Survey Analysis Agent
Turns drone and LiDAR captures into volumes and progress
Tool: Volume calculator · Source: Drone and LiDAR surveys
Industry
Conceptual visualization onlyMining AI agent operations
Open-pit and underground operations with survey, fleet and geotechnical monitoring
Operational layers in view
Pit surveyDrone photogrammetryFleet telemetryGeotechnical sensorsGrade model
Survey Analysis Agent
Geotechnical Agent
Fleet Optimisation Agent
Grade Control Agent
Environmental Agent
Safety Agent
Node activity below is a demonstration using sample mining data.
Ask the live agents
Describe the problem in your own words. We classify the industry, assign specialist agents, and you watch the workflow run and produce the visual deliverables — maps, dashboards, charts, tables and reports — instead of one block of text.
Demo mode uses clearly labelled sample data — never presented as verified.
Assigned specialists
Mining
- Survey Analysis Agent
- Geotechnical Agent
- Fleet Optimisation Agent
- Grade Control Agent
- Environmental Agent
- Safety Agent
Deliverables planned
Interactive map
Data tables
Charts
Methodology and limitations
Cited sources
Pauses for human approval
Mine manager approval
Workflow the run follows
- 1. User request
- 2. Requirement validation
- 3. Industry classification
- 4. Specialist agent selection
- 5. Data and file collection
- 6. Tool authorisation
- 7. Execution plan
- 8. Multi-agent collaboration
- 9. Analysis or generation
- 10. Quality validation
- 11. Human approval
- 12. Visual deliverables
- 13. Monitoring and audit
Demo
Mining AI agent operations centre
Workflow: Survey and telemetry received → Monitor pit · Run pending · started — · elapsed 0s
Active agents
0
Tasks running
0
Completed steps
0/9
Tool calls
0
Human approvals
1
Warnings
0
Success rate
0%
Est. completion
36s
Active agents
Pending
Geotechnical Agent
Monitors slope movement and stability thresholds
Tool: Volume calculator · Source: Drone and LiDAR surveys
Fleet Optimisation Agent
Improves haul cycles and queue times
Tool: Volume calculator · Source: Drone and LiDAR surveys
Grade Control Agent
Estimates ore grade and dilution by block
Tool: Volume calculator · Source: Drone and LiDAR surveys
Workflow canvas
Live activity stream
No activity yet. Start the demo run to watch mining agents work through the workflow with sample data.
Demonstration using sample data — no real client operation is shown. Sign in and connect an approved AI provider and authorised industry data source to activate live execution.
Mining specialist agents
Each agent owns one step of the workflow, with its own tools, inputs and approval level.
Autonomous
Survey Analysis Agent
Turns drone and LiDAR captures into volumes and progress
- Inputs:
- Drone and LiDAR surveys, Fleet management data, Slope monitoring sensors
- Tools:
- Volume calculator, Slope stability model, Fleet optimiser
Approval required
Geotechnical Agent
Monitors slope movement and stability thresholds
- Inputs:
- Drone and LiDAR surveys, Fleet management data, Slope monitoring sensors
- Tools:
- Volume calculator, Slope stability model, Fleet optimiser
Autonomous
Fleet Optimisation Agent
Improves haul cycles and queue times
- Inputs:
- Drone and LiDAR surveys, Fleet management data, Slope monitoring sensors
- Tools:
- Volume calculator, Slope stability model, Fleet optimiser
Autonomous
Grade Control Agent
Estimates ore grade and dilution by block
- Inputs:
- Drone and LiDAR surveys, Fleet management data, Slope monitoring sensors
- Tools:
- Volume calculator, Slope stability model, Fleet optimiser
Autonomous
Environmental Agent
Tracks dust, water and rehabilitation obligations
- Inputs:
- Drone and LiDAR surveys, Fleet management data, Slope monitoring sensors
- Tools:
- Volume calculator, Slope stability model, Fleet optimiser
Approval required
Safety Agent
Flags exposure and exclusion-zone breaches
- Inputs:
- Drone and LiDAR surveys, Fleet management data, Slope monitoring sensors
- Tools:
- Volume calculator, Slope stability model, Fleet optimiser
Solution blueprint
Operational context
Industry challenges
- Operational data for mining is spread across disconnected systems
- Analysis-heavy work is repeated manually every cycle
- Risks and exceptions are found late
- Reporting and audit overhead grows with volume
