Fenra

Fenra is a multi-agent system for detecting corruption patterns in public procurement data. It uses a swarm of specialized AI agents coordinated by a verification layer called “the Judge.”

The Problem

Corruption in procurement leaves traces across multiple systems—financial records, company registries, contract documents. The patterns are obvious in hindsight but buried under noise in real-time. Single AI models fail because corruption detection requires simultaneous reasoning about:

• Financial anomalies (pattern matching)
• Relationship networks (graph traversal)
• Legal compliance (rule evaluation)
• Industry context (domain knowledge)

The Swarm Architecture

Specialist Agents (The Workers):

• Transaction Analyzer: Detects outliers in amounts, timing, and frequency
• Network Mapper: Traverses knowledge graphs to find hidden relationships between companies and officials
• Legal Checker: Flags procedural violations against procurement regulations
• Document Reader: Extracts entities and relationships from unstructured text

The Judge (The Verifier):

• Validates findings against evidence
• Checks for contradictions between agents
• Assigns confidence scores
• Only escalates high-confidence, well-supported flags

Graph RAG

Fenra uses Graph RAG instead of standard vector search. Entities (companies, people, transactions) are nodes; relationships are edges. This enables multi-hop reasoning:

“Company A paid Company B → Company B’s director is Person C → Person C is related to Procurement Officer D”

The graph structure also provides explainability—every flag comes with an auditable path.

Tech Stack

• Neo4j for relationship data and graph traversal
• Vector store for semantic search over documents
• OCaml for the core logic—type safety for financial and legal data
• Erlang for agent orchestration—hot code reloading without downtime