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I will build a custom rag system with enterprise knowledge graphs
Jonathan T.
About this gig
About This Gig
If your AI is having trouble with connecting data from multiple sources meaningfully, you need a better system that introduces relationships in your data. I build GraphRAG systems using Neo4j and NetworkX to improve AI performance & reasoning.
Basic
Target: Perfect for validating your data structure or a small-scale pilot.
- What you gain: A functional Proof of Concept (POC) that proves your data can be modeled as a Knowledge Graph to eliminate hallucinations.
- Deliverables: Neo4j schema, ingestion script, and RAG query functions.
Standard
Target: For teams that need high-accuracy AI for their specific internal data.
- What you gain: A robust, hybrid RAG system that combines vector search with graph context. This results in 30-50% higher accuracy on complex queries compared to standard bots.
- Deliverables: Production-ready backend code, Graph database schema, and an API endpoint for your FE.
Premium
- Deliverables: All of Standard + Full analytics-heavy RAG pipeline, NetworkX logic layer, graph visualization tool, and 14 days of technical support. By using NetworkX for graph algorithms, the AI can answer questions that require connecting 3 or more sources.
Get to know Jonathan T.
Jonathan T.
AI Engineer
- FromIndonesia
- Member sinceApr 2026
- Avg. response time1 hour
Languages
English, Indonesian, German
I am an experienced AI engineer that is comfortable in building custom AI automations and advanced Agentic AI systems as a service. My services extend to creating automation for business, reliable and robust chatbots with built-in Knowledge Base and websites with integrated AI.
If you're looking to implement AI in your business or someone to teach you how to setup custom AI agents like Hermes and OpenClaw, don't hesitate to DM me 👋.
FAQ
What makes your GraphRAG system different from standard RAG or chatbot solutions?
Standard RAG systems rely purely on vector similarity, which often leads to irrelevant or hallucinated answers. My GraphRAG systems use structured relationships in a knowledge graph (Neo4j) combined with vector search. This allows the AI to reason over connected data, improving its response quality.
How do you reduce hallucinations?
Hallucinations are minimized by: 1. Grounding responses in explicit graph relationships 2. Restricting answers to verified data paths 3. Combining graph traversal with retrieval The AI doesn’t invent answers—it derives them from your data structure.
How accurate is the system compared to ChatGPT or standard bots?
For complex, domain-specific queries, GraphRAG systems typically achieve 30–50% higher accuracy because they: 1. Use verified relationships instead of guessing 2. Maintain context across multiple documents 3. Support multi-hop reasoning
What kind of data can you work with?
I can work with most structured and unstructured data formats, including: - PDFs (reports, research papers, legal docs) - CSV / Excel datasets - JSON / APIs - SQL databases - Internal documentation or wikis If your data has relationships hidden inside it, it’s a strong candidate for GraphRAG.
Do I need technical knowledge to use the system?
No. For Standard and Premium plans, I provide a FastAPI backend with simple endpoints that your frontend or internal tools can call. You don’t need to understand graphs or AI internals to use it effectively.
What will I receive at the end of the project?
Depending on the plan, deliverables may include: Graph schema (Neo4j) Data ingestion pipeline Hybrid RAG query system FastAPI backend NetworkX analytics layer (Premium) Graph visualization tools Documentation + support Everything is designed to be usable and extendable, not just a demo.
Can you integrate this with my existing systems or frontend?
Yes. The Standard and Premium plans include an API layer, making it easy to integrate with: Internal dashboards Chat interfaces Web apps Existing AI tools
Can you customize the system for my exact use case?
Absolutely. Every system is tailored to your: - Data structure - Query needs - Business logic This is not a one-size-fits-all chatbot—it’s a custom reasoning engine.
Do you support local/private LLMs?
Yes. I can configure the system to work with: 1. OpenAI (e.g., GPT models) 2. Anthropic Claude 3. Local models like Llama This is especially useful for privacy-sensitive or on-premise deployments.
What do you need from me to get started?
Before starting, I need: 1. Your dataset (PDFs, CSVs, etc.) 2. A clear problem statement (what current AI fails at) 3. (Optional) Sample queries you want the system to answer If you’re unsure, message me and I’ll help define the structure.
