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I will n8n ai rag agent automation postgresql qdrant supabase
Jansher K
About this gig
I will build a custom AI RAG (Retrieval-Augmented Generation) agent that can search your private data, reason intelligently, and automate actions using n8n workflows.
This service is for businesses that need more than a basic chatbot. I create agentic AI systems that retrieve knowledge from documents and databases, use tools and APIs, apply conditional logic, and execute automated workflows.
Your AI agent can work with PDFs, CSVs, websites, APIs, and internal data sources while remaining secure and private.
Technologies I work with include n8n AI automation, OpenAI or other LLMs, LangChain or LlamaIndex, PostgreSQL, Supabase, Qdrant, Pinecone, and other vector databases.
Use cases include internal knowledge base AI, customer support agents, CRM and sales assistants, SaaS embedded AI, and research or analytics assistants.
Packages range from a single RAG agent with basic Q&A to full agentic architectures with multi-step reasoning, tool usage, database integration, and production-ready deployment.
Please contact me before ordering so I can understand your requirements and design the right AI RAG agent for your business.
Get to know Jansher K
Jansher K
Automate Everything
- FromPakistan
- Member sinceJul 2016
- Last delivery2 years
Languages
English
I’m Jansher Khan, a full-stack software engineer and AI automation specialist focused on building intelligent, scalable systems for modern businesses. I design agentic AI workflows, automation pipelines, and API integrations that reduce manual work, improve efficiency, and scale operations. My solutions are security-first, open-source focused, and production-ready, deployed on reliable VPS infrastructure. I work with startups and teams to turn complex processes into clean, maintainable, and cost-effective automation systems that deliver real business results.
FAQ
Q1: What information do you need to start building my AI RAG agent?
I need your use case, data source (documents, database, APIs, or URLs), preferred AI model if any, and deployment preference. If required, access credentials can be shared securely after the order starts.
Q2: How is a RAG agent different from a normal AI chatbot?
A RAG agent retrieves answers from your own data using vector databases and then generates responses using an AI model. Unlike basic chatbots, it provides accurate, context-aware results and can use tools, APIs, and automation workflows.
Q3: Do you support n8n AI automation with RAG agents?
Yes. I integrate RAG agents with n8n to create automated workflows, triggers, conditional logic, API calls, and data syncing with your existing systems.
Q4: Which databases and vector stores can you work with?
I support PostgreSQL, Supabase, Qdrant, Pinecone, and other vector databases. The final choice depends on your data size, performance needs, and deployment environment.
Q5: Can the AI agent be deployed in my own environment?
Yes. The system can be deployed on your cloud, server, or SaaS environment to ensure data privacy, security, and full ownership.
Q6: Is my data secure and confidential?
Yes. Your data is used only for your project. I do not reuse, store, or share client data outside the agreed deployment environment.
Q7 (Technical): How do you design agentic RAG architectures?
I design agentic systems using retrieval pipelines, embedding strategies, vector search, memory management, tool usage, and reasoning chains. Depending on the project, I use LangChain, LlamaIndex, n8n workflows, and custom logic to ensure scalable and production-ready performance.
Q8 (Technical): Can you handle large datasets and real-time updates?
Yes. I support chunking strategies, incremental data syncing, scheduled ingestion, and optimized vector indexing to handle large datasets and near real-time updates efficiently.
Q9: Do you offer post-delivery support or future enhancements?
Yes. Each package includes revisions, and long-term support, scaling, or feature enhancements can be provided as a separate service.
