Aqdas A.
RAG Expert | LLM + Vector DB Engineer | Secure On-Premise AI
I build private Retrieval-Augmented Generation (RAG) systems, AI-powered search and Q&A engines that run entirely on your own servers, cloud VPC, or air-gapped environment. No data sent to third-party APIs. No compliance risks. Just fast, accurate answers from your own documents. What clients typically come to me for: 1. An internal chatbot that answers questions from your PDF manuals, contracts, or wikis 2. A secure document search system for healthcare, legal, or finance that can't use public AI APIs 3. Replacing slow keyword search with smart semantic search across thousands of files 4. Connecting an LLM to your existing databases, SharePoint, Notion, or Confluence 5. An on-premise AI assistant your whole team can use without sharing any data externally What I bring to your project: 1. Full-stack RAG pipeline covering chunking, embedding, vector storage, retrieval, and response 2. generation 3. Tech stack: LangChain Ā· LlamaIndex Ā· Ollama Ā· FastAPI Ā· Pinecone Ā· Weaviate Ā· ChromaDB Ā· pgvector Ā· OpenAI Ā· Mistral Ā· LLaMA 4. Privacy-first architecture using self-hosted models (Ollama, vLLM) or private API deployments 5. Production-ready delivery with Docker, CI/CD, monitoring, and documentation included I take the time to understand your actual use case, not just your tech stack. Most projects start with a short discovery call where I ask about your data sources, team size, privacy requirements, and what "good answers" looks like to you. Send me a message describing your data and what you want users to be able to ask. I'll tell you within 24 hours what's possible and how long it would take.