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English
$
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I will build custom rag ai agents, chatbots and knowledge systems
Tayyab E.
Top Rated
About this gig
Build a production-ready RAG system that transforms your documents, website, database, or internal knowledge into an intelligent AI assistant that can retrieve, reason, and respond with context-aware answers.
I design custom Retrieval-Augmented Generation systems, AI agents, and knowledge-base chatbots with a strong focus on accuracy, retrieval quality, scalability, and maintainable architecture.
What I can build:
- RAG chatbots and AI knowledge assistants
- AI agents with tools, memory, and retrieval
- PDF, website, database, API, CSV, and Excel ingestion
- Vector search with Pinecone, Qdrant, Chroma, or pgvector
- OpenAI, Claude, Gemini, Llama, DeepSeek, and local LLM integration
- Hybrid search, metadata filtering, reranking, and citations
- FastAPI / backend integration and custom APIs
- Web scraping and automated knowledge ingestion
- Private or self-hosted RAG deployments
- Retrieval evaluation, testing, and optimization
With 7+ years of Backend and System Design, automation, scraping, and data engineering experience, I build more than basic chatbot wrappers -- I deliver robust AI systems around your real business data.
Message me before ordering for the best architecture and package fit.
Get to know Tayyab E.
Tayyab E.
Turn Any Website Into Structured Data
Top Rated
- FromPakistan
- Member sinceMar 2019
- Avg. response time1 hour
- Last delivery2 weeks
Languages
Urdu, English
Hi! I’m Tayyab Elahi, a web scraping and data extraction expert with 7+ years of experience. I help businesses collect, clean, and structure data from websites using reliable scraping, automation, and scalable data workflows. From one-time data extraction to large-scale and recurring scraping projects, I focus on accurate results, clean delivery, and solutions built around your specific requirements.
My Portfolio
FAQ
What is a RAG system?
RAG, or Retrieval-Augmented Generation, allows an AI model to retrieve relevant information from your own data before answering. It is useful for assistants that need access to documents, websites, product information or internal business knowledge.
What data can I use?
PDFs, Word documents, spreadsheets, websites, databases, APIs, product catalogs, help-center articles and other structured or unstructured data can be used.
Can you scrape my website and use the data for RAG?
Yes. Web scraping and structured data extraction are core areas of my work. I can build both the data acquisition pipeline and the RAG system
Which vector databases do you work with?
Depending on the project, I can work with Pinecone, Qdrant, Chroma, PostgreSQL/pgvector and other suitable vector storage solutions.
Do you use LangChain?
Yes, where it makes sense. I can use LangChain, LangGraph or custom Python implementations depending on the architecture and complexity of the application
Can you use OpenAI, Claude or Gemini?
Yes. The RAG architecture can integrate with OpenAI, Claude, Gemini or compatible/open source LLMs depending on your requirements.
Can the chatbot provide citations?
Yes. The system can return source documents, URLs, document names or relevant passages alongside generated answers where the data architecture supports it.
Can RAG completely eliminate hallucinations?
No RAG implementation can guarantee zero hallucinations. However, retrieval quality, prompt design, metadata filtering, reranking, citations and response constraints can significantly improve reliability.
Can you build a private or locally hosted RAG system?
Yes. If the requirements call for it, I can design systems using self-hosted vector databases and local/open-source models.
Can you connect the RAG system to my website or application?
Yes. I can expose the RAG pipeline through an API or integrate it into an existing application depending on your stack.
