You will get a production-ready AI-powered application built with RAG and LLMs
Project details
I build production-grade AI-powered applications — not just prototypes. With 2+ years of hands-on experience in Gen AI and backend engineering, I specialize in RAG pipelines, multi-agent systems, LLM integration, and scalable APIs that real users depend on.
What sets me apart is that I think deeply about engineering tradeoffs — from designing custom heuristic algorithms that cut LLM costs by 95%, to building LangGraph multi-agent architectures that coordinate intelligently. I don't just call an LLM API and call it a day. I architect the full system around it.
Whether you need a document Q&A chatbot, a codebase intelligence tool, an AI-powered search system, or a custom LLM-integrated backend — I will design, build, and deploy it end-to-end with clean code, proper documentation, and production-ready infrastructure.
What sets me apart is that I think deeply about engineering tradeoffs — from designing custom heuristic algorithms that cut LLM costs by 95%, to building LangGraph multi-agent architectures that coordinate intelligently. I don't just call an LLM API and call it a day. I architect the full system around it.
Whether you need a document Q&A chatbot, a codebase intelligence tool, an AI-powered search system, or a custom LLM-integrated backend — I will design, build, and deploy it end-to-end with clean code, proper documentation, and production-ready infrastructure.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, AI Mobile App Development, AI Text-to-Image, AI Text-to-Speech, AI-Enhanced Classification, AI-Enhanced Medical Imaging, AI-Generated Art, AI-Generated Code, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
GitHub Copilot, Hugging Face, PyTorch, TensorFlowAI Models
ChatGPT, DALL-E, GPT-3, GPT-4, GPT-Neo, LLaMA, OpenAI CodexWhat's included
| Service Tiers |
Starter
$150
|
Standard
$400
|
Advanced
$800
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 14 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Batch Normalization | |||
Database Integration | |||
Detailed Code Comments | |||
Image Upscaling | |||
MLOps | - | ||
Model Deployment | |||
Model Documentation | - | ||
Model Monitoring | - | ||
Model Testing & Optimization | - | ||
Model Tuning | |||
Natural Language Processing | |||
NLP Tokenization | |||
Pre-Training | |||
Prompt Engineering | |||
Setup File | - | ||
Source Code |
Frequently asked questions
About Vivek
AI Fullstack Engineer
Haridwar, India - 3:27 pm local time
I don't just integrate AI into apps. I architect it. From designing RAG pipelines that retrieve context intelligently, to building LangGraph multi-agent systems that coordinate multiple AI agents, to optimizing LLM costs by 95% through smart data selection — I think deeply about how AI fits into the full product.
What I build:
🤖 Gen AI & LLM Integration
RAG pipelines with semantic search using Qdrant, FAISS, Pinecone
Multi-agent systems with LangChain and LangGraph
LLM integration with OpenAI, Groq, Anthropic, HuggingFace
Prompt engineering, chain-of-thought, and agent flows
Vector embeddings with fastembed, HuggingFace, and ONNX Runtime
Chatbots, code assistants, and document intelligence systems
⚙️ Backend
REST APIs with FastAPI and Django
Background job queues with Redis and RQ
Authentication with JWT and OAuth
PostgreSQL, SQLite, SQLAlchemy
Docker, Docker Compose, deployment on Render and Vercel
🖥️ Frontend
React with clean, responsive UI
Tailwind CSS for modern styling
Real-time updates and async job polling
What I've shipped:
✅ Cortex — an AI-powered codebase intelligence system. Users paste a GitHub URL and get an instant summary plus a conversational Q&A interface over the entire codebase. Built with FastAPI, LangGraph, Qdrant, and React. Reduced LLM API costs by 95% through a custom heuristic file-scoring algorithm.
✅ Prediction Market Platform — co-founded and engineered a Polymarket-style Web3 platform on an Ethereum-compatible chain. Grew to 2,000+ organic users and hit a $500K peak market cap with a team of 4 and near-zero marketing budget.
✅ Non-Custodial Crypto Wallet — built end-to-end over 2.5 years handling key management, transaction signing, and multi-chain support serving 100+ users.
If you need an AI-powered product built right — not just a prototype but something that scales, performs, and ships — let's talk.
Steps for completing your project
After purchasing the project, send requirements so Vivek can start the project.
Delivery time starts when Vivek receives requirements from you.
Vivek works on your project following the steps below.
Revisions may occur after the delivery date.
1
Understand your requirements, data sources, and expected outputs through a detailed discussion.
2
Design the system architecture — pipelines, agent flows, APIs, and data storage.