You will get your PDFs converted into clean structured JSON or CSV data using AI
Project details
Turn your PDFs, invoices, contracts, and reports into clean, usable data automatically.
Manual extraction is slow and error-prone. Naive AI extraction returns inconsistent, malformed outputs. I build production-grade LLM extraction pipelines that solve both problems.
Real results from my production work: a pipeline that converts 200+ page financial statement PDFs into schema-validated JSON in under 25 seconds, with a self-correcting validation loop and 75% lower token costs than standard approaches.
What you get:
1. A working extraction pipeline built for your exact document type
2. Output validated against a schema we define together, so no malformed data ever reaches your systems
3. Clean code, documentation, and full handover
Ideal for invoices, financial statements, contracts, forms, reports, and resumes. If you process the same document type repeatedly, this pays for itself fast.
Send me a sample document and I will confirm feasibility before you order.
Manual extraction is slow and error-prone. Naive AI extraction returns inconsistent, malformed outputs. I build production-grade LLM extraction pipelines that solve both problems.
Real results from my production work: a pipeline that converts 200+ page financial statement PDFs into schema-validated JSON in under 25 seconds, with a self-correcting validation loop and 75% lower token costs than standard approaches.
What you get:
1. A working extraction pipeline built for your exact document type
2. Output validated against a schema we define together, so no malformed data ever reaches your systems
3. Clean code, documentation, and full handover
Ideal for invoices, financial statements, contracts, forms, reports, and resumes. If you process the same document type repeatedly, this pays for itself fast.
Send me a sample document and I will confirm feasibility before you order.
AI Development Type
Model Tuning, Software MaintenanceAI Tools
PyTorchAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$75
|
Standard
$200
|
Advanced
$450
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 15 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Detailed Code Comments | - | ||
Knowledge Graph | - | ||
Model Documentation | - | - | |
Ontology | - | - | - |
Source Code | - | - | |
Taxonomy | - | - | - |
About Mohcine
AI/LLM Engineer | RAG, Document Extraction, Chatbots & Full-Stack Apps
Casablanca, Morocco - 11:13 pm local time
documents, return validated structured data, and don't blow up your API bill.
Recent production work:
- A 3-stage LLM extraction pipeline converting 200+ page financial-statement
PDFs into schema-validated JSON in under 25 seconds (parallelized API calls,
live log streaming)
- A self-correcting validation loop that feeds Pydantic schema errors back to
the LLM combined with context caching, it cut input-token costs by 75%
- A RAG chatbot indexing 2,000+ documents at 92% retrieval precision
(LangChain, ChromaDB runs fully offline)
- MockCoder, an AI interview platform with 200+ active users
(Next.js, Gemini, voice AI)
What I can build for you:
✅ Document extraction turn PDFs, invoices, contracts, or reports into
clean, validated JSON. Reliably, at scale.
✅ RAG chatbots trained on your docs, website, or knowledge base
✅ Web scraping with structured output (clean CSV/JSON, not raw HTML dumps)
✅ LLM integration into your existing product (OpenAI, Gemini, open-source
models via Ollama/vLLM)
✅ LLM pipeline audits I'll cut your token costs and latency
I'm a software engineer first: every system ships with a FastAPI backend, a
clean Next.js frontend when needed, deployment, and documentation.
Message me with what you're trying to build I'll reply within a few hours
with a concrete plan and a fixed quote.
Steps for completing your project
After purchasing the project, send requirements so Mohcine can start the project.
Delivery time starts when Mohcine receives requirements from you.
Mohcine works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements Review
I review your sample documents, confirm the exact data fields to extract, and define the output schema with you before any coding starts.
Pipeline Developmen
I build the extraction pipeline with validation logic tailored to your document type, ensuring every output matches the agreed schema.