Hi everyone,
I’m looking for an experienced n8n consultant to help me build and deploy a production-grade RAG (Retrieval-Augmented Generation) workflow.
The ideal consultant should:
Have proven experience with RAG pipelines (e.g. vector databases, LLM integrations, document indexing)
Be able to integrate external data sources (APIs, databases, cloud storage, or custom endpoints) efficiently within n8n
Be confident in building, deploying, and optimizing scalable workflows in production
Bonus: If you speak Italian, that’s a big plus — but English is perfectly fine.
Work type: Freelance / Hourly-based Remote Feel free to DM me here
I believe it’s a great fit. I am a Python and n8n developer with 5+ years of experience and I recently delivered an AI-powered bid management system for an engineering firm using n8n, where I built and deployed a robust RAG pipeline integrating vector databases, LLMs, and document indexing—plus seamless API and database integrations. The workflows were designed for reliability and scalability in production environments (what I believe you are looking for)
I’d love to discuss this project in details. Just sent you a message
Hello @Simon_Marussi , I would love to work with you on this, you can book a call Here to discuss the project requirements and you can checkout my upwork profile Here
This sounds like something I can help with, comfortable working with vector DBs, LLMs, and handling custom integrations in n8n.
I focus on building clean, scalable workflows that are production ready from the start.
Let me know if you want to go over your setup or chat through requirements.
Hi Simon – building a production-grade RAG pipeline in n8n is a great approach for keeping your data refreshes and model calls manageable.
Here’s a framework I use when deploying similar solutions:
Trigger Layer – watch for a new or updated source document, generate a document ID, and enqueue processing so ingestion can scale independently.
Ingestion & Chunking – normalize the file (PDF, HTML, etc.), chunk content, and enrich with metadata (source, timestamp) before upserting into the vector store.
Retrieval & Context Builder – when a query comes in, retrieve top-k chunks and assemble a context object (text + metadata) that downstream LLM nodes can consume.
Orchestration & Caching – route queries through your preferred LLM with a keyed cache so repeated questions don’t hammer the API.
Monitoring – persist usage metrics, latency, and vector-store health into a dashboard so you can spot drift or slow queries quickly.
A couple of scoping questions:
• Which vector database are you leaning toward (e.g., Pinecone, Qdrant, PgVector)? n8n has solid HTTP/SQL nodes for each, but limits can influence design.
• Do external data sources arrive in realtime (webhooks) or in batches? This will shape how you schedule ingestion vs. on-demand retrieval.
Best-practice tips:
• Keep ingestion in a separate sub-workflow triggered via webhook to avoid blocking end-user queries.
• Store API keys and DB creds in credential nodes and reference via env variables – makes rotation painless.
This is general guidance from similar RAG builds I’ve deployed – hope it helps hone your approach! Feel free to clarify any details and I can dive deeper.
I got you, I have been building all forms of automations for the past 2 years and have built 100s of flows for my clients. Have worked with all sorts of companies and gotten them 10s of thousands in revenue or savings by strategic flows. When you decide to work with me, not only will I build this flow out, but also give you a free consultation like I have for all my clients that led to these revenue jumps.
I have built a similar workflow like this for one of my clients. I can not only share that but also how you can streamline processes in your company for faster operations. All this with no strings attached on our first call.
Here, have a look at my website and you can book a call with me there!
Hi Simon,
this sounds right up our alley – we’ve successfully built and deployed production-ready RAG pipelines using n8n with vector databases, LLMs, and external data sources.
Happy to learn more about your exact use case and see how we can support you efficiently. Is your project still relevant? If so, please send me an E-Mail to [email protected]
Work type: Freelance / Hourly-based
Remote
Feel free to DM me here