You will get an enterprise-grade RAG pipeline with vector database integration
Project details
You will get an enterprise-grade RAG (Retrieval-Augmented Generation) pipeline built for accurate, context-aware AI responses using your business data. I create scalable retrieval systems with vector databases like Pinecone, Weaviate, and pgvector to power AI assistants, search systems, and knowledge bots.
This includes document ingestion, chunking, embeddings, semantic search, metadata filtering, and LLM integration for real-time responses. The system is optimized for speed, accuracy, and scalability.
Whether you need an internal knowledge assistant, customer support bot, legal document search, or research assistant, I can build a production-ready RAG solution tailored to your business.
This includes document ingestion, chunking, embeddings, semantic search, metadata filtering, and LLM integration for real-time responses. The system is optimized for speed, accuracy, and scalability.
Whether you need an internal knowledge assistant, customer support bot, legal document search, or research assistant, I can build a production-ready RAG solution tailored to your business.
AI Algorithms
Autoencoder, Convolutional Neural Network, Feedforward Neural Network, Generative Adversarial Network, Large Language Model, Linear Discriminant Analysis, Multimodal Large Language Model, Recurrent Neural Network, Regression Analysis, Transformer ModelAI Applications
AI Chatbot, AI-Generated Code, Anomaly Detection, Conversational AI, Facial Recognition, Natural Language Generation, Natural Language Understanding, Neural Machine Translation, Neural Style Transfer, Sequence Modeling, Synthetic Data Generation, Text RecognitionAI Development Language
PythonAI Tools
Azure OpenAI, Bing AI, Copy.ai, GitHub Copilot, Hugging Face, Microsoft 365 Copilot, NVIDIA AI Platform, PyTorch, Streamlit, TensorFlowAI Models
BLOOM, ChatGPT, DALL-E, GPT-3, GPT-4, GPT-J, GPT-Neo, LLaMA, Midjourney AI, OpenAI Codex, Stable Diffusion, WhisperWhat's included
| Service Tiers |
Starter
$1,200
|
Standard
$2,400
|
Advanced
$10,000
|
|---|---|---|---|
| Delivery Time | 3 days | 6 days | 10 days |
Number of Revisions | 2 | 3 | 5 |
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 | - | - | - |
About Bhupinder Singh
Agentic AI Architect | Multi-Agent Systems | RAG | LLMOps
Johnston, United States - 3:03 pm local time
With 5+ years of AI/ML experience and 30+ successful AI deployments, I specialize in building intelligent systems using LangGraph, CrewAI, AutoGen, AWS Bedrock, Azure OpenAI, and Vertex AI.
𝗠𝘆 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 𝗰𝗼𝘃𝗲𝗿𝘀 𝘁𝗵𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗔𝗜 𝗹𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲:
✔ Multi-Agent System Design
✔ RAG (Retrieval-Augmented Generation) Pipelines
✔ MCP Server Development & Tool Integration
✔ AI Automation Workflows
✔ LLMOps, Monitoring & Evaluation
✔ AI Governance, Guardrails & Compliance
✔ Enterprise AI Infrastructure on AWS, GCP & Azure
I build AI systems that do more than just chat.
𝗥𝗲𝗰𝗲𝗻𝘁 𝘄𝗼𝗿𝗸 𝗶𝗻𝗰𝗹𝘂𝗱𝗲𝘀:
• Autonomous Loan Underwriting Agent (78% faster decisions)
• Clinical Knowledge Assistant for Hospital Chains (40K+ documents indexed)
• AI Customer Support Automation Platform ($1.2M annual savings)
• Contract Intelligence Platform (90% review time saved)
• Agentic Market Research Systems (85% analyst time saved)
𝗠𝘆 𝘁𝗲𝗰𝗵 𝘀𝘁𝗮𝗰𝗸 𝗶𝗻𝗰𝗹𝘂𝗱𝗲𝘀:
→ LangGraph, CrewAI, AutoGen
→ AWS Bedrock, SageMaker
→ Azure OpenAI
→ Vertex AI
→ Claude, GPT-4o, Gemini, Llama
→ Pinecone, Weaviate, pgvector
→ LangSmith, Arize Phoenix
→ Terraform, CI/CD, MLflow
If you're looking to build autonomous AI agents, enterprise RAG systems, or AI workflow automation, I can help you architect, build, and deploy scalable solutions end-to-end.
Steps for completing your project
After purchasing the project, send requirements so Bhupinder Singh can start the project.
Delivery time starts when Bhupinder Singh receives requirements from you.
Bhupinder Singh works on your project following the steps below.
Revisions may occur after the delivery date.
Step 1: Data Collection
Gather your documents, files, and knowledge sources
Step 2: Data Processing & Embeddings
Chunk, clean, and convert data into vector embeddings