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I will build an ai semantic search engine using rag for your documents or saas
Shahriar Hassan
 I Built a RAG-Powered Search Engine on 1.1 Million Live News Articles — Full Engineering Breakdown _ LinkedIn_kuv6s8.jpg)
About this gig
I will build a production-ready AI semantic search engine using RAG (Retrieval-Augmented Generation), LangChain, and OpenAI GPT, Claude, or any LLM API for your website, SaaS, or business documents.
Tired of users not finding the right answers? I build intelligent search systems that understand meaning not just keywords so your users instantly find accurate results from your documents, database, or knowledge base.
What you get:
- Semantic search engine trained on your data (PDFs, docs, database)
- RAG pipeline with Qdrant or Pinecone vector search
- LLM integration OpenAI, Claude, Gemini, or LLaMA
- LangChain-powered document retrieval and ranking
- Clean REST API backend for seamless integration
- Scalable, production-ready architecture
Perfect for:
- SaaS platforms needing intelligent document search
- Businesses with large knowledge bases or PDF libraries
- Internal enterprise search tools for teams
- Customer support automation with accurate answers
Tech: Python, TypeScript, LangChain, Qdrant, OpenAI API, Claude API, FastAPI
This is not a basic chatbot it is a semantic search engine that understands meaning, context, and intent at scale.
Please contact me to discuss your requirements first
Get to know Shahriar Hassan
Shahriar Hassan
AI Engineer
- FromBangladesh
- Member sinceApr 2026
- Avg. response time1 hour
Languages
Bengali, English, French
I am an AI Engineer and Full-Stack Developer specializing in RAG & LLM Systems. I have experience in full-stack development, database management, and AI/ML algorithm development.
FAQ
What types of data or documents can your AI search engine work with?
I can build AI search systems that work with PDFs, Word documents, text files, websites, databases (PostgreSQL, MongoDB, MySQL), and structured or unstructured data. If your data exists in any readable format, I can build a semantic search engine around it.
How is this different from a regular chatbot or keyword search?
Unlike basic chatbots or keyword search, this system uses RAG (Retrieval-Augmented Generation) and vector embeddings to understand the meaning and context behind a query — not just exact words. This means users get accurate, relevant results even when they phrase questions differently.
Can you integrate the AI search engine into my existing website or SaaS platform?
Yes. I build a clean REST API backend that connects to your existing product regardless of your tech stack. Whether you use React, Next.js, WordPress, or a custom platform, integration is straightforward and documented.
Which LLM API do you use — OpenAI, Claude, or something else?
I work with all major LLM providers including OpenAI GPT, Claude by Anthropic, Gemini, and open-source models like LLaMA and DeepSeek. I recommend the best fit based on your budget, data privacy needs, and performance requirements.