Solution patterns
Recommended AI solutions
- Specialist agents that own one step of the workflow each
- Grounded retrieval over your permitted systems only
- Predictive scoring with explainable reasons
- Approval-gated execution into your systems of record
Data readiness
Required data
- Drone and LiDAR surveys
- Fleet management data
- Slope monitoring sensors
- Assay and grade records
Controlled process
Example workflow
- Survey and telemetry received
- Validate captures
- Compute volumes
- Assess slope stability
- Optimise haul cycles
Connection points
Integration opportunities
- Mine planning software
- Fleet management
- GIS services
- ERP maintenance
Measure after baseline
Expected outcomes
- Analysis cycle time
- Manual review effort
- Decision auditability
- Earlier risk detection
Governance controls
Security and compliance
- Row-level security scopes every read to the requesting organisation
- Retrieval is limited to documents the user may already open
- Every agent action, tool call and approval is written to the audit log
- Sensitive steps pause until a named human approves them
Inputs, integrations and outputs
Inputs
- • Drone and LiDAR surveys
- • Fleet management data
- • Slope monitoring sensors
- • Assay and grade records
Integrations
- • Mine planning software
- • Fleet management
- • GIS services
- • ERP maintenance
Outputs
- • Volume and progress report (PDF, CSV)
- • Geotechnical risk map (GeoJSON, PDF)
- • Fleet performance dashboard (Dashboard)
Business outcomes to baseline and measure
Analysis cycle time
Target and measurement method agreed during discovery.
Manual review effort
Target and measurement method agreed during discovery.
Decision auditability
Target and measurement method agreed during discovery.
Earlier risk detection
Target and measurement method agreed during discovery.
No performance result is promised before a baseline is measured. Targets are agreed for each engagement and reported against real delivery data.
How This AI Agent Works
The process the mining agent team follows, from trigger to approved deliverable.
Mining suitability workflow
From a study area to a geologist-approved suitability map and ranked zones.
Workflow preview
Saved with the run so the recorded execution trail shows what was asked. Demonstration runs use sample data. They do not touch your systems.
Public visitors can preview the structure and inspect every node. Labelled demonstrations and live runs require a signed-in account with configured providers.
72%
Workflow summary as a text list
- 1. Define study area: Input. Licence block or exploration area supplied as spatial data.
- 2. Geological and mineral data: Data source. Load approved geology layers and mineral occurrence records.
- 3. Terrain and infrastructure: Data source. Add slope, access, water and power proximity layers.
- 4. Environmental constraints: Validation. Apply protected areas and exclusion buffers.
- 5. Weighted suitability model: Approved tool. Run the weighted overlay across the prepared layers.
- 6. Rank candidate zones: AI agent. Rank zones with the contribution of each criterion.
- 7. Geologist and GIS approval: Human approval. A geologist approves before the map is issued. Human approval required.
- 8. Suitability map and report: Output. Ranked zones, suitability map and method report.
Approval gate: Geologist and GIS approval. Deliverable: Map.
Workflow audit for this page
Structure only. Run a demonstration to include events.
| Template ID | Nodes | Approval node | Output type | Preview | Demo | Live | Mobile test |
|---|---|---|---|---|---|---|---|
| mining-suitability-workflow | 8 | Yes. Policy: always | Map | Available | Available (signed in) | Available via Studio · requires ai_provider, python_service | Verified at 390px |
Demonstration runs are stored and replayed from recorded events. Live runs execute server-side in the Studio and only start when the listed integrations report healthy.
Relevant case study
Illustrative case study
Wastewater network GIS for a national water utility
A wastewater network needed utility-GIS structuring, mapping and quality-controlled network intelligence for daily operations.
View all case studiesReady to scope your AI initiative?
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