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English
$
USD
I will build a custom ai chatbot and rag pipeline on your data
Luigi
About this gig
I build custom AI chatbots, RAG pipelines and AI agents that connect your data to large language models from document Q&A to fully autonomous multi-agent systems.
What you get:
- RAG pipeline with document ingestion, chunking and embedding
- Vector database setup (Pinecone, Supabase pgvector, Chroma)
- AI agent with tool calling, memory and custom knowledge base
- LangChain or LangGraph orchestration with LLM of your choice
- REST API or chat interface for your application
- Evaluation suite to measure retrieval accuracy
- Production deployment on your infrastructure or cloud
I have built PixelFlow (AI image generation pipeline with custom agents) and Reunivo (AI-powered matching system with semantic search), both in production.
Why work with me:
- Systems that work in production, not just demos
- Full control: no SaaS dependency, all code delivered
- One price, complete system no hidden add-ons, no per-call fees
- 10+ years of experience, direct communication, based in Italy, fluent in English
Not sure if RAG or a fine-tuned model fits your use case? Message me I'll help you choose the right architecture before you order.
Get to know Luigi
Luigi
Full Stack Developer, SaaS, AI Automation and CRM for Startups
- FromItaly
- Member sinceMay 2026
- Avg. response time1 hour
Languages
Italian, English, French
I'm Luigi Garone, founder of Logika.studio — a full-stack software studio based in Italy, delivering scalable digital products for SMEs, startups, and finance professionals.
What I build:
— Custom SaaS platforms and CRM systems
— AI agents and LLM-powered automation (n8n, OpenAI)
— Digital marketing infrastructure
— Quantitative trading algorithms
Stack: Next.js · TypeScript · Node.js · Python · PostgreSQL · Supabase · n8n · Docker
Every project starts with clear architecture and ends with production-ready code.
My Portfolio
FAQ
What file formats can you ingest into the RAG pipeline?
PDF, Word, Excel, CSV, Markdown, HTML and plain text. For databases or APIs, I build custom connectors to pull data at ingestion time.
Which vector database do you recommend?
Supabase pgvector for projects already on Postgres, Pinecone for large-scale production, Chroma for local or lightweight setups. I recommend based on your stack.
Will the AI answer only from my documents or also from general knowledge?
Configurable. I can restrict answers strictly to your knowledge base, blend with LLM general knowledge, or return a fallback when no relevant document is found.
Can this integrate with my existing app or website?
Yes. I deliver a REST API or WebSocket endpoint ready to plug into any frontend or backend. React, Next.js, mobile apps — all compatible.
What do you need to get started?
Your documents or data source, the use case you want to solve, and your preferred LLM. For Standard and Premium, a short discovery call is required before I accept the order.
