Browse categories
Explore
Fiverr Pro
English
$
USD
I will build ai document processing to extract validate data from pdf and ocr
Abdul W
About this gig
TURN DOCUMENT PILES INTO CLEAN, VALIDATED DATA
If someone on your team reads documents and re-types the contents into a system, that job can run automatically, with an audit trail.
WHAT I BUILD
- Structured extraction from PDFs, scans, invoices, contracts, quotations and reports
- OCR pipeline for scanned and image-based documents
- Rule-based validation: flag any document that breaks your business rules
- Spatial verification: violations highlighted at exact coordinates on the original PDF
- Confidence scores and review queue for anything uncertain
- Output as JSON, CSV, database rows, annotated PDF or API endpoint
- Batch processing for thousands of docs
REAL EXAMPLE
I built an LLM compliance checker that reads business PDFs, tests them against editable rules (specs, lead times, warranty terms), locates the offending sentence, and returns an annotated PDF with red boxes plus a pass/fail table with evidence and reasoning.
WHY BUYERS PICK ME
- Every extraction is traceable back to the source text
- Deployed to your cloud with documentation and clean handoff
Send 3 sample documents and your rules for a scoped quote.
Get to know Abdul W
Abdul W
AI Engineer
- FromPakistan
- Member sinceMar 2021
- Last delivery1 year
Languages
English
I build AI systems that run in production, not prototypes that break after the demo.
6+ years in AI and ML. I ship agentic workflows with LangGraph, RAG and Graph-RAG assistants over your documents, extraction and compliance pipelines, and FastAPI backends deployed to your cloud.
My background is deep learning at scale: distributed training across thousands of GPUs, reinforcement learning, LLM fine-tuning. That depth is why my systems handle messy real-world data instead of failing on it.
You get documented code and a clean handoff.
Send your workflow and I will reply with a scoped plan.
FAQ
My documents have no fixed template. Can you still handle them
Yes, that's the point of using an LLM instead of a regex parser. It reads meaning, not position, so layout changes don't break extraction. I still add validation so anything unusual is flagged rather than silently wrong.
How accurate is it?
Depends on document quality, and I'll never quote a number before seeing your documents. I run a benchmark on your real samples and report field-level accuracy, so you decide with data. Low-confidence extractions go to a human review queue.
Can it check documents against our rules, not just extract?
Yes. I build an editable rule engine — your team writes rules in plain English, the system returns pass/fail per rule with the evidence sentence, the reason, and coordinates on the original PDF.
What about scanned or photographed documents?
Handled with an OCR fallback layer. Quality varies with the scan, which is why I benchmark on your actual documents before committing to accuracy targets.
Is my data secure?
I can build fully self-hosted with open models so documents never leave your infrastructure. NDA on request, and I delete all sample data after delivery.
