Architecture: Multi-Cloud Privacy Pipeline (Heroku + Salesforce Agentforce)
This project addresses the inherent PII (Personally Identifiable Information) liability in municipal reporting. By implementing a "Privacy Wall" architecture, sensitive biometric data (faces) and vehicle identifiers (license plates) are redacted at the edge before data ingestion into the CRM.
The architecture utilizes a zero-trust gateway to ensure compliance with data sovereignty and privacy regulations (GDPR/SOC2).
A Python-based microservice hosted on Heroku utilizes YOLOv8 and MediaPipe to perform real-time computer vision analysis.
To account for edge cases where AI confidence intervals fall below 0.6, I implemented a deterministic fallback logic. This secondary layer applies a calculated Gaussian Global Head-Zone Blur based on image dimensions, ensuring no PII enters the Salesforce environment even if specific feature detection is obstructed.
The redacted payload is processed by the Austin311Analysis Prompt Template. This allows the AI Agent to perform complex visual categorization against the Toronto 311 taxonomy without exposing the municipality to raw biometric data.
To ground the AI Agent in real-world municipal logic, I performed a multi-day ETL and normalization process on the City of Toronto's Open Data portal:
- Taxonomy Mapping: Extracted and cleaned 371 unique Service Request types from raw municipal datasets.
- Semantic Vector Search: Architected the custom schema to support Vector Embeddings, enabling the Agent to perform semantic matches between citizen-uploaded imagery and the municipal taxonomy.
- Data Integrity: Conducted an extensive ETL process to ensure the taxonomy was deduplicated and optimized for high-accuracy vector retrieval.
| Component | Identifier | Functional Role |
|---|---|---|
| Prompt Template | Austin311Analysis | Generative Vision & Taxonomy Mapping |
| Vector Database | Einstein Vector Store | Semantic Search & Grounding |
| Orchestration Flow | Analyze_311_Photo_Flow | Multi-Cloud Transaction Management |
| Apex REST Handler | AustinAgentREST.cls | Secure External Data Ingestion |
| Custom Object | Service_Request_Type__c | Municipal Service Taxonomy Schema |
- Environment: Python 3.11
- Computer Vision: OpenCV, MediaPipe, Ultralytics (YOLOv8)
- Authentication: RSA-256 JWT Bearer Tokens
- /heroku: ML Microservice and Redaction Logic.
- /force-app: Salesforce Metadata (Apex, Flows, Objects, Prompt Templates).
- /scripts: Data Engineering scripts for Toronto Open Data ETL.