What if an AI agent could detect outdated policies, trace answers to evidence, identify conflicting instructions, and admit when it doesn't know the answer?
That's the problem I explored while building SetuBrain for the FalkorDB Graph Hackathon 2026.
Inspired by Y Combinator's Company Brain Request for Startups, SetuBrain aims to bridge scattered company knowledge and reliable AI execution.
🌐 Live Demo | 💻 GitHub Repository | 🎥 Watch the Project Video
🧩 The Problem with Traditional RAG
Company knowledge is scattered across PDFs, internal wikis, Slack conversations, policies, and approval records.
A typical Retrieval-Augmented Generation (RAG) system retrieves relevant text, but what happens when:
- An old policy conflicts with a new one?
- An answer requires connecting multiple departments and approvals?
- Two documents contain contradictory instructions?
- No approved procedure exists?
Retrieving information isn't the same as verifying it.
🧠 Why FalkorDB?
SetuBrain uses FalkorDB as its graph data layer to represent company knowledge as connected entities rather than isolated text chunks.
For example:
Department → Procedure → Application → Policy → Approver
Using Cypher queries, the system can traverse relationships, identify superseded policies, detect conflicts, and trace decisions to supporting evidence.
The goal is to make graph relationships part of the reasoning process—not just a visualization.
⚙️ How SetuBrain Works
1. Graph-powered knowledge: Represents departments, procedures, applications, policies, contacts, decisions, and evidence as connected nodes.
2. Policy verification: Uses graph traversal and supersession relationships to identify authoritative policies.
3. Conflict detection: Flags contradictory policies and supports human review before an uncertain instruction is treated as authoritative.
4. Human-in-the-loop learning: Escalates missing knowledge to subject-matter experts and incorporates approved updates into the graph.
5. Agent integration through MCP: Exposes graph-backed tools through a Model Context Protocol endpoint so compatible AI clients can access SetuBrain's capabilities.
6. Reusable agent skills: Compiles verified graph information into structured SKILL.md specifications.
7. Multi-tenant architecture: Separates tenant knowledge contexts using graph selection, with access controls required to enforce isolation securely.
📊 Testing the Approach
In SetuBrain's documented 15-question enterprise benchmark, the results were:
| Approach | Correct answers | Accuracy |
|---|---|---|
| Flat vector RAG baseline | 4/15 | 26.7% |
| SetuBrain graph approach | 15/15 | 100% |
These results apply to this specific test set, not every RAG or graph-based system. The benchmark explores multi-hop reasoning, outdated policies, contradictions, missing knowledge, and evidence provenance.
The key takeaway: vector retrieval and graph traversal solve different problems and can complement each other.
🛠️ Tech Stack
- Next.js & TypeScript — Application and interface
- FalkorDB & Cypher — Graph storage and relationship traversal
- MCP — Integration with compatible AI agents
- Three.js — Interactive 3D knowledge graph visualization
- AI agent workflows — Conflict review, expert escalation, and skill generation
🔗 Explore SetuBrain
- 🌐 Live Demo
- 🖥️ Interactive Console
- 💻 Source Code on GitHub
- 📚 Full Technical Guide
- 🔌 MCP Endpoint
- 🏁 FalkorDB Graph Hackathon
💭 My Biggest Takeaway
Building SetuBrain reinforced one important principle:
A reliable AI system should know what is approved, what is outdated, what conflicts, and when human judgment is needed.
The future of enterprise AI isn't just about generating better answers. It's about building systems that can verify their knowledge and handle uncertainty responsibly.
💬 Question for developers: Would you trust a graph-powered AI agent to manage company knowledge? What would you build differently?
Explore the repository, share your feedback, and let's build more reliable AI systems together!
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