You will get Custom AI RAG Agent: Chat with Your Business Documents (PDF, SQL, Notion)
Project details
Stop searching through folders. Start talking to your data.
Most "AI Chatbots" are just thin wrappers around ChatGPT that hallucinate facts. I build Engineering-Grade RAG (Retrieval-Augmented Generation) Systems designed for accuracy, citation, and business logic.
This Project is Perfect For:
• Legal/Finance: Querying complex contracts (like my "GraphMERT" project) to find specific clauses.
• Operations: Chatting with technical manuals, SOPs, or inventory logs.
• Customer Support: An agent that references your actual knowledge base before answering.
What You Get:
• No Vendor Lock-in: I build using open standards (Python, LangChain, FastAPI). You own the code.
• Smart Retrieval: I implement "Hybrid Search" (combining keywords + vectors) so the AI understands specific jargon, not just general vibes.
• Visual Logic: For Standard/Advanced tiers, I use n8n to create visual, auditable workflows so you can see exactly why the AI gave an answer.
My Tech Stack:
• Backend: Python, FastAPI, Supabase (Vector Store).
• Orchestration: LangChain, n8n.
• Frontend (Advanced Tier): React + Framer Motion for a polished user experience.
Most "AI Chatbots" are just thin wrappers around ChatGPT that hallucinate facts. I build Engineering-Grade RAG (Retrieval-Augmented Generation) Systems designed for accuracy, citation, and business logic.
This Project is Perfect For:
• Legal/Finance: Querying complex contracts (like my "GraphMERT" project) to find specific clauses.
• Operations: Chatting with technical manuals, SOPs, or inventory logs.
• Customer Support: An agent that references your actual knowledge base before answering.
What You Get:
• No Vendor Lock-in: I build using open standards (Python, LangChain, FastAPI). You own the code.
• Smart Retrieval: I implement "Hybrid Search" (combining keywords + vectors) so the AI understands specific jargon, not just general vibes.
• Visual Logic: For Standard/Advanced tiers, I use n8n to create visual, auditable workflows so you can see exactly why the AI gave an answer.
My Tech Stack:
• Backend: Python, FastAPI, Supabase (Vector Store).
• Orchestration: LangChain, n8n.
• Frontend (Advanced Tier): React + Framer Motion for a polished user experience.
Data Entry Type
Data Cleansing, Document Conversion, Error Detection, Online Research, Word ProcessingData Entry Tool
CRM Software, Google Docs, Google Sheets, Microsoft Excel, Microsoft Office, Microsoft WordWhat's included
| Service Tiers |
Starter
$300
|
Standard
$750
|
Advanced
$1,500
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 14 days |
Number of Revisions | 1 | 3 | 10 |
Formatting & Clean Up | - | - | - |
Graph & Table Creation | - | - | - |
Frequently asked questions
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PP
Paul P.
Dec 24, 2025
Build basic local e2e RAG CLI in python
Very good experience. Wrote canonical and elegant python code, with fast turnaround and good communication.
About Alexander
AI Automation Engineer & RAG Specialist | Python, n8n, LangChain |
Oak Ridge, United States - 4:52 am local time
My focus is on systems that are stable, explainable, and usable by non-technical teams — not fragile demos.
With a background in Electrical Engineering Technology and Industrial Automation, I don't just write scripts; I engineer stable, drift-resistant AI agents and RAG workflows that solve complex operational problems.
I specialize in bridging the gap between messy business data and clean, actionable AI outputs. whether you need to chat with your legal contracts or automate a complex logistics chain.
My Core Stack:
AI & Retrieval: RAG pipelines, hybrid search, document analysis (LangChain, OpenAI, vector DBs)
Automation: n8n, Python, FastAPI.
Frontend: React, Framer Motion (for when you need your AI to look as good as it works).
Recent Wins:
GraphMERT: A legal contract analysis tool using graph-based retrieval.
Tekita: A React-based social operating system with AI companion integration.
Custom ERP: Built a full-stack inventory system for a logistics company, replacing manual spreadsheets with Python automation.
If you are tired of "toy" AI demos and want a robust system architecture, let's talk.
Steps for completing your project
After purchasing the project, send requirements so Alexander can start the project.
Delivery time starts when Alexander receives requirements from you.
Alexander works on your project following the steps below.
Revisions may occur after the delivery date.
Data Discovery:
You send me the documents or API keys for the data you want to index.
Pipeline Construction:
I build the ingestion pipeline to clean and vectorise your data (the most important step!).