Python · FastAPI · OCR
Invoice & Document Extraction Service
Upload invoices, receipts or forms and get structured JSON back. Large batches go through a queue and results are delivered by webhook.
Screens are illustrative layouts of the product, not client screenshots.
Overview
What it is
A document-processing API. Upload invoices, receipts or forms and receive structured data with confidence scores. Large batches are queued and delivered by webhook.
The problem
Why it was needed
Finance teams were keying invoice data by hand. Generic OCR returned raw text, with no structure or way to know what was uncertain.
Features
What We Built
Upload API
Single files or batches by API or dashboard.
OCR and extraction
Text recognition plus LLM-assisted field extraction.
Confidence scores
Per-field scores, with a review queue for uncertain ones.
Webhooks
Idempotent delivery with retries.
Exports
CSV or direct push to accounting tools.
Review UI
Correct a field and the fix is stored for later tuning.
Architecture
How It Fits Together
- Upload APIFastAPI + S3
- QueueCelery workers
- OCRtext and layout
- Extractionfields via rules + LLM
- Review queuelow-confidence items
- Webhook / exportstructured JSON, CSV
Every document moves through the same queued steps, so large batches scale by adding workers.
Roadmap
From Idea to Launch
A typical delivery roadmap for a project of this kind.
- 1
Document study
3-5 days- Collect sample documents and layouts
- Define the target fields
- Agree accuracy targets
- 2
Extraction core
2 weeks- OCR and layout parsing
- Field extraction and validation
- Confidence scoring
- 3
API and queue
1-2 weeks- FastAPI endpoints and auth
- Celery batches
- Webhooks and idempotency
- 4
Review and export
1 week- Review UI for low-confidence fields
- CSV and accounting integration
- Feedback storage
- 5
Launch
1 week- Accuracy benchmark on real documents
- Monitoring
- Handover
Want something like this?
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