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$
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I will build an end to end rag knowledge system
Kalachand D
About this gig
Need an AI system that can reliably answer questions from your documents, knowledge base, or internal data?
I build end-to-end Retrieval-Augmented Generation (RAG) systems that connect your data to AI models for grounded, relevant answers.
I can build document ingestion, chunking, embeddings, vector search, metadata filtering, reranking, citations, context handling, evaluation, APIs, and deployment.
RAG systems can be built for internal knowledge bases, documentation, research, customer support, product knowledge, and other domain-specific use cases.
Depending on your requirements, I can also implement private/local processing, guardrails, observability, context engineering, and model routing.
You receive a working RAG systemnot just a prompt or basic chatbot wrapper.
For complex production systems, please contact me before ordering so I can confirm the architecture and scope.
Get to know Kalachand D
Kalachand D
AI Developer
- FromIndia
- Member sinceSep 2026
Languages
English
AI Developer focused on building and optimizing production-ready AI systems, including AI agents, LLM/RAG applications, Voice AI, and intelligent automation. I combine engineering, prompt/context optimization, and rigorous testing to deliver reliable AI solutions.
FAQ
Can you build a RAG system using my existing documents or knowledge base?
Yes. I can build the ingestion, processing, retrieval, and generation pipeline around your existing documents or data sources.
Can you integrate the RAG system into my existing application?
Yes. I can integrate the RAG backend through APIs with your existing web application, AI application, or internal platform.
Can you use a private or local AI model?
Yes. Private/local processing can be implemented when your data should not be sent to third-party cloud AI providers.
Will the system provide citations or sources for its answers?
Yes. Citation and source attribution can be implemented so users can see which retrieved information supports an answer.
Can you build a production-ready RAG system?
Yes. Production implementations can include retrieval optimization, evaluation, guardrails, observability, API integration, and deployment depending on the selected package and requirements.
