Product Experience
Every role. One platform.
From front-counter staff handling resident requests to the CAO reviewing council-ready reports — every role has a purpose-built journey. Explore how Civic AI Platform works for your team.
Watch the 3-Minute Demo
See Civic AI Platform handle a complete resident service request — from intake through resolution and council reporting.
Request Video AccessTry It Now
Explore the Interface
Click through the actual Civic AI Platform interface. Navigate between the dashboard, resident profiles, service requests, and reports to see how everything connects.
Role-Based Journeys
One Platform, Every Perspective
Select a role to explore their complete journey through Civic CRM — from day-one onboarding to daily workflows and strategic outcomes.
AI Platform Administrator
From Model Development to Production Governance
The AI administrator's journey — managing the ML Model Registry, deploying pre-trained models, monitoring production performance, configuring bias detection, and ensuring every model meets governance requirements before reaching citizens.
Register
Model catalog entry
Register a new model in the ML Model Registry — capturing training data lineage, algorithm selection, accuracy metrics, and bias assessment results. Version management ensures full history of every model iteration.
The ML Model Registry (spec 1.1) provides a central catalog for all AI models with semantic versioning, metadata (training data, accuracy, bias metrics, latency benchmarks), deployment status, and lineage tracking. Model comparison tools enable side-by-side evaluation across versions. Each model entry records training pipeline configuration, feature store dependencies, and governance classification (Type I–IV per Treasury Board Directive).
Train
Pipeline execution
Launch automated training pipeline — data ingestion, feature engineering, hyperparameter tuning (Bayesian optimization), cross-validation, and model selection. GPU/CPU resource management with department-level quotas.
The Training Pipeline (spec 1.2) orchestrates end-to-end: data ingestion from platform data sources, feature engineering with reusable transformation pipelines, algorithm selection from library (scikit-learn, TensorFlow, PyTorch, XGBoost), hyperparameter tuning, and cross-validation with stratified sampling. GPU/CPU resource management enforces quotas per department. Training jobs are scheduled with priority queuing and real-time monitoring dashboards.
Validate
Bias & fairness testing
Mandatory pre-deployment bias testing against fairness metrics — demographic parity, equalized odds, predictive parity — across all protected characteristics. No model enters production without passing governance review.
The Bias Detection & Mitigation system (spec 5.1) evaluates every model against fairness metrics across protected characteristics (race, gender, age, income, geography). Pre-deployment gates block production deployment until bias testing passes. If bias is detected, mitigation strategies include re-sampling training data, re-weighting examples, adversarial debiasing, and post-processing calibration. Results recorded in the governance dashboard.
Deploy
Blue-green rollout
Deploy to production via blue-green deployment for zero downtime, or canary deployment with configurable traffic percentage. Auto-scaling inference serves demand. REST and gRPC endpoints activate immediately.
Model Deployment & Serving (spec 1.3) provides containerized serving with auto-scaling, load balancing, and health monitoring. Blue-green deployment ensures zero-downtime updates. Canary deployments allow gradual rollout with automated rollback on performance degradation. Model serving metrics track latency (p50, p95, p99), throughput, error rate, and resource utilization. REST and gRPC inference endpoints with batching for high-throughput scenarios.
Monitor
Drift detection
Continuous production monitoring — accuracy drift, data distribution drift (KS test, PSI), concept drift, and feature importance changes. Automated alerts trigger retraining when performance degrades below thresholds.
Model Monitoring (spec 1.4) continuously evaluates production performance: prediction accuracy drift against ground truth, data distribution drift using statistical tests (KS test, PSI, chi-squared), concept drift detection, and feature importance changes. Automated alerts fire when thresholds are exceeded. Retraining triggers automatically initiate the training pipeline with latest data while preserving the audit trail of previous versions.
Govern
Transparency reporting
Generate quarterly AI transparency reports for council — model inventory, fairness assessments, incident summary, and improvement plans. Annual report auto-compiled for public publication.
