You will get AI-Powered RAG Chatbot for Real-Time Answers from Your Knowledge Base
Project details
I will build a custom Generative AI chatbot using RAG (Retrieval-Augmented Generation) that connects to your documents (PDFs, SOPs, websites) and provides intelligent, real-time answers. Ideal for customer support, internal tools, and knowledge bases. I focus on scalable, secure, and production-ready solutions using OpenAI, LangChain, and vector databases like FAISS, or Milvus.
AI Algorithms
Generative Adversarial Network, Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, AI Text-to-Image, AI-Generated Code, AI-Generated Music, Conversational AI, Natural Language Generation, Natural Language Understanding, Neural Machine Translation, Sentiment Analysis, Sequence Modeling, Text RecognitionAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Gradio, Hugging Face, Microsoft 365 Copilot, Microsoft CNTK, NVIDIA AI Platform, PyTorch, Streamlit, TensorFlowAI Models
BERT, ChatGPT, DALL-E, GPT-3, GPT-4, GPT-Neo, LLaMA, Midjourney AIWhat's included
| Service Tiers |
Starter
$150
|
Standard
$400
|
Advanced
$1,000
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 10 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 |
Frequently asked questions
About KHIZAR
Generative AI Engineer - AI Chatbot, RAGs, LLMs, Agentic AI, Langchain
Lahore, Pakistan - 5:08 am local time
I am a Senior Generative AI Engineer with a number of years of experience in building full-stack AI solutions across telecom, healthcare, fintech, retail, and cybersecurity. I hold a Master’s in Data Science and multiple Microsoft Azure certifications. My expertise lies in designing, deploying, and optimizing intelligent Agentic AI systems that solve complex business problems from LLM-powered search pipelines to AI copilots and multimodal GenAI applications.
I have delivered the following 5+ projects, including:
* Building a GenAI-powered customer support assistant (LangChain + OpenAI) that cut ticket resolution time by 45%
* Leading an agentic chatbot for educators, saving 4+ hours daily through automation and smart memory
* Engineering a 900 GB RAG pipeline using Milvus, Azure Search, and LlamaIndex for high-volume document QA
* Creating a document intelligence solution that extracted structured insights from financial PDFs with 90%+ accuracy
What I Offer:
* Full-stack GenAI systems: LangChain, Autogen, CrewAI, LangGraph, RAG, LLM agents
* Custom LLM integrations using OpenAI, Hugging Face, Mistral, and Azure OpenAI
* Vector DBs and semantic search: Milvus, FAISS, Pinecone, Azure Cognitive Search
* Deployment with Docker, Kubernetes, FastAPI, Flask, and CI/CD using GitHub Actions
* Conversational AI: intelligent chatbots, semantic memory, context-aware Q&A
* Document AI: OCR, unstructured document parsing, structured extraction
* Multimodal GenAI: combining text, vision, and embeddings for advanced applications
* Performance optimization: LoRA, QLoRA, quantization, pruning, distillation
Tech Stack:
* LLMs & GenAI: LangChain, OpenAI SDK, LlamaIndex, RAG, LangGraph, Hugging Face, Transformers
* Backend: Python, FastAPI, Flask, Docker, Kubernetes, CI/CD (GitHub Actions, Jenkins)
* Databases: MongoDB, PostgreSQL, SQL, NoSQL
* Cloud: Azure (AI Search, ML Studio), AWS (SageMaker, EC2, S3), GCP
* Vector Search: Milvus, FAISS, Pinecone
* Data Engineering: Spark, Dask, ELT pipelines, Azure Databricks
* ML Libraries: PyTorch, TensorFlow, Scikit-learn, spaCy, NLTK, OpenCV
* DevOps: MLflow, Airflow, GitHub, Bitbucket
* Tools: VS Code, Jupyter, Postman, Anaconda, Colab
Certifications:
* Microsoft Azure AI & Data Fundamentals
* Deep Learning Specialization – DeepLearning.ai
* MLOps Specialization – DeepLearning.ai
* Machine Learning – University of Washington
* Data Science – University of Michigan
If you are looking for a Generative AI expert who can take your vision from idea to production with efficiency, scalability, and innovation, let’s work together.
Steps for completing your project
After purchasing the project, send requirements so KHIZAR can start the project.
Delivery time starts when KHIZAR receives requirements from you.
KHIZAR works on your project following the steps below.
Revisions may occur after the delivery date.
Review client requirements and sample documents
Analyze documents and define the use case, target audience, and chatbot goals.
Build and test the RAG pipeline
Set up embedding, vector DB, retriever, and LLM integration using tools like LangChain or custom pipelines.