AI & ML Engine
The artificial intelligence, machine learning, and intelligent automation layer for the entire Civic platform — every module that needs NLP, predictive analytics, computer vision, or conversational AI plugs into AI & ML Engine.
Uptime SLA
Inference Latency
Chatbot Response
Batch Throughput
Purpose-Built for Canadian Municipalities
How It Works
The identity journey, step by step
From first registration to golden record resolution — how a resident's identity evolves across the platform.
Citizen Chatbot Interaction
A resident uses the chatbot to find the right service and submit a request.
How it works
A resident visits the municipal website and asks the chatbot 'How do I report a pothole?' The chatbot recognizes the intent, retrieves the relevant knowledge base article via RAG, provides step-by-step instructions, and offers to create a service request directly in the conversation. The request is auto-categorized and routed to the roads department.
Step 1 of 5
Purpose & Scope
What this module owns
Clear ownership boundaries prevent duplication and ensure every capability has exactly one authoritative home.
Owns
11Delegated to
4Business rules specific to domains
Data storage of source data
Notification delivery
Workflow orchestration
These capabilities are handled by dedicated modules and consumed via stable API contracts — keeping boundaries clean and ownership unambiguous.
Core Capabilities
What it does
5 capability groups comprising 10 discrete capabilities — each with API surface, business rules, and data ownership.
Categorize citizen requests, emails, and complaints into service categories, and extract structured data from free text.
Text Classification
Categorize citizen requests, emails, complaints into service categories using fine-tuned models.
Entity Extraction
Extract addresses, dates, account numbers, names from free text for automated processing.
Keyword Extraction
Extract key topics from text for tagging, routing, and search optimization.
Similarity Matching
Find similar cases, duplicate requests, and related documents using semantic embedding.
Detect citizen sentiment in feedback and survey responses, and auto-summarize long documents and correspondence.
Sentiment Analysis
Detect citizen sentiment in feedback, social media, survey responses for service quality monitoring.
Summarization
Auto-summarize long documents, council reports, citizen correspondence for staff efficiency.
Language Detection
Detect English/French for bilingual routing of citizen communications.
Translation
Real-time English ↔ French translation for citizen communications across all channels.
Real-World Scenarios
Who uses this, and how
4 persona-driven scenarios showing how AI & ML Engine works in practice — from resident registration to privacy compliance.
311 Call Centre Agent
Automated Service Request Categorization
The 311 centre receives 500+ emails daily. AI auto-categorizes and routes them, reducing manual triage time by 80%.
Steps
- 1Citizen emails a complaint about a noisy construction site near their home
- 2NLP pipeline classifies the email as 'bylaw complaint — noise' with 94% confidence
- 3Entity extraction pulls out the address, date, and time of noise
- 4Sentiment analysis flags the citizen as 'frustrated' — higher priority
- 5The request is auto-created in CRM with all extracted fields pre-populated
- 6A bylaw officer receives the case with AI-generated summary and risk score
Outcome
Manual triage eliminated. The officer gets a fully populated case with context — average handling time drops from 8 minutes to under 2.
View scenario
Asset Management Engineer
Predictive Asset Degradation Modeling
The municipality needs to prioritize $50M in road rehabilitation spending across 2,000 km of roads.
Steps
- 1Historical condition data, traffic volumes, and climate data are ingested into the feature store
- 2The predictive model scores every road segment with a 5-year degradation forecast
- 3Results are ranked by cost-effectiveness — which repairs prevent the most expensive failures
- 4The engineer reviews the top 50 recommendations with explainability factors
- 5Capital plan is updated with AI-recommended rehabilitation priorities
- 6Model accuracy is tracked against actual condition assessments the following year
Outcome
Data-driven capital planning replaces subjective prioritization. The municipality extends average road life by 3 years and reduces emergency repair costs by 35%.
View scenario
French-Speaking Resident
Bilingual Chatbot Self-Service
A French-speaking resident needs to find and register for a recreation program for their child.
