AI Agent & RAG Automation Developer | n8n, Python, FastAPI by Emmanuel OlaoluwaAI Agent & RAG Automation Developer | n8n, Python, FastAPI by Emmanuel Olaoluwa
AI Agent & RAG Automation Developer | n8n, Python, FastAPIEmmanuel Olaoluwa
Cover image for AI Agent & RAG Automation Developer | n8n, Python, FastAPI
I build AI automations and backend services that connect your documents, data and tools, so your team gets results it can check.
WHAT CAN AI AUTOMATION DO FOR YOU?
A RAG assistant can answer questions from your own files: FAQs, policies, price lists and service guides.
A workflow can read invoices and receipts, extract the data and categorize it.
An AI step can score, flag or route cases, and send people the ones that need review.
I start with your process: what triggers the work, which tools are involved, where a person must approve something, and what a good result looks like.
WHAT I’VE BUILT ✔ Document-to-knowledge-base pipeline (n8n + Pinecone + OpenAI): Upload PDF, DOCX, CSV or TXT files through a form, route them by type, clean them, embed them and store them in a vector database for a RAG chatbot or support assistant. ✔ AI service layer for a property management platform (Python/FastAPI): As sole developer, I built anomaly detection on financial data, a vision-LLM invoice OCR pipeline, vendor sentiment analysis, automated PDF investor reports, and tenant renewal scoring with LLM output guards. It integrates with a Java Spring Boot backend and Angular frontend. ✔ CareConnect AI (n8n + Flask): A team project for Smart India Hackathon. Patient data arrives through a webhook, gets an AI-assisted risk assessment, and is routed by risk and confidence, with automated email alerts for cases needing clinical review. It uses mock data.
WHAT MAKES IT PRODUCTION-READY? A working demo is only the start. I scope in: ✔ Validation of AI outputs and tool inputs ✔ Deterministic rules around LLM decisions, so the AI can’t act on its own for high-stakes steps ✔ Error handling, retries and clear logs ✔ Human review for uncertain or sensitive cases ✔ Testing against your real examples
TECH I USE Python, FastAPI, Flask, n8n, PostgreSQL, Pinecone, OpenAI and Groq/LLaMA models, Pandas, REST APIs, webhooks.
WHAT YOU RECEIVE We agree on scope and acceptance criteria first. You then get a working prototype, a connected workflow or API, testing, basic documentation, and a walkthrough.
WHAT WOULD YOU LIKE TO AUTOMATE FIRST? Send me your current process, the tools you use and the result you want. I’ll tell you whether a simple workflow is enough or whether you need a custom backend.
FAQs

Contact for pricing
Duration1 week
Tags
AI Agents
FastAPI
Forager
LLM Integration
N8N
Pinecone
Python
API Integration
Business Workflow Automation
Service provided by
1
Followers
AI Agent & RAG Automation Developer | n8n, Python, FastAPIEmmanuel Olaoluwa
Contact for pricing
Duration1 week
Tags
AI Agents
FastAPI
Forager
LLM Integration
N8N
Pinecone
Python
API Integration
Business Workflow Automation
Cover image for AI Agent & RAG Automation Developer | n8n, Python, FastAPI
I build AI automations and backend services that connect your documents, data and tools, so your team gets results it can check.
WHAT CAN AI AUTOMATION DO FOR YOU?
A RAG assistant can answer questions from your own files: FAQs, policies, price lists and service guides.
A workflow can read invoices and receipts, extract the data and categorize it.
An AI step can score, flag or route cases, and send people the ones that need review.
I start with your process: what triggers the work, which tools are involved, where a person must approve something, and what a good result looks like.
WHAT I’VE BUILT ✔ Document-to-knowledge-base pipeline (n8n + Pinecone + OpenAI): Upload PDF, DOCX, CSV or TXT files through a form, route them by type, clean them, embed them and store them in a vector database for a RAG chatbot or support assistant. ✔ AI service layer for a property management platform (Python/FastAPI): As sole developer, I built anomaly detection on financial data, a vision-LLM invoice OCR pipeline, vendor sentiment analysis, automated PDF investor reports, and tenant renewal scoring with LLM output guards. It integrates with a Java Spring Boot backend and Angular frontend. ✔ CareConnect AI (n8n + Flask): A team project for Smart India Hackathon. Patient data arrives through a webhook, gets an AI-assisted risk assessment, and is routed by risk and confidence, with automated email alerts for cases needing clinical review. It uses mock data.
WHAT MAKES IT PRODUCTION-READY? A working demo is only the start. I scope in: ✔ Validation of AI outputs and tool inputs ✔ Deterministic rules around LLM decisions, so the AI can’t act on its own for high-stakes steps ✔ Error handling, retries and clear logs ✔ Human review for uncertain or sensitive cases ✔ Testing against your real examples
TECH I USE Python, FastAPI, Flask, n8n, PostgreSQL, Pinecone, OpenAI and Groq/LLaMA models, Pandas, REST APIs, webhooks.
WHAT YOU RECEIVE We agree on scope and acceptance criteria first. You then get a working prototype, a connected workflow or API, testing, basic documentation, and a walkthrough.
WHAT WOULD YOU LIKE TO AUTOMATE FIRST? Send me your current process, the tools you use and the result you want. I’ll tell you whether a simple workflow is enough or whether you need a custom backend.
FAQs

Contact for pricing