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I will a secure custom rag ai system for your private data
Julio Martinez
About this gig
Stop feeding your sensitive company data into public AI models.
In 2026, data is your most valuable asset. But generic LLMs don't know your business, and pasting private documents into them is a security risk. You need a Private RAG System that "talks" to your data without leaking it.
I specialize in building Enterprise-Grade RAG (Retrieval-Augmented Generation) systems using LangChain, Agno, and Vector Databases (Pinecone/Chroma). I don't just "connect a PDF"; I engineer Hybrid Search architectures that eliminate hallucinations and provide cited, accurate answers from your internal knowledge base.
What You Get (Benefits, not just features):
- Zero Hallucinations: My systems use "Grounding" techniques. If the answer isn't in your data, the AI says "I don't know" instead of lying.
- Hybrid Search Accuracy: I combine Semantic Search with Keyword Search to ensure the AI finds specific technical terms or IDs that standard embeddings miss.
- Data Privacy First: Your data is stored in secure, encrypted Vector Databases. The LLM only sees the specific snippets relevant
Turn your static documents into an interactive corporate brain.
Contact me now to discuss your data needs.
Get to know Julio Martinez
Julio Martinez
Full Stack Developer
- FromVenezuela
- Member sinceApr 2017
- Last delivery1 year
Languages
Spanish, English
Hello! My name is Julio, I am a programmer.
Core Areas:
Web development,
Mobile apps development.
Backend: Python (Django), Php (Codeigniter, Slim Framework)
Frontend: Javascript (Angular)
Other AI Development Services I Offer
FAQ
How do you prevent the AI from making things up (Hallucinations)?
I use a technique called Strict Retrieval. The system is instructed to only answer using the context retrieved from your Vector Database. I also implement "Source Citations," so every answer includes a link to the page/document used.
Is my data secure? Does OpenAI train on it?
If we use the API (Enterprise/Team tier), OpenAI does not train on your data by default. For maximum security, I can implement Local RAG using Ollama and open-source models (Llama 3, Mistral) so your data never leaves your server.
What is "Hybrid Search" and why do I need it?
Standard RAG uses "Semantic Search" (matching meanings). This often fails with specific product codes, names, or dates. Hybrid Search adds a keyword layer (BM25) to ensure that if you search for "Invoice #1234", you get that exact document, not just "documents about invoices".
Can I chat with my SQL Database?
Yes, but this requires the Premium package. I use Agno or LangChain SQL Agents to translate natural language questions into secure SQL queries, allowing you to ask "What were sales last month?" without writing code.
Are there ongoing costs?
Yes. You will pay for the LLM usage (e.g., OpenAI API) and the Vector Database hosting (Pinecone has a free tier, but enterprise use requires a paid plan). I optimize the code to minimize token usage and keep costs low.
