Overview#
The Review Queue System centralises every workflow that requires human judgement: items enter a queue, are assigned to the right reviewer based on workload and expertise, and are tracked through to completion with a full audit trail and SLA monitoring.
Consider a digital forensics team processing evidence from a major financial fraud case, with hundreds of items waiting for classification, quality check, and investigation linking on any given day. Without a structured queue, items get missed, SLAs slip, and supervisors have no visibility into where things stand.
The system supports multiple queue types including evidence review, AI suggestion approval, data quality validation, compliance checks, and administrative approvals. Each queue has tailored configuration for its purpose, but all share the same priority management, assignment, and analytics infrastructure.
Key Features#
- Multiple Queue Types: Distinct queues for evidence review, AI suggestion approval, compliance verification, and administrative sign-offs, each with tailored configurations and review stages.
- Priority-Based Sorting: Automatic ordering by urgency, criticality, and business rules so reviewers always work on the most important items first.
- Intelligent Assignment: Work distribution through round-robin, skill-based routing, and workload balancing algorithms with escalation rules and manual override capabilities.
- SLA Tracking and Alerts: Processing time monitored against service level targets, with proactive alerts for items approaching or exceeding deadlines so compliance-critical reviews stay on schedule.
- Configurable Review Stages: Multi-step review workflows with approval and rejection actions, comments, annotations, and evidence attachment at each stage.
- Batch Operations: Process multiple related items simultaneously for efficient handling of high-volume queues, with bulk approve, reject, and reassign capabilities. Bulk approval of AI-suggested entities promotes each item with its correct security clearance and writes an individual audit record for every promotion.
- Clearance-Aware Promotion: Approve, edit, and reject actions on AI-suggested entities enforce the acting reviewer's security clearance before an entity is promoted to a live profile, so an approver can only promote records at classification levels they hold. Promoted classified persons remain hidden from readers without the appropriate clearance.
- Reviewer Performance Analytics: Track queue depth, processing times, reviewer throughput, SLA compliance rates, and trend visualisation to identify bottlenecks and optimise workflows.
- Complete Audit Trail: Every review action, decision, and comment recorded for accountability, compliance reporting, and process improvement. All audit data is stored with full organisation-level isolation.
Use Cases#
- Processing incoming evidence for investigations with automatic queue assignment, metadata verification, classification review, quality assessment, and investigation linking.
- Reviewing AI-generated suggestions before they are applied, including smart field recommendations, entity extraction results, classification proposals, and duplicate detection matches. Extracted entities are matched against existing records, and human approval, with the reviewer's clearance enforced, is required before a suggestion becomes a live profile.
- Clearing a backlog of AI-extracted entities with a single bulk approval, where each promotion carries the correct clearance and its own audit entry, and a classified informant profile created from evidence stays invisible to staff without the appropriate clearance.
- Ensuring regulatory compliance through structured review of disclosure packets, redaction verification, chain of custody validation, export approvals, and retention decisions.
- Managing administrative approvals such as access requests, configuration changes, and policy exceptions with appropriate routing and oversight documentation.
Integration#
Connects with role-based access control, single sign-on, configurable data import and export workflows, real-time event notifications, and standards-based data exchange protocols. The review queue serves as the central approval hub across the platform's investigation, compliance, and AI-assisted workflows.
Open Standards#
- JSON Web Token (RFC 7519) and OAuth 2.0 Bearer Token (RFC 6750): Every authenticated workflow requires a signed JWT presented as an HTTP Bearer token, with role-based permissions derived from the token claims.
- WebSocket Protocol (RFC 6455): Live queue updates are delivered over WebSocket connections, so reviewers receive assignment and status-change notifications in real time without polling.
- ISO 8601 Date and Time: All timestamps recorded in queue items, audit decisions, SLA deadlines, and analytics snapshots are serialised as UTC ISO 8601 strings, ensuring unambiguous ordering for chain-of-custody purposes.
- W3C Web Content Accessibility Guidelines (WCAG) 2.2: The reviewer workspace is built against WCAG 2.2 guidance, with WAI-ARIA roles, accessible labelling, and full keyboard navigation on decision controls.
- HTTP Semantics (RFC 9110): Outbound webhook notifications, dispatched to configured endpoints on review decisions, are delivered as HTTPS POST requests with JSON payloads, conforming to standard HTTP request/response semantics.
- Role-Based Access Control (NIST RBAC model, ANSI INCITS 359): The queue enforces a role hierarchy with named permissions governing which operations each role may perform, consistent with the NIST flat and hierarchical RBAC model.
Last Reviewed: 2026-07-16 Last Updated: 2026-07-16