Enterprise-grade, event-driven anomaly detection pipeline with sub-millisecond ONNX inference.
Sentinel is an enterprise-grade, real-time fraud detection system. It simulates high-throughput financial transactions via streaming (Redpanda/Kafka) and evaluates them in milliseconds using an optimized ONNX inference engine.
Peak Performance Benchmark: Sustaining 25,300+ RPS with 7.79ms average latency over 4.5 million requests.
Note: Note: Peak 25,300+ RPS was achieved under optimal hardware conditions. Standard local Docker Desktop deployments typically yield ~18,000+ RPS due to local CPU and network bridge constraints.
graph TD
%% Styling
classDef go fill:#00ADD8,stroke:#fff,stroke-width:2px,color:#fff;
classDef rust fill:#DEA584,stroke:#fff,stroke-width:2px,color:#000;
classDef python fill:#FFD43B,stroke:#306998,stroke-width:2px,color:#306998;
classDef infra fill:#f9f9f9,stroke:#333,stroke-width:2px;
classDef storage fill:#ff9900,stroke:#fff,stroke-width:2px,color:#fff;
classDef broker fill:#8B0000,stroke:#fff,stroke-width:2px,color:#fff;
%% Nodes
Client([Client / K6 Load Tester])
subgraph API Layer
Gateway[Go Gin Gateway]:::go
end
subgraph Streaming & Validation
Redis[(Redis<br>Idempotency)]:::infra
Broker1{Redpanda<br>raw-events}:::broker
Validator[Rust Stream Processor]:::rust
Broker2{Redpanda<br>clean-events}:::broker
end
subgraph AI & Persistence
Consumer[Python Inference Engine<br>ONNX Model]:::python
S3[(AWS S3 / LocalStack<br>Audit Logs)]:::storage
DB[(PostgreSQL)]:::infra
end
%% Edges (Flow)
Client -->|HTTP POST| Gateway
Gateway -->|1. Check tx_hash| Redis
Redis -.->|Duplicate? Block| Gateway
Gateway -->|2. Fire & Forget| S3
Gateway -->|3. Publish| Broker1
Broker1 -->|4. Consume Batch| Validator
Validator -->|5. Type Check & Validate| Validator
Validator -->|6. Publish Validated| Broker2
Broker2 -->|7. Consume Batch| Consumer
Consumer -->|8. Fraud Inference| Consumer
Consumer -->|9. Persist Result| DB
# 1. Clone repository
git clone https://github.com/enesgulerdev/sentinel.git
cd sentinel
# 2. Configure environment (Requires Google Drive File ID for gdown)
cp .env.example .env
# 3. Install dependencies via uv
task env:install
# 4. Execute ML Pipeline (Fetch dataset, preprocess, train baseline)
task ml:pipeline
# 5. Start microservices (API Gateway, Redpanda, etc.)
task docker:on
# 6. Run load tests to verify system performance
k6 run tests/fixtures/loadtest.js
task docker:on # Start all services
task docker:down # Stop gracefully (keeps images intact)
task docker:off # Full wipe (removes containers, networks, volumes, images)
| Service | Local URL |
|---|---|
| API Gateway | http://localhost:8000 |
| Redpanda | http://localhost:8080 |
Explore the sub-modules for advanced deployment, scaling, and observability patterns:
| Module / Component | Description |
|---|---|
| Testing Suite | Unit, integration, and mock fixtures. |
| Helm Workloads | Autonomous local provisioning for stateful dependencies and isolated ML workloads. |
| GitOps & CD | Zero-touch deployment architecture using ArgoCD and Jenkins for deterministic state synchronization. |
| AWS FinOps Simulation | Infracost model demonstrating system scale to 25,300+ RPS with an 85% cost reduction under enterprise conditions. |
| Policy & Governance | Enterprise Policy-as-Code standards enforcing infrastructure, container, and Kubernetes security via OPA/Rego. |
| AI Release Agent | Autonomous, AI-driven Python agent for dynamic Semantic Versioning and automated release notes generation via Gemini. |
| AI Doc Agent | Autonomous, AI-driven Engineering Council that analyzes git diffs to generate weekly persona-based architectural reviews. |
| Local AWS Simulation | Fully offline AWS S3 audit logging and event routing simulation via LocalStack. |