Technical Specifications
Built for municipal AI at scale.
A microservices architecture purpose-built for shared AI infrastructure — from model training and governance to real-time inference and edge deployment. Every component designed for Canadian data residency, sub-500ms inference, and responsible AI governance.
99.9%
Uptime SLA
5,000+
Concurrent Users
<200ms
API Response
3
Platform Modules
Architecture Overview
The Civic AI Platform follows a modular microservices architecture with clear separation of concerns across the machine learning lifecycle. The Architecture layer provides GPU-accelerated training infrastructure and auto-scaling inference serving. The Intelligence layer encompasses NLP, computer vision, predictive analytics, and pre-trained municipal models. The Governance layer embeds bias detection, explainability, and compliance controls into the model lifecycle. The Data layer manages the feature store, data pipelines, and real-time feature computation. An Edge Runtime enables mobile and field deployment with offline-capable inference. All services communicate through event-driven messaging (Apache Kafka) and synchronous APIs (REST/gRPC), with a centralized API Gateway providing authentication, rate limiting, and request routing.
Platform Modules
Core Services
Eleven core microservices organized into four architectural tiers — each independently deployable, horizontally scalable, and monitored through distributed tracing and structured logging.
Total Modules
3
Protocol
REST / gRPC
Bus
Async Events
Container
Kubernetes
Database
PostgreSQL 16
Specifications
Technical Details
Browse specifications by category. All values reflect current production configuration.
GPU Compute
NVIDIA A100/T4 or CPU-only option
Training Orchestration
Apache Airflow + Kubernetes Jobs
Model Serving
TensorFlow Serving + Triton Inference Server
Feature Store
Online (Redis) + Offline (PostgreSQL/Parquet)
ML Frameworks
TensorFlow, PyTorch, scikit-learn, XGBoost, ONNX
Experiment Tracking
MLflow with artifact versioning
Uptime
99.9% for inference endpoints, 99.5% for training infrastructure Availability SLA
Distributed tracing (Jaeger/OpenTelemetry) across all microservices, Prometheus metrics for resource utilization, Grafana dashboards for real-time AI platform health, and PagerDuty integration for automated alerting on model performance degradation, inference failures, and data pipeline anomalies.
99.953%
30-Day Avg
1
Incidents
3× DC
Redundancy
< 15min
Recovery
30-Day Uptime History
All Systems Operational
Deployment
Deployment Options
On-premises private cloud or municipal-hosted Kubernetes cluster. All services containerized as OCI-compliant images. Helm charts for Kubernetes deployment with configurable resource profiles. GPU node pools for training and inference workloads.
On-premises private cloud deployment
Municipal-hosted Kubernetes cluster
Hybrid cloud with Canadian data residency