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I will build a rag chatbot or ai knowledge base
Eshban Salman
Acerca de este Servicio
Your LLM only knows what it was trained on. A well-built RAG system makes it an expert on your data too without hallucination.
I design and deploy production-grade Retrieval-Augmented Generation pipelines that let your team or customers query documents, databases, and knowledge bases in plain English with every answer grounded in your actual content and backed by source citations.
My background includes building semantic search at 10M+ document scale (Traindex) achieving 95%+ retrieval accuracy, and implementing RAG systems for patent intelligence, enterprise Q&A, and government platforms. I don't just follow tutorials I've solved the hard problems: chunking strategy, reranking, hybrid search, and hallucination control at scale.
You get clean, documented, production-ready code not a Jupyter notebook.
Conoce a Eshban Salman
Eshban Salman
AI and Data Science Expert
- DePakistán
- Miembro desdeago 2017
- Responde aprox. en:1 hora
Idiomas
Inglés
AI Lead with 7+ years building production AI systems for governments, banks, and telecoms. Currently deploying AI agents for Qatar's national smart city platform (TASMU), processing 1,000+ weekly queries at 99.9% uptime.
I specialize in LLM agents, multi-agent systems, RAG pipelines, semantic search, and predictive ML — across AWS, GCP, and Azure.
Past work includes a $124M cost-saving optimization system and credit models outperforming industry benchmarks by 18%.
You get production-grade code, full documentation, and someone who's actually shipped at scale.
FAQ
What exactly is a RAG system and why do I need one?
RAG (Retrieval-Augmented Generation) lets an LLM answer questions using your own documents instead of relying on general training data. It eliminates hallucination by grounding every answer in your actual content — with citations.
How do you handle hallucination?
Through a combination of retrieval grounding, source citation, cross-encoder reranking to surface the most relevant context, and a faithfulness evaluation step that flags answers not supported by retrieved documents.
Can you handle large document collections?
Yes. I've built and deployed RAG systems over 10M+ patent documents at Traindex with 95%+ accuracy. I'll design the chunking, indexing, and retrieval strategy to match your scale.
Will I own the source code?
Yes! Full, commented source code, architecture documentation, and a deployment guide are included in every tier.
Can you integrate the chatbot into my existing website or app?
The Standard and Premium tiers include a FastAPI backend with a clean REST interface that can be integrated into any frontend. UI development is available as a custom add-on.
Do you sign NDAs?
Yes, happy to sign before kickoff. Your documents, data, and business logic remain strictly confidential.
