You will get a private, self-hosted LLM with RAG for your team
Project details
I am an AI Engineer focused on building production-ready RAG / Retrieval-Augmented Generation systems and Knowledge Base AI solutions that turn business documents into reliable, conversational intelligence.
I design Custom GPT and LLM-powered applications that allow companies to query their internal data using natural language. My work focuses on building accurate, scalable RAG pipelines powered by modern LLMs and clean architecture.
I use frameworks like LangChain, LlamaIndex, LangGraph, and CrewAI to design robust AI workflows, and integrate vector databases such as Pinecone, pgvector, Weaviate, Qdrant, ChromaDB, and FAISS for fast and accurate semantic search.
My strength is not just connecting tools, but engineering full systems that are reliable, production-ready, and easy to extend. Every solution is built with strong retrieval logic, structured data flow, and real business use in mind.
If you need a Custom GPT, RAG system, or Knowledge Base AI solution built with modern LLM stacks, I can design and deploy it end-to-end with a focus on accuracy, performance, and scalability.
I design Custom GPT and LLM-powered applications that allow companies to query their internal data using natural language. My work focuses on building accurate, scalable RAG pipelines powered by modern LLMs and clean architecture.
I use frameworks like LangChain, LlamaIndex, LangGraph, and CrewAI to design robust AI workflows, and integrate vector databases such as Pinecone, pgvector, Weaviate, Qdrant, ChromaDB, and FAISS for fast and accurate semantic search.
My strength is not just connecting tools, but engineering full systems that are reliable, production-ready, and easy to extend. Every solution is built with strong retrieval logic, structured data flow, and real business use in mind.
If you need a Custom GPT, RAG system, or Knowledge Base AI solution built with modern LLM stacks, I can design and deploy it end-to-end with a focus on accuracy, performance, and scalability.
AI Algorithms
Convolutional Neural Network, CycleGAN, Large Language Model, Long Short-Term Memory Network, Recurrent Neural Network, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, AI Text-to-Image, AI Text-to-Speech, AI-Generated Art, AI-Generated Code, Conversational AI, Facial RecognitionAI Development Language
PythonAI Tools
Azure OpenAI, Hugging Face, Microsoft 365 Copilot, PyTorch, Replit, Streamlit, TensorFlowAI Models
BERT, ChatGPT, DALL-E, GPT-4, LLaMA, OpenAI CodexWhat's included $1,200
These options are included with the project scope.
$1,200
- Delivery Time 7 days
- Number of Revisions 1
- AI Model Integration
- Batch Normalization
- Database Integration
- MLOps
- Natural Language Processing
- Prompt Engineering
- Setup File
About Muhammad
AI Engineer | RAG & Knowledge Base AI | LangChain, Custom GPTs, LLMs
Lahore, Pakistan - 5:40 am local time
I build LangChain-powered Custom GPTs and LLM integrations that turn company documents,
internal wikis, support tickets, and SharePoint into accurate, source-cited AI assistants that SaaS
and enterprise teams actually trust.
Most of my AI Engineer work is for mid-market product teams who tried a generic ChatGPT or
Custom GPT and got hallucinations, no citations, and zero control over their data. They come to
me for a production RAG system instead - one that uses hybrid search, returns answers
grounded in their knowledge base, and ships with proper evaluation so they can measure
accuracy instead of guessing.
What I build as an AI Engineer:
• Custom RAG chatbots and Knowledge Base AI over Notion, Confluence, SharePoint, Google Drive,
S3, or any custom data source
• Custom GPTs and LangChain-powered AI assistants embedded directly into your product, Slack,
web app, or internal tools
• Document-aware AI agents with hybrid search (vector + keyword + graph) - significantly higher
answer accuracy than vanilla embedding retrieval
• Private and self-hosted LLM deployments using open-source LLMs (Llama, Mistral, Mixtral) for
clients with data residency or compliance requirements
• LangGraph multi-agent workflows for business logic that a single LLM prompt cannot handle
reliably
• Production observability for RAG pipelines - answer-quality metrics, hallucination detection,
retrieval evaluation, and cost monitoring across LLM calls
Tech stack I default to as an AI Engineer:
• LLMs: OpenAI GPT-4o and GPT-4 Turbo, Anthropic models, Llama 3, Mistral, Mixtral, and open-
source LLMs for self-hosted deployments
• RAG frameworks: LangChain, LlamaIndex, LangGraph, CrewAI
• Vector databases: Pinecone, pgvector, Weaviate, Qdrant, ChromaDB, FAISS
• Embeddings: OpenAI text-embedding-3, BGE, Cohere, custom fine-tuned embedding models
• Backend: Python (FastAPI), Node.js
• Frontend: React, Next.js
• Infra: AWS, GCP, Azure, self-hosted Docker / Kubernetes
How I work:
• Fixed price for scoped RAG and Knowledge Base AI builds, hourly for ongoing LangChain and
LLM engagements
• Daily written updates while a project is live; a short Loom walkthrough at every milestone
• Adversarial testing on every delivery - no RAG pipeline ships without it
• I respond within 4 working hours during business days
If you have a pile of documents, a Knowledge Base AI problem, or a Custom GPT that is
hallucinating its way through customer queries - send me a short note describing the pain. I will
reply with a concrete first-week plan for the smallest version of the RAG or LLM build that proves
it works, with a fixed price you can decide on the same day.
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 your documents and requirements.
Build and train the RAG chatbot on your knowledge base.