The AI Governance Dashboard (spec 5.3) provides centralized visibility: model inventory with Type I–IV risk classification, usage tracking showing inference volumes per consuming application, compliance status, and incident tracking with root cause analysis. Quarterly and annual AI transparency reports are auto-generated compiling all model assessments, bias testing results, incidents, and corrective actions for council review and public publication.
AI Lifecycle
0 / 6
Step 01
Register
Model catalog entry
Register a new model in the ML Model Registry — capturing training data lineage, algorithm selection, accuracy metrics, and bias assessment results. Version management ensures full history of every model iteration.
The ML Model Registry (spec 1.1) provides a central catalog for all AI models with semantic versioning, metadata (training data, accuracy, bias metrics, latency benchmarks), deployment status, and lineage tracking. Model comparison tools enable side-by-side evaluation across versions. Each model entry records training pipeline configuration, feature store dependencies, and governance classification (Type I–IV per Treasury Board Directive).
Step 02
Train
Pipeline execution
Launch automated training pipeline — data ingestion, feature engineering, hyperparameter tuning (Bayesian optimization), cross-validation, and model selection. GPU/CPU resource management with department-level quotas.
The Training Pipeline (spec 1.2) orchestrates end-to-end: data ingestion from platform data sources, feature engineering with reusable transformation pipelines, algorithm selection from library (scikit-learn, TensorFlow, PyTorch, XGBoost), hyperparameter tuning, and cross-validation with stratified sampling. GPU/CPU resource management enforces quotas per department. Training jobs are scheduled with priority queuing and real-time monitoring dashboards.
Step 03
Validate
Bias & fairness testing
Mandatory pre-deployment bias testing against fairness metrics — demographic parity, equalized odds, predictive parity — across all protected characteristics. No model enters production without passing governance review.
The Bias Detection & Mitigation system (spec 5.1) evaluates every model against fairness metrics across protected characteristics (race, gender, age, income, geography). Pre-deployment gates block production deployment until bias testing passes. If bias is detected, mitigation strategies include re-sampling training data, re-weighting examples, adversarial debiasing, and post-processing calibration. Results recorded in the governance dashboard.
Step 04
Deploy
Blue-green rollout
Deploy to production via blue-green deployment for zero downtime, or canary deployment with configurable traffic percentage. Auto-scaling inference serves demand. REST and gRPC endpoints activate immediately.
Model Deployment & Serving (spec 1.3) provides containerized serving with auto-scaling, load balancing, and health monitoring. Blue-green deployment ensures zero-downtime updates. Canary deployments allow gradual rollout with automated rollback on performance degradation. Model serving metrics track latency (p50, p95, p99), throughput, error rate, and resource utilization. REST and gRPC inference endpoints with batching for high-throughput scenarios.
Step 05
Monitor
Drift detection
Continuous production monitoring — accuracy drift, data distribution drift (KS test, PSI), concept drift, and feature importance changes. Automated alerts trigger retraining when performance degrades below thresholds.
Model Monitoring (spec 1.4) continuously evaluates production performance: prediction accuracy drift against ground truth, data distribution drift using statistical tests (KS test, PSI, chi-squared), concept drift detection, and feature importance changes. Automated alerts fire when thresholds are exceeded. Retraining triggers automatically initiate the training pipeline with latest data while preserving the audit trail of previous versions.
Step 06
Govern
Transparency reporting
Generate quarterly AI transparency reports for council — model inventory, fairness assessments, incident summary, and improvement plans. Annual report auto-compiled for public publication.
The AI Governance Dashboard (spec 5.3) provides centralized visibility: model inventory with Type I–IV risk classification, usage tracking showing inference volumes per consuming application, compliance status, and incident tracking with root cause analysis. Quarterly and annual AI transparency reports are auto-generated compiling all model assessments, bias testing results, incidents, and corrective actions for council review and public publication.
Ready to Transform Your Municipality?
See Civic AI Platform in your environment
Schedule a personalized walkthrough with our municipal solutions team. We’ll configure a demo environment to match your municipality’s structure.