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Fiverr Pro
Deutsch
$
USD
I will build a custom rag chatbot for your data
Mazu
Über diesen Service
Turn your documents, databases, and PDFs into an AI that
"chats" with your data. Accurately. Securely.
I build custom RAG pipelines connecting LLMs to your data.
WHAT I BUILD:
PDF/Document Chatbots Chat with your knowledge base
Database RAG Query SQL/NoSQL with natural language
Hybrid Search Semantic + keyword for accuracy
Custom UI Clean interface your users love
Security Layers PII protection, access controls
️ TECH STACK:
Vector DBs: Pinecone, ChromaDB, Weaviate, Qdrant
Frameworks: LangChain, LlamaIndex
Embeddings: OpenAI, Cohere
Backend: FastAPI, Python
PACKAGES:
BASIC ($100) Single data source RAG setup, basic chatbot,
simple deployment
STANDARD ($250) Multi-source RAG pipeline, vector DB setup,
custom UI deployment
PREMIUM ($500) Enterprise RAG with hybrid search, security
layers, full deployment
️ WHY RAG:
Raw LLMs hallucinate RAG grounds answers in YOUR data
60% cheaper than fine-tuning
Your data stays private
Easy to update just swap documents
Message me before ordering with details about your data.
Limited: 5 clients per month.
Lerne Mazu kennen
Mazu
"I Protect Your AI From Security Risks, Bias Compliance Failures"
- AusPakistan
- Mitglied seitNov. 2025
- ⌀ Antwortzeit9 Stunden
Sprachen
Urdu, Englisch
I'm an AI Security & Trust Engineer passionate about making
AI safe, reliable, and legally compliant. I help businesses
deploy production-ready AI systems that don't hallucinate,
leak data, or fail audits.
My work spans AI red teaming, building LLM guardrails,
evaluating agents, developing custom RAG pipelines, and
creating EU AI Act/NIST compliance documentation.
I use tools like LangChain, RAGAS, PyRIT, Pinecone, and
LlamaGuard to ensure your AI is secure, accurate, and ready
for enterprise.
Message me to discuss your project.
Mein Portfolio
FAQ
What is RAG?
Retrieval-Augmented Generation. It's a system that lets an AI "read" your documents before answering, making it accurate and grounded in your specific data instead of hallucinating.
What data sources can you connect?
PDFs, Word docs, websites, SQL databases, Notion, Google Drive, SharePoint, and most REST APIs. If it has text, I can probably connect it.
How is this different from just uploading files to ChatGPT?
ChatGPT can't handle large volumes, has no access control, can't be customized, and doesn't integrate with your apps. A custom RAG system does all of this securely.
Do you use OpenAI or open-source models?
I work with both. OpenAI (GPT-4) for maximum quality, or open-source models (Llama, Mistral) if you need privacy, lower cost, or self-hosting.
Will my data be used to train AI models?
No. I configure the system so your documents are only used for retrieval, never sent to training pipelines. Your data stays yours.
Can you make it handle multiple languages?
Yes. The pipeline can process and respond in multiple languages using multilingual embeddings and models.
What is "hybrid search" in the Premium package?
It combines semantic search (meaning-based) with keyword search (exact matches). This dramatically improves accuracy for technical or legal documents.
Do you provide deployment and hosting?
Yes. I can deploy to Vercel, Railway, AWS, or your own servers. The Premium package includes full deployment and setup documentation.
How accurate is the system?
With proper chunking and retrieval setup, accuracy is typically 85-95%. I include evaluation metrics in every package so you can measure it yourself.
What do you need from me to start?
A sample of your data (a few PDFs or a database schema), a description of what users should be able to ask, and any specific requirements (security, UI preferences, etc.).
