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Fiverr Pro
English
$
USD
I will build a custom rag chatbot with langchain, knowledge graph, and gdpr if needed
AI Engineer
I am an AI Engineer with experience in designing production systems integrating LLMs, RAG, and cloud architectures. I specialize in building automated pipelines, multi-agent architectures, and serverl...
About this Gig
Most "AI chatbots" on the market are thin wrappers around ChatGPT with no real retrieval logic they hallucinate, don't scale, and break under real usage.
I build chatbots differently: with proper Retrieval-Augmented Generation (RAG) pipelines, optional knowledge graph structuring for complex relational data, and production-grade backend architecture designed to scale not a weekend prototype.
What sets this apart:
- Custom retrieval pipeline (not just a vector DB dump) tuned for accuracy on YOUR data, not generic answers
- Knowledge graph integration when your data has relationships that plain vector search misses (e.g. legal documents, product catalogs, org structures)
- Async, scalable backend (FastAPI) built to handle real traffic, not just a demo
- Choice of LLM provider based on your actual needs (cost, latency, data sensitivity) Groq, OpenAI, Anthropic, open-source models
Proven on a real production case:
Built a RAG chatbot answering questions on France's entire Labor Code a domain where hallucination isn't acceptable and document structure (articles, cross-references, amendments) is genuinely complex. Retrieval logic built from scratch, not a LangChain default pipeline.
Programming language:
Python
APIs:
Other
My Portfolio
FAQ
Do you offer GDPR-compliant data handling for EU-based clients?
Yes. For clients based in Europe, I can set up GDPR-compliant data processing — including data residency options, anonymization, and retention policies aligned with your compliance needs.
Which LLM provider do you use — OpenAI, Groq, Together AI?
I work with multiple providers (Groq, Together AI, OpenAI, Anthropic, and open-source models) and recommend the best fit based on your latency, cost, and data privacy requirements — not a one-size-fits-all default.
What's the difference between a "RAG chatbot" and a regular ChatGPT wrapper?
A regular wrapper just sends your question to ChatGPT with no real data grounding — it can hallucinate. A proper RAG chatbot retrieves accurate information from YOUR documents/data before generating an answer, so responses are grounded in facts, not guesses.
Can this scale if I have a lot of documents or high traffic?
Yes — the Premium package includes a scalable, async backend architecture built to handle production traffic, not just a small demo.
I don't have my data organized yet — can you still help?
Yes. During the discovery call, I'll help assess your data sources and recommend the best structure for retrieval, even if it's not clean or organized yet.
