type at a time, starting with the highest volume.
What to prepare
- Sample documents of each type, shared under NDA
- API access to your TMS and accounting system
- Your rules for matching rates and charges
- Who reviews exceptions today
- Your security and data-retention rules
Common questions
Why run our own models instead of a public AI tool?
Models served with NVIDIA NIM in your own AWS account keep documents and customer data inside your environment, which helps with customer contracts and privacy rules. You also control the cost and the model choice.
How accurate is it?
It depends on your documents. We measure accuracy on your own samples before you commit, and the review queue catches anything the AI is unsure about.
Do we need GPUs running all the time?
No. Lower volumes can run on smaller GPU instances that scale down outside business hours. We size the setup to your document volume.
Which TMS can it connect to?
Any TMS or ERP with an API or file import. Where there is no API, we use structured exports or email.
Buried in freight paperwork?
Send us a few sample documents. We'll show you what AI can read from them before you build anything.
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