Questions & Answers
Frequently Asked Questions
Common questions about Document Records Management
Civic Document & Records Management is a full Enterprise Content Management / Electronic Document and Records Management System (ECM/EDRMS) purpose-built for Ontario municipalities. It covers the complete records lifecycle — from document capture, AI-powered classification, and collaborative editing through automated retention enforcement, MFIPPA (FOI) processing, and authorized disposition with destruction certificates. Built on the Civic platform with Canadian-only data residency.
Unlike shared drives or basic DMS platforms, Civic Document & Records Management provides records lifecycle management — classification with retention schedules, automated disposition workflows, legal hold enforcement, and destruction certificates. AI auto-classification eliminates manual filing burden. MFIPPA processing is built-in rather than bolted on. Full audit trails satisfy Municipal Act s.254–255 retention requirements. Records are declared, retained, and disposed per bylaw — not just stored.
Yes. Digital records (documents, emails, scanned images) are managed in the electronic repository. Physical records (boxes, files, folders) are tracked via the Box Registry with barcode assignment, storage facility mapping (facility → room → shelf → bay → position), retrieval request management, and physical destruction scheduling with vendor coordination. Both digital and physical records share the same classification codes and retention schedules.
TOMRMS (The Ontario Municipal Records Management System) classification templates are pre-loaded with support for custom classification schemes. The hierarchical file plan supports primary → secondary → tertiary classification codes with cross-referencing. AI auto-classification suggests codes upon upload with confidence scoring. Custom classification schemes can be layered on top of or replace TOMRMS templates.
Upon document upload, the AI Classification Model analyzes document content, metadata, and structure to identify document type and suggest classification codes with a confidence score. High-confidence documents (above a configurable threshold) are auto-filed without staff intervention. Low-confidence documents queue for records staff review. Every correction trains the model for improved accuracy — the system gets smarter over time, targeting 70%+ accuracy.
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