Project Description

n8n DevRel build · retrieval-augmented AI

Generic AI chatbots give generic answers. When your team or customers need responses grounded in your actual data — internal docs, SOPs, product documentation — you need Retrieval-Augmented Generation. I built a RAG assistant that answers from your own data with high accuracy.

It runs on Weaviate vector search with production-grade RAG: tuned chunking, metadata filtering, and carefully chosen embedding models. Documents are ingested, split into semantically meaningful chunks, embedded, and made instantly searchable — and every question retrieves the most relevant context before the model answers.

The gap between a toy demo and a production RAG system is all in the details — chunk size, overlap, metadata-aware filtering, embedding choice. I built this one with those lessons baked in, so the answers are reliable from day one.

What it does

Weaviate vector search

Enterprise-grade vector database for fast, accurate semantic search over your docs.

Optimized chunking

Tuned document splitting that preserves context and improves answer quality.

Metadata filtering

Filter results by document type, date, department, or any custom field.

Production-ready architecture

Built with error handling, monitoring, and scaling in mind.

Built with

n8nWeaviateOpenAI EmbeddingsOpenAI GPT-4REST API

Want AI that answers from your data, not the internet’s?

I build production RAG on a layer you own — grounded in your documents, tuned for reliable answers. Let’s look at where it fits.

Book a free consultation →