Self-Hosted ML · Document Processing
OCR & Document Intelligence
Self-Hosted ML · Document Processing
Document intelligence built entirely on internally trained models, with no third-party AI service or external inference API anywhere in the pipeline — from dataset preparation and training through to a structured extraction serving layer.
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- Domain
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- Capabilities
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- Public links
Who it's for
Businesses processing confidential documents — invoices, IDs, contracts — under privacy or data-residency rules that forbid third-party OCR APIs.
Where it fits
Back-office document pipelines feeding ERPs and business systems, with low-confidence results routed to human review instead of guessed.
The hard part
The problem
Document processing usually means uploading source documents to a third-party OCR API — which is a non-starter when those documents are confidential or subject to data-residency rules.
Architecture overview
All recognition and extraction models are trained internally and served from owned infrastructure. Celery workers run inference off the request path. Recognised text is parsed into typed, validated fields with confidence thresholds, retry logic and a human review queue for low-confidence results.
Confidence gate → human review
How it works
What's actually in it.
- 01Fully in-house model stack — no external AI provider or hosted inference API.
- 02Training and evaluation pipeline: dataset preparation, annotation, training runs and benchmarking against held-out sets.
- 03Asynchronous serving layer keeps API latency independent of model processing time.
- 04Structured extraction into typed, validated fields with confidence thresholds and a human review queue.
Engineering decisions
Self-host the whole pipeline
Rather than depend on a hosted OCR API, every model is trained and served in-house. Source documents never leave controlled infrastructure, there is no per-call vendor cost, and the capability stays fully owned — the same principle that runs through Airsume and MeetingMind.
The trade-off
Training and maintaining your own recognition models is more work than an API call, but it keeps confidential documents on owned infrastructure and removes vendor cost and lock-in.
Technical highlights
- Source documents never leave controlled infrastructure.
- Low-confidence results route to a human review queue rather than being trusted blindly.
Claims & evidence
No external AI provider or hosted inference API sits anywhere in the pipeline.
Qualitative · OCR project design constraint
Every statement above carries its evidence class — nothing is invented.
Tech stack
- Python
- PyTorch
- Self-trained recognition models
- Django REST Framework
- Celery
- Redis
- PostgreSQL
Want to go deeper on OCR & Document Intelligence?
I'm happy to walk through the architecture and the decisions behind it.