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

What It Does

• Multi-gateway ingestion: Pulls transactions from M-Pesa, bank APIs, Stripe, and other payment providers
• AI-driven reconciliation: Automatically matches payments to invoices, categorizes expenses, and flags anomalies
• Immutable tax rules: Kenyan tax regulations encoded as verifiable logic—no guesswork, no drift
• Direct KRA filing: Files returns directly to the Kenya Revenue Authority via eTIMS/GavaConnect APIs

Why Agentic?

Instead of rigid automation, Kowalski uses specialized agents that handle different aspects of tax compliance:

• Ingestion Agent: Handles API connections, rate limiting, and data normalization
• Reconciliation Agent: Uses ML to match fuzzy transaction descriptions to actual business activities
• Compliance Agent: Applies Kenyan tax rules (VAT, income tax, withholding) to categorized transactions
• Filing Agent: Prepares and submits returns, handling KRA’s specific formatting requirements

Each agent reports to a supervisor that ensures consistency and flags edge cases for human review.

Built With

• OCaml for the tax rule engine—type safety is non-negotiable when dealing with government compliance
• Erlang/OTP for the agent orchestration—fault tolerance and hot code reloading are essential for financial systems
• LLMs for classification and anomaly detection, constrained by structured output schemas

Features

• Blazing Fast: Startup time < 50ms.
• LSP Support: Native LSP integration for TypeScript, Go, Rust, OCaml, and more.
• Plugin Manager: Uses lazy.nvim for efficient plugin loading.
• Aesthetics: Custom colorscheme integration (Flexoki support included).

Check out the repository for installation instructions.