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I will build a production ready rag system to chat with your documents using llm
Custom NLP, RAG and LLM Systems Built for Production, Not Just Demos
Most AI projects fail because they never move past demos I build ones that actually work.
I create NLP pipelines, RAG systems, AI agents, and automations for real business use document processing, se...
About this Gig
Want to ask questions directly to your PDFs, documents, or
internal data and get accurate, sourced answers instantly?
I build production-ready RAG systems that connect your private
documents to a powerful LLM so your team gets precise,
hallucination-free answers from YOUR data, not generic AI guesses.
WHAT I BUILD:
Custom RAG Pipeline (end-to-end, fully documented)
Document Ingestion (PDF, Word, Excel, CSV, Notion, URLs)
Vector Database Setup (FAISS, Pinecone, Chroma)
Semantic + Hybrid Retrieval (BM25 + dense vectors)
LLM Integration (GPT-4, Claude, LLaMA, Mistral)
Conversational Memory & Source Citation
FastAPI / Streamlit UI + Docker deployment
PERFECT FOR:
Businesses querying internal knowledge bases
Legal, healthcare & finance document teams
SaaS founders building AI knowledge products
Most RAG tools are demos. I build systems that work in
production with proper chunking, re-ranking, and
hallucination reduction from day one.
Message me BEFORE ordering I'll confirm the right
architecture for your specific use case.
Frameworks:
Scikit-learn
•
Google ML Kit
•
Keras
•
PyTorch
•
Panda
Data type:
Text
Programming language:
Python
•
SQL
•
Colab
•
NoSQL
•
MLflow
My Portfolio
Other Data Science & ML Services I Offer
FAQ
What types of documents can the RAG system handle?
The system can ingest PDFs, Word documents (DOCX), Excel spreadsheets, CSVs, plain text files, Markdown files, Notion exports, and web URLs. For the Standard and Premium packages, I build a multi-format ingestion pipeline that handles all these types in one unified system.
Will the RAG system hallucinate or make up answers?
This is exactly what RAG is designed to prevent. Unlike standard LLMs that generate answers from training data alone, my RAG systems retrieve actual passages from your documents first and then generate answers grounded in that retrieved content.
Do I need my own OpenAI or LLM API key?
For cloud-based LLMs like GPT-4 or Claude, yes - you will need your own API key (billed directly by OpenAI/Anthropic at their standard rates). I can also build the system using fully open-source, locally-run models like LLaMA or Mistral that require no API key and have zero ongoing cost.
How many documents can the system handle?
The Standard package is optimized for up to 500 documents or roughly 50MB of text content. The Premium package uses scalable vector databases (Pinecone or Weaviate) that can handle millions of documents and grow with your needs.
Will I be able to maintain and update the system myself?
Yes - that's a core part of every delivery. You receive clean, well-commented Python code, a detailed README, and step-by-step instructions for adding new documents, updating the knowledge base, and deploying updates.
Can you integrate the RAG system into my existing website or app?
Yes. Every Standard and Premium delivery includes a FastAPI backend with REST endpoints, which means the RAG system can be integrated into any existing application - web app, mobile app, Slack bot, customer support tool, or internal dashboard.
