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I will build n8n supabase vector search ai agent, pinecone vector database retrieval
Gabriel
About this gig
Need an AI solution that answers from your own data? I provide n8n AI agent development, custom AI agent builds, RAG chatbot development, vector database integration and vector search setup tailored to your workflow. Use AI workflow automation, Pinecone integration, Supabase integration, an n8n RAG workflow or AI chatbot development to turn scattered information into searchable, useful answers.
For a business knowledge base, document Q&A chatbot, PDF chatbot or website chatbot, I can help design a customer support bot, internal knowledge assistant or SaaS AI assistant for enterprise search across company documents with private data retrieval. The solution can include an n8n workflow, Supabase vector search, Pinecone vector database or Supabase vector database, depending on your architecture. I can configure semantic search, embeddings, retrieval augmented generation, LangChain integration, API integration and a vector store retriever to connect your sources with the right retrieval flow.
Share your use case, data sources and preferred stack before ordering so we can define a practical scope. I'll align retrieval settings with your data and document the agreed setup.
Get to know Gabriel
Gabriel
n8n AI Agent and Vector RAG Automation Engineer
- FromUnited Kingdom
- Member sinceApr 2026
- Avg. response time1 hour
Languages
English, German, French, Dutch, Spanish, Italian
I engineer custom n8n AI agents, multi-agent architectures, and automated workflows designed to scale business operations. From RAG systems using Pinecone and Supabase vector search to human-in-the-loop approval webhooks, custom code nodes, and streaming chat widgets, I build reliable automation tailored to your stack. Whether you need an intelligent HubSpot lead scoring workflow or an automated customer support system, I deliver secure, production-ready n8n integrations that convert leads and streamline operations. Let's turn your manual workflows into autonomous systems.
FAQ
What is a RAG AI agent?
RAG stands for retrieval-augmented generation. It lets an AI system retrieve relevant information from a knowledge base before generating an answer, helping it respond using your supplied data.
Can you use either Supabase or Pinecone?
Yes. The project can use Supabase pgvector or Pinecone, depending on your existing infrastructure, requirements, and preferred database.
Can the agent answer questions from PDF files and company documents?
Yes, provided the document formats are supported and their content can be extracted. Complex layouts, scanned PDFs, tables, and image-heavy files may require additional processing.
Do I need API keys or paid subscriptions?
You may need access to n8n, a vector database, an embedding model, and an LLM provider. Their pricing and usage limits vary. Existing accounts can often be used, and additional costs are not included unless explicitly agreed.
Will the AI always give correct answers?
No AI retrieval system is guaranteed to be accurate. I can test retrieval quality, use relevant source context, and configure fallback behavior, but performance depends on the data, embeddings, retrieval configuration, and model.
Can you connect the agent to my existing business tools?
Yes, where the necessary APIs, credentials, and integrations are available. Additional integrations may require a custom quote.
