Client Portal — controlled project access
Project intelligence, evidence, and delivery in one controlled workspace.
The AGM Groups Client Portal provides active clients with a structured view of project progress, validated outputs, evidence records, and delivery artifacts — scoped by organization, project, role, and review workflow.
Project Workspace
Delivery Command View
Controlled
Capture Coverage
Field data streams
82%
Processing Status
AI-assisted review
68%
QC Queue
Evidence validation
46%
Delivery Package
Exports & reports
74%
Data Layers
Imagery
Assets
Conditions
Review
Flags
Evidence
Approvals
Delivery
GIS
Reports
Archive
Capture
Process
Validate
Deliver
Workspace Model
Designed for delivery teams, reviewers, and decision-makers.
Each client workspace is organized around the data chain: capture status, processing milestones, review evidence, validated outputs, and final deliverables.
Progress Command
Track coverage, processing status, review queues, QC flags, and delivery milestones across project areas.
- Coverage and completeness views
- Processing and validation status
- Milestone and review notes
Evidence Review
Review imagery, map context, observations, flags, and validation status through project-scoped interfaces.
- Evidence-linked records
- QC and approval workflows
- Traceable review history
Delivery Outputs
Access structured outputs prepared for planning, reporting, GIS integration, audit support, and decision workflows.
- GIS-ready exports
- Reports and summaries
- Archived delivery packages
Access Governance
Controlled access for project-specific delivery.
Project-scoped access
Access is controlled per organization and project so users see only the workspaces and deliverables assigned to them.
Least-privilege roles
User access is organized around practical workflows such as viewing, review, approval, and download.
Evidence traceability
Outputs are connected to review records, source evidence, and delivery context for audit-friendly project handoff.
Model & data boundaries
Client data is not used to train shared models unless explicitly agreed in writing.
