You will get a custom RAG chatbot for your documents using Node.js and Next.js
Project details
Turn your PDFs, policies, manuals, reports, or internal documents into a searchable AI assistant.
I will build a custom RAG chatbot in JavaScript and TypeScript using Node.js, Next.js, LangChain.js, an agreed LLM provider, and a suitable vector database. Depending on your package, the solution can include document ingestion, semantic search, grounded answers, source references, a responsive chat interface, authentication, persistent chat history, document management, and deployment.
This project is suitable for internal knowledge bases, customer support, product documentation, employee handbooks, education, and research.
You will receive the source code, setup files, and documentation included in your selected package. API usage, hosting, and third-party service fees are not included.
Please contact me before ordering if you need complex integrations, scanned PDF OCR, unusual file formats, or integration into an existing application.
I will build a custom RAG chatbot in JavaScript and TypeScript using Node.js, Next.js, LangChain.js, an agreed LLM provider, and a suitable vector database. Depending on your package, the solution can include document ingestion, semantic search, grounded answers, source references, a responsive chat interface, authentication, persistent chat history, document management, and deployment.
This project is suitable for internal knowledge bases, customer support, product documentation, employee handbooks, education, and research.
You will receive the source code, setup files, and documentation included in your selected package. API usage, hosting, and third-party service fees are not included.
Please contact me before ordering if you need complex integrations, scanned PDF OCR, unusual file formats, or integration into an existing application.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, Conversational AI, Natural Language Generation, Natural Language Understanding, Text RecognitionAI Tools
Azure OpenAI, Hugging FaceAI Models
BERT, ChatGPT, GPT-4, LLaMA, WhisperWhat's included
| Service Tiers |
Starter
$195
|
Standard
$495
|
Advanced
$995
|
|---|---|---|---|
| Delivery Time | 5 days | 8 days | 14 days |
Number of Revisions | 1 | 2 | 2 |
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 |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$75 - $250
Additional Revision
+$50
Cloud Deployment
(+ 2 Days)
+$100
Additional document source
(+ 2 Days)
+$100Frequently asked questions
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MA
Muhammad A.
Jul 30, 2026
Next.js Website Redesign & Content Management Dashboard (Fixed Price)
Working with Muhammad Faizan on the our website was an amazing experience. He completed the full-stack project using Next.js, Tailwind CSS, Drizzle, and PostgreSQL in just two days, and the final result exceeded my expectations.
I was especially impressed by his communication, professionalism,, and attention to detail. He understood the requirements clearly and delivered well-built website. I’m extremely happy with his work and highly recommend him!
I was especially impressed by his communication, professionalism,, and attention to detail. He understood the requirements clearly and delivered well-built website. I’m extremely happy with his work and highly recommend him!
About Muhammad
AI Agent Developer| LangGraph, MCP, RAG | Node.js & Next.js
Sargodha, Pakistan - 10:55 am local time
⭐ I build AI agents, RAG systems, and full stack AI applications that connect seamlessly with your documents, APIs, databases, and internal tools. My approach is centered on creating structured workflows where users can search information, get accurate answers, and automate tasks through a clean and intuitive interface.
✔ My expertise includes:
• AI Agent Development using LangGraph with multi step workflows, tool calling, and memory handling
• RAG Chatbots and Document AI with vector databases like Qdrant and pgvector for accurate knowledge retrieval
• MCP server development to connect AI assistants with APIs, databases, and business systems
• LLM integration using OpenAI, Claude, DeepSeek with structured outputs and prompt optimization
• Full stack AI applications using Node.js, Next.js, React with secure and scalable architecture
• AI powered search systems with embeddings, metadata filtering, and private data indexing
I work with modern tools and technologies including LangChain, LangGraph, Node.js, Next.js, React, PostgreSQL, MongoDB, Qdrant, Docker, and cloud platforms. I also implement advanced techniques like prompt engineering, retrieval optimization, agent orchestration, and AI workflow automation to ensure reliable and efficient performance.
Whether you need a complete AI product, a RAG pipeline, an AI agent, or integration into an existing system, I deliver solutions that are scalable, maintainable, and aligned with your business goals. My focus is always on building systems that are practical, efficient, and ready for real world use.
AI Agent Developer LangGraph MCP RAG Developer Node.js Next.js AI Chatbot Development LLM Integration OpenAI Claude DeepSeek Vector Database Qdrant pgvector AI Automation Full Stack AI Developer Prompt Engineering AI Workflow Automation AI SaaS Development Document AI Knowledge Base AI API Integration
Steps for completing your project
After purchasing the project, send requirements so Muhammad can start the project.
Delivery time starts when Muhammad receives requirements from you.
Muhammad works on your project following the steps below.
Revisions may occur after the delivery date.
Review requirements and define the architecture
I review your documents, goals, expected user flow, selected package, and required integrations. I then define the document ingestion, retrieval, application, database, and deployment structure.
Build document ingestion and retrieval
I implement document processing, text extraction, chunking, embeddings, vector storage, and semantic retrieval for the agreed document formats and sample data.