You will get an AI pipeline to extract and classify documents automatically
Rising Talent
Rising Talent
Project details
Drowning in PDFs, invoices, contracts, or forms? I build AI pipelines that extract, classify, and route your documents automatically — no manual data entry.
Real results from production systems:
• Document classification reducing manual review 70-85% (Icod Systems)
• Regulatory form automation reducing manual work 75-90% (Spanish legal client)
• LLM fine-tuning for domain accuracy +25-40% over base models
What you get: OCR extraction, document classification, structured JSON/CSV output, n8n workflow automation, human review queue for edge cases.
I handle the messy stuff — scanned PDFs, inconsistent layouts, handwritten fields, multi-page documents. Edge cases route to a human queue with notes, not silent failures.
Stack: Python, FastAPI, n8n, Tesseract, LLM APIs, Docker.
Send me an example document and I'll tell you exactly what I can automate.
Real results from production systems:
• Document classification reducing manual review 70-85% (Icod Systems)
• Regulatory form automation reducing manual work 75-90% (Spanish legal client)
• LLM fine-tuning for domain accuracy +25-40% over base models
What you get: OCR extraction, document classification, structured JSON/CSV output, n8n workflow automation, human review queue for edge cases.
I handle the messy stuff — scanned PDFs, inconsistent layouts, handwritten fields, multi-page documents. Edge cases route to a human queue with notes, not silent failures.
Stack: Python, FastAPI, n8n, Tesseract, LLM APIs, Docker.
Send me an example document and I'll tell you exactly what I can automate.
Machine Learning Tools
NumPy, Python, Tesseract OCRWhat's included
| Service Tiers |
Starter
$1,000
|
Standard
$2,000
|
Advanced
$3,500
|
|---|---|---|---|
| Delivery Time | 7 days | 10 days | 14 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 3 | 5 |
Number of Scenarios | 3 | 10 | 20 |
Number of Graphs/Charts | 0 | 1 | 2 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code |
Frequently asked questions
1 review
(0)
(1)
(0)
(0)
(0)
This project doesn't have any reviews.
AG
Ander G.
May 4, 2026
AI Automation Specialist (n8n + Database + Web Form Automation) – Long-term collaboration possible
About Eduardo
AI Engineer | RAG, LLM Fine-Tuning & Document AI | Production Systems
Santa Cruz de Tenerife, Spain - 11:06 pm local time
Production track record:
US tax compliance RAG over 50+ IRS documents — 100% local Docker, zero cloud, hybrid retrieval (Recall@5 0.98 benchmarked on a 270-point golden dataset, RAGAS metrics)
Spanish legal form automation — OCR + LLM extraction + n8n workflows reducing manual completion time 75–90%
As AI Tech Lead at Icod Systems — multilingual document classification (IT/ES/EN), LoRA/QLoRA fine-tuning with +25–40% accuracy on domain tasks
Currently AI Engineer in the in-house AI department of a multinational (Verisure – Securitas Direct).
See Portfolio for two public production repos:
fastapi-rag-lab: 9 retrieval configs benchmarked, 79 tests, Langfuse tracing, 6 ADRs
agentic-sql-assistant: LangGraph agent, autonomous tool calling, 138 tests
Best fit if you need:
RAG over your documents (legal, tax, compliance)
Document automation: OCR + LLM extraction + workflow integration
Local/private LLM deployment (Ollama, Qdrant) for data sovereignty
Production discipline: golden datasets, evaluation, observability
Stack: Python, FastAPI, Qdrant, Ollama, OpenAI/Anthropic API, LangGraph, Hugging Face, Docker, n8n, Tesseract/PaddleOCR.
Native Spanish, professional English. Available 15–25 hrs/week for long-term engagements. Based in Tenerife (UTC+0/+1, full EU overlap, US morning overlap).
Steps for completing your project
After purchasing the project, send requirements so Eduardo can start the project.
Delivery time starts when Eduardo receives requirements from you.
Eduardo works on your project following the steps below.
Revisions may occur after the delivery date.
Review your example documents and define extraction schema
Build OCR + extraction pipeline for your document types