You will get Multilingual Enterprise Data Assistant (Local & Secure)
Project details
Give your team instant, private answers from your own documents.
I build self-hosted RAG systems that let your team ask questions in plain language and get accurate answers from your company's documents — running entirely on your own infrastructure, so your data never leaves your network and there are no per-query API costs.
What you get:
A private RAG system running on your own server (no data sent to external APIs like OpenAI)
Multilingual support
OCR for scanned and image-based documents, with automatic quality detection
Source-cited answers: every response shows which document and page it came from
Grounded responses drawn strictly from your documents — dramatically reducing hallucination
Clean web interface and Docker deployment
Tech stack: Python, LangChain, ChromaDB, self-hosted LLMs via Ollama (Qwen/Llama), OCR.
Ideal for: companies in privacy-sensitive or regulated fields (legal, healthcare, finance) that can't send confidential documents to cloud AI services.
Note: This system requires a GPU server (24GB+ VRAM). Please message me before ordering to confirm your environment is a good fit.
Message me with a few sample documents and I'll tell you exactly what's possible.
I build self-hosted RAG systems that let your team ask questions in plain language and get accurate answers from your company's documents — running entirely on your own infrastructure, so your data never leaves your network and there are no per-query API costs.
What you get:
A private RAG system running on your own server (no data sent to external APIs like OpenAI)
Multilingual support
OCR for scanned and image-based documents, with automatic quality detection
Source-cited answers: every response shows which document and page it came from
Grounded responses drawn strictly from your documents — dramatically reducing hallucination
Clean web interface and Docker deployment
Tech stack: Python, LangChain, ChromaDB, self-hosted LLMs via Ollama (Qwen/Llama), OCR.
Ideal for: companies in privacy-sensitive or regulated fields (legal, healthcare, finance) that can't send confidential documents to cloud AI services.
Note: This system requires a GPU server (24GB+ VRAM). Please message me before ordering to confirm your environment is a good fit.
Message me with a few sample documents and I'll tell you exactly what's possible.
AI Development Type
Deep LearningAI Tools
PyTorchAI Development Language
PythonWhat's included $1,000
These options are included with the project scope.
$1,000
- Delivery Time 20 days
- Number of Revisions 2
- AI Model Integration
- Detailed Code Comments
- Model Documentation
- Source Code
Optional add-ons
You can add these on the next page.
On-Server Deployment & Setup
(+ 3 Days)
+$400Frequently asked questions
About Merve
AI Document Processing & RAG Engineer - PDF to Structured Data
Izmir, Turkey - 1:01 am local time
What makes my approach different:
• VISION AI, NOT JUST OCR. I use modern vision language models that "see" your document as a whole — layout, tables, handwriting, mixed formats. This works where traditional OCR breaks down: messy scans, handwritten fields, complex table structures.
• DUAL-MODEL CROSS-CHECK. Every extraction is independently verified by a second AI model from a different family. When the two models disagree on a field, the system flags it for human review instead of silently passing a wrong value. This catches the most dangerous error in document AI: confident-looking but incorrect data.
• HONEST, NOT PERFECT. The system tells you what it could not read, rather than guessing. Missing fields are flagged. Disputed fields show both readings. You review only the flagged items — not every field.
• PRIVACY-READY. Everything can run on your own infrastructure — no data leaves your network. Useful for confidential documents (contracts, financials, HR records). Cloud deployment is also available if you prefer.
I also build RAG systems (document Q&A): upload your documents, ask questions in plain language, get answers with source citations. Same privacy options, same honest "I don't know" when the answer isn't there.
Typical projects:
Document extraction: → Extract data from 500 invoices into one Excel spreadsheet → Pull party names, dates, and amounts from scanned contracts → Process application forms and deliver structured data in Excel
Document Q&A (RAG): → Build a searchable assistant over internal policy documents.
Steps for completing your project
After purchasing the project, send requirements so Merve can start the project.
Delivery time starts when Merve receives requirements from you.
Merve works on your project following the steps below.
Revisions may occur after the delivery date.
Share sample documents
Client shares sample documents and environment details; I confirm the setup fits.
Setup the system
I set up the RAG system, process your documents (with OCR for scanned files), and configure multilingual retrieval.