An on-premises AI document processing pipeline
A legal services provider needed to process documents with varying layouts in batches of up to 100 pages, without the recurring cost of a cloud AI API at volume. We deployed a physical AI server on-site behind the client's own static IP, orchestrated by n8n, with a local model returning structured JSON and no per-request cost.
- CLIENT
- A US legal services provider
- INDUSTRY
- Legal services
- ENGAGEMENT
- On-premises AI and OCR document processing architecture and development
- STACK
- On-prem AI server, n8n, Local LLM, OCR pipeline
DATA
High-volume document processing, with cloud AI pricing and data residency both working against it.
- Cloud AI API costs would be significant at the client's document volume
- Documents have varying layouts and formatting, requiring flexible, reliable extraction rather than a fixed template
- Data-residency and privacy considerations apply when legal documents are sent to outside services
One system, built around how the work actually happens.
A physical AI server on-site
Installed at the client's own office, sitting behind their static IP, rather than a cloud instance or a VPS anywhere else.
n8n as the automation layer
The client's web app hands a document to n8n, which routes it to the on-premises AI server and returns the structured result to the application.
A local model, not a cloud API call
A locally hosted model (Llama-class) processes each document on the client's own AI server and returns structured JSON, rather than sending it to a third-party AI API.
No per-request AI API cost
The cost model is the one-time hardware investment plus electricity and upkeep, rather than a fee for every request as volume grows.
What it runs on.
What changed.
- No per-request cloud AI API cost, only the one-time hardware cost plus electricity and upkeep
- Documents with varying layouts and page counts processed into structured JSON
- Document processing runs on the client's own AI server rather than a third-party cloud AI API
This page shows no percentages or figures because none have been confirmed by the client for publication. Where a number is not verified, it is left out rather than estimated.
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