Steps
- 1The resident opens the chatbot on the municipal website and types in French
- 2Language detection identifies French; the bot switches to French conversation
- 3Intent recognition understands the resident is looking for children's swimming lessons
- 4The bot queries the recreation module and presents available programs matching the child's age
- 5The resident selects a program; the bot guides them through registration and payment
- 6Confirmation is sent in French via email with all program details
Outcome
Full self-service in French without staff intervention. The resident completes registration in 3 minutes instead of waiting on hold for a bilingual agent.
View scenario
Internal Audit Manager
Procurement Fraud Detection
The anomaly detection model identifies suspicious patterns in vendor invoicing that suggest potential kickback arrangements.
Steps
- 1The fraud detection model analyzes 12 months of procurement transactions
- 2It flags a cluster of invoices from 3 vendors that are consistently just below the competitive bidding threshold
- 3The model detects that all 3 vendors share a mailing address (entity resolution)
- 4An ai.anomaly_detected event triggers an alert to the internal audit team
- 5Auditors review the AI explanation showing the specific patterns and confidence scores
- 6Investigation confirms vendor collusion; procurement policy is updated with new controls
Outcome
AI catches a pattern that manual review missed. The municipality recovers $280K and implements preventive controls advised by the model's pattern analysis.
View scenario
Internal Architecture
How it's built
4 architectural layers comprising 24 components — from API gateway to data quality engine.
4 layers · 24 total components
AI & ML Engine
Every module owns a single bounded context, exposes stable APIs, and can be composed into any Civic product — that's the architecture that scales.
Krutik Parikh
Creator of Civic
Data Model
Entity Architecture
3 entities with 3 relationships — the authoritative schema for this bounded context.
Entities
Select an entity to explore its fields and relationships
API Surface
Integration Endpoints
13 RESTful endpoints across 4 resource groups — plus 6 domain events for async integration.
/api/v1/ai/nlp/classify
Text classification into service categories
/api/v1/ai/nlp/extract-entities
Entity extraction from free text
/api/v1/ai/nlp/sentiment
Sentiment analysis on citizen feedback
/api/v1/ai/nlp/summarize
Text summarization for documents and correspondence
/api/v1/ai/nlp/translate
EN ↔ FR translation for citizen communications
Ecosystem
Products that depend on this module
17 Civic products consume AI & ML Engine — making it one of the most critical platform services in the ecosystem.
AI Platform
Full ML lifecycle — this IS the AI platform spec for model registry, training, deployment, and monitoring
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CRM / 311
Auto-categorization of service requests, chatbot, sentiment analysis on citizen feedback
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Bylaw Enforcement
Risk scoring for proactive enforcement prioritization
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Building Inspection
Inspection priority scoring, image analysis of building conditions
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Asset Management
Predictive degradation models, optimal maintenance scheduling
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Utility Billing
Consumption anomaly detection (leak/theft), demand forecasting
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Property Tax
Assessment equity analysis, appeal prediction
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Parking
Occupancy prediction, dynamic pricing recommendations
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Transit
Ridership forecasting, schedule optimization, delay prediction
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Smart City / IoT
Sensor anomaly detection, predictive maintenance, traffic optimization
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Climate & ESG
GHG emission forecasting, energy optimization, flood prediction
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Fire Services
Fire risk scoring, response time optimization, incident prediction
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Emergency Management
Damage prediction, resource allocation optimization
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HR & Payroll
Attrition prediction, workforce planning
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Cybersecurity
Threat detection, anomalous access patterns
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Document & Records
Auto-classification, OCR accuracy improvement, content extraction
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Procurement
Spending analytics, vendor risk scoring, fraud detection
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Technical Specifications
Performance, Compliance & Configuration
Availability
Inference Latency
Chatbot Response
Batch Throughput
Model Monitoring
Bias Checks
Explainability
Privacy
FAQ
Frequently Asked Questions
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