You will get an advanced knowledge base chatbot trained on your documents
Project details
You will get an advanced knowledge base chatbot trained on your business documents, PDFs, internal data, or website content to deliver accurate and context-aware responses. I build enterprise-grade RAG-powered chatbots that can understand, retrieve, and generate answers from your private knowledge sources.
This includes document ingestion, chunking, embeddings, vector database setup, semantic retrieval, citation-backed responses, and deployment. Whether you need an internal company assistant, customer support bot, legal document assistant, or healthcare knowledge system, I can build it end-to-end.
With expertise in LangGraph, vector databases, LLMs, and enterprise RAG systems, I create AI assistants capable of indexing thousands of documents with high accuracy and scalable performance.
This includes document ingestion, chunking, embeddings, vector database setup, semantic retrieval, citation-backed responses, and deployment. Whether you need an internal company assistant, customer support bot, legal document assistant, or healthcare knowledge system, I can build it end-to-end.
With expertise in LangGraph, vector databases, LLMs, and enterprise RAG systems, I create AI assistants capable of indexing thousands of documents with high accuracy and scalable performance.
AI Algorithms
Autoencoder, Convolutional Neural Network, Feedforward Neural Network, Generative Adversarial Network, Large Language Model, Linear Discriminant Analysis, Multilayer Perceptron, Multimodal Large Language Model, Recurrent Neural Network, YOLOAI Applications
AI Chatbot, AI Mobile App Development, AI-Generated Code, AIOps, Conversational AI, Facial Recognition, Machine Translation, Natural Language Generation, Natural Language Understanding, Neural Machine Translation, Object Localization, Synthetic Data GenerationAI Development Language
PythonAI Tools
Adobe Firefly, Azure OpenAI, Bing AI, Copy.ai, GitHub Copilot, Gradio, Hugging Face, Jasper AI, Microsoft 365 Copilot, NVIDIA AI PlatformAI Models
AlphaCode, BERT, BLOOM, ChatGPT, DALL-E, GPT-3, GPT-4, GPT-J, GPT-Neo, LaMDA, LLaMA, OpenAI CodexWhat's included
| Service Tiers |
Starter
$1,200
|
Standard
$2,400
|
Advanced
$4,000
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 10 days |
Number of Revisions | 2 | 3 | 4 |
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: Document Collection
Gather and analyze your knowledge sources, PDFs, and business documents
Step 2: Data Processing
Chunk, clean, and prepare documents for vector embedding