Offer 5 of 5 · Invoice & document extraction
Extraction that survives 5,000 documents, not 5.
A single-prompt demo looks great on the invoice you picked. It falls apart on the messy, rotated, European-decimal, missing-field ones that make up the other 4,995. I build extraction pipelines with confidence scoring, validation rules, and a human review queue. I publish the accuracy benchmark that proves the difference.
For: teams posting extraction jobs that name a volume or an accuracy requirement, not the $15–37/hr "scrape a PDF" floor.
What the pipeline does
- Ingests PDFs, images, photos, rotated and scanned documents, with deskew and an OCR path that degrades gracefully when no OCR engine is present.
- Regex PII redaction (email, Luhn-checked card, SSN/SIN, IBAN, phone) before any model call, with a restore map afterward.
- Layout-aware extraction into a strict schema, then field-level confidence scoring.
- Validation and repair: line items sum to subtotal, tax math checks out, currency and dates normalized.
- A confidence-gated human review queue: low-confidence fields get a human, high-confidence ones don't.
- A correction-feedback loop that turns fixes into few-shot exemplars for future documents.
The benchmark: against a naive baseline
| 0.902 | pipeline macro-F1 (synthetic dataset, mock-mode) |
| 0.221 | naive single-prompt baseline (synthetic dataset, mock-mode) |
| 31 of 45 | wrong-field documents caught by the review gate |
Proof: open the dashboard and the essay
Benchmarked extraction pipeline
The full pipeline plus an eval dashboard, per-field precision/recall/F1, and a failure-case gallery with analysis.
"Why your extraction demo fails at 5,000 documents"
The data-backed essay behind this offer: the exact failure modes of the naive baseline, and what buying reliability actually looks like.
Failure-case gallery
The documents the pipeline got wrong, and why: the honesty that separates an accuracy buyer's vendor from a demo shop.
Send me a handful of your real documents.
The messy ones: rotated scans, foreign formats, missing fields. Tell me the volume and the fields you need. I'll benchmark before I quote.
amin.ale.business@gmail.com