You will get RAG System with OpenAI & Vector Database
Project details
Transform your documents into an intelligent Q&A system using RAG
(Retrieval Augmented Generation). Upload PDFs, documents, or knowledge
bases - the system will answer questions accurately using semantic search
and LLMs. Get a web dashboard, API access, analytics, and 10 days support.
Perfect for customer support automation, research document analysis, or
internal knowledge management. Accurate, fast, and scalable.
(Retrieval Augmented Generation). Upload PDFs, documents, or knowledge
bases - the system will answer questions accurately using semantic search
and LLMs. Get a web dashboard, API access, analytics, and 10 days support.
Perfect for customer support automation, research document analysis, or
internal knowledge management. Accurate, fast, and scalable.
Data Tool
PythonWhat's included $700
These options are included with the project scope.
$700
- Delivery Time 14 days
- Number of Pages Mined/Scraped 80
- Number of Sources Mined/Scraped 10
- Number of Revisions 1
Frequently asked questions
About Rahul
AI & Full Stack Developer | LLM | RAG | Voice Agents | AI SaaS
Mohali, India - 2:21 pm local time
Retrieval Augmented Generation (RAG), and Voice Agents.
What I Build:
✓ LLM Applications (ChatGPT, Claude, Llama integration)
✓ RAG Systems (Document QA, Knowledge bases, Semantic search)
✓ Voice Agents (Real-time speech-to-text, AI conversations)
✓ AI Chatbots (Multi-turn conversations, context awareness)
✓ Full-Stack SaaS (Node.js backend + Vue.js frontend)
✓ Vector Databases (Pinecone, Weaviate, Milvus integration)
✓ LLM Fine-tuning & Prompt Engineering
Tech Stack:
→ LLM APIs: OpenAI (GPT-4), Anthropic (Claude), HuggingFace
→ Backend: Node.js, Express, Python (FastAPI)
→ Frontend: Vue.js, React
→ AI Tools: LangChain, LlamaIndex, Pinecone, Chroma DB
→ Voice: Deepgram, ElevenLabs, AssemblyAI
→ Database: MongoDB, PostgreSQL, Vector DBs
→ Deployment: AWS, DigitalOcean, Vercel
Recent Projects:
- AI Customer Support Bot (RAG + LLM, 95% accuracy)
- Voice Agent Platform (Real-time speech processing)
- Document QA System (Semantic search + embeddings)
- SaaS Dashboard with AI Features
My Approach:
→ Production-grade AI architectures
→ Cost-optimized LLM usage
→ Fast iteration & testing
→ Clean, documented code
→ Weekly updates & transparent communication
Who I Work With:
✓ Startups building AI-first products
✓ Enterprises integrating AI into existing systems
✓ Projects needing quick AI MVP (2-4 weeks)
✓ Technical teams needing AI architecture guidance
Current Rate: $10-25/hr (negotiable for long-term projects)
Let's build intelligent applications! 🚀
Steps for completing your project
After purchasing the project, send requirements so Rahul can start the project.
Delivery time starts when Rahul receives requirements from you.
Rahul works on your project following the steps below.
Revisions may occur after the delivery date.
Document Analysis
Review documents, plan RAG architecture. Timeline: 1-2 days.
Vector Database Setup
Process documents, create embeddings, setup Pinecone/Weaviate. Timeline: 2-3 days.