AI & Graph
Intelligent systems, built on graphs.
AI that reasons over structure, not just text — agentic workflows, GraphRAG on Neo4j, graph-native tokenomics, and graph agent memory. Some of this is shipped and running; some is on the frontier and in development. We label which is which — proof, not promises.
Shipped & running
Agentic workflows
Deterministic multi-agent orchestration — poll → execute → validate → unblock, with a zero-trust validator between every step.
Agent-OS: 12 agents · 755 tasks · 98.7% autonomously verified · $0 infra. MB-agentic governance engine: 1,345 tests, 100% coverage.
GraphRAG on Neo4j
Retrieval that beats flat vector search — hybrid vector + full-text over a knowledge graph, grounded and cited.
Our EcoGraph: 77k chunks + a 9,828-entity knowledge graph of the whole Neo4j corpus. Hybrid retrieval benchmarked +18 pts, recovery doubled vs naive vector.
Graph-native tokenomics
Token economies modelled as graphs and fused with Monte-Carlo simulation — the graph⇄sim bridge that answers structural questions about dynamic output.
Neo4j + GDS + radCAD, applied to real token models (DevGuild $GUILD). See the Portfolio case study.
RAG at the edge
Grounded retrieval running on Cloudflare Workers AI + Vectorize, with MCP tool bridges so agents act on real systems safely and observably.
In production across the studio's edge stack (Kointel, KTHULHU).
On the roadmap
In developmentNeo4j agent memory
Graph-backed long-term memory for agents — entities, relationships and episodic recall in Neo4j rather than a flat vector store.
AI-agent GraphRAG
Production agent retrieval over live knowledge graphs — the hybrid retriever we benchmarked, packaged as an agent capability.
Agent-ID & Web3 agent security
Identity, authorisation and security for autonomous agents acting on-chain — a fast-growing surface as agents get wallets.
Graph-native security-audit engine
Smart-contract auditing on a Code Property Graph (AST + CFG + DFG) with a multi-agent roster — Topology → Vulnerability → Exploit → Verification → Reporting — and an EVM-sandbox auto-heal loop. The direction for the KTHULHU engine.
Industry context (attributed, not our metric): an independent study reports agents using GraphRAG are ~80% more truthful than vector-only RAG, answering roughly 2× the questions with fewer hallucinations — “Independent study: GraphRAG makes AI agents 80% more truthful”, Neo4j. Our own measured result is the +18-pt hybrid-retrieval lift above.
The through-line
Systems are graphs. Model them, then prove it.
An LLM over flat text guesses; an LLM over a graph reasons about structure — who connects to whom, what depends on what, which path an attacker or a whale can take. The same discipline runs through everything we build: model the system as a graph, query it, then prove the constraints hold.
Building AI that has to be right?
Deterministic agents, GraphRAG grounded in your data, or a graph model of a system you can query. Tell us what you're building.
Book a technical call