Industries.

Modules

• Asset Tree

  The full hierarchy: site → building → level → room → asset class → component, drill-down in one click.

• Condition Assessment

  The structured room-by-room assessment with grades, photos, quantities and Current Replacement Cost.

• Inspection Cycles

  Schedules recurring inspections so condition data never goes stale.

• Field Survey

  Captures condition in the field, on device, offline.

• Capital Plan

  The year-by-year renewal programme by asset, room and building.

• Funding Optimiser

  Test annual budget caps and see the backlog consequence.

• Scenario Modeller & Scenario Compare

  Model deferral, inflation and climate stress, then compare scenarios side by side.

• Defect Backlog

  Rank defects by urgency and cost.

• Defect Cascade

  Predict which deferred defect triggers a bigger failure downstream.

• Work Orders / Maintenance

  Create, assign and track work orders.

• Predictions

  Flag equipment likely to fail before it does.

Problems solved

• “I don't know what we own.”

  Every building, room and component in one register, quantities, rates, grades and replacement costs.

• “Our condition data is five years old.”

  Condition is continuous, re-scan a room and grades update; close a work order and remaining life recalculates.

• “It takes two weeks to answer a director's question.”

  Ask AssetMind in plain English and the report is there in seconds.

• “Our Forward Works Programme is a spreadsheet I rebuild every year.”

  The 20-year FWP is generated from assessment data and updates itself as conditions change.

• “I can't tell you what happens if we defer $200k of work.”

  The Funding Optimiser shows the ten-year backlog consequence of any budget cap before you commit.

• “We replace things after they fail.”

  Predictions flag equipment before it fails and cascade modelling shows which deferred fix becomes a bigger failure.

• “Nobody can find the photos or the history.”

  Every photo, scan, work order and record is attached to the asset it belongs to.

• “We pay a consultant to re-discover what we already know.”

  Condition assessment is built in and continuous. Consultant spend shifts to specialist work only.

• “Reports take a week and the numbers don't tie out.”

  One-click reports where every number traces back to its source record. Audit-ready by default.

• “Six systems, three people and a shared drive, and I'm the join.”

  Register, grades, photos, work orders, costs and reports live in one place.

Modules

• Evidence Classification

  Every result classified as measured, derived or modelled. Confidence matched to evidence quality.

• The Continuous Engineering Process

  One connected process, not five disconnected systems. Every intervention traced to evidence.

• The Decision Layer

  Network Command Centre and Network Decision Room. A common operational picture with governed decision review and approval.

• Predictive Track Geometry

  Analyses track geometry at sample, defect, segment, corridor and network level. Rate-of-change run comparison and geometry forecasting with confidence.

• Track Condition Index

  Consistent network-wide condition comparison with deterioration velocity, peer comparison and intervention simulation.

• Welded Track Stability Analysis

  Stress free temperature register, measured vs inferred SFT evidence, stability loss and temperature headroom.

• Rail Condition & Fracture Intelligence

  Rail wear, defect lifecycle records, crack growth scenarios and transparent fracture risk assumptions.

Problems solved

• “I can't tell if this is measured data or a model guess.”

  Every result is classified: measured, derived or modelled. Confidence matched to evidence quality.

• “Our geometry data lives in a spreadsheet from the last vehicle run.”

  Continuous geometry strip charts with exceedance consolidation and rate-of-change comparison across successive runs.

• “We diagnose failures after they happen.”

  Detect → Diagnose → Decide → Execute → Verify. Each stage produces the evidence the next depends on.

• “Decisions are made in meetings with no traceable record.”

  The Network Decision Room converts findings into controlled, traceable actions with review, approval and verification.

• “Our welded rail stability assessment uses inferred temperatures as facts.”

  Measured stress free temperature evidence is differentiated from inferred values. Stability conclusions carry evidence quality.

• “We don't know if the last intervention actually worked.”

  Verify compares condition before and after. Record effectiveness. Feed verified outcomes back into the models.

Modules

• Source-to-Market Production Model

  Extraction, processing, transport and market delivery as a single capacity-constrained flowsheet.

• Operational Asset Models

  Equipment, pressure systems, ground control zones, tailings facilities and safety-critical elements linked to the stages they serve.

• Reliability Execution

  Preventive, predictive and corrective work prioritised by failure consequence, production exposure and intervention readiness.

• Critical Control & Integrity Assurance

  Safety-critical elements, SIFs, pressure systems and tailings governed against performance standards and TARP triggers.

• Value-at-Stake Economics

  Lost tonnes, downtime, energy and risk translated into margin exposure using the operator's own commodity price and margin-per-unit.

• Governed Decisions with Verified Outcomes

  Structured proposal → approval → implementation → verification lifecycle with independently verified savings ledger.

Problems solved

• “Our production, maintenance and integrity data live in four different systems.”

  One governed operating picture connecting the full value chain from pit to port.

• “We prioritise work by condition severity, not production impact.”

  Work is ranked by failure consequence and production exposure. The work that protects the most margin is done first.

• “Overdue proof tests and bypassed trips surface too late.”

  Critical control assurance with performance standards, proof-test intervals and TARP trigger tracking.

• “Our capital plans use generic benchmarks, not our actual margin.”

  Value-at-stake economics using your commodity price, recovery, utilisation and margin-per-unit.

• “We have no traceable record of why a decision was approved.”

  Every material recommendation moves through proposal → approval → implementation → verification with evidence basis retained.

• “Our models never learn from whether the intervention worked.”

  Verified outcomes are measured, learning deltas recorded, and savings independently verified, not self-attested.

Modules

• Visual, Component-Level Asset Register

  Network → region → substation → asset system → equipment → component.

• 3D Digital Twins and Spatial Intelligence

  Navigate a digital representation of a facility and select individual assets to access their records.

• Condition-Based Asset Management

  Actual condition informs maintenance and replacement decisions, not just age and fixed schedules.

• AI-Assisted Condition Intelligence

  Reduce manual effort reviewing large volumes of inspection information while improving consistency.

• Predictive Maintenance and Lifecycle Planning

  Proactive maintenance informed by deterioration, maintenance history and replacement costs.

• Dynamic Forward Works and Capital Planning

  Capital forecasts connected directly to the underlying asset register and condition information.

• Integration with Existing Enterprise Systems

  Complements EAM, GIS, finance and document management systems rather than replacing them.

Problems solved

• “Asset information is distributed across multiple systems, teams and contractors.”

  One connected layer of asset intelligence alongside existing operational systems.

• “Inspection results are difficult to compare consistently across sites.”

  Structured condition assessments with AI-assisted classification and cross-portfolio pattern detection.

• “Maintenance decisions are driven by age and fixed schedules.”

  Condition-based asset management reflecting actual state, environmental exposure and loading history.

• “Long-term replacement programs are static spreadsheets.”

  Dynamic forward works that update when new inspection data arrives. A living model, not a static plan.

• “Asset owners have limited visibility of contractor-maintained infrastructure.”

  Common environment for reviewing information from different contractors with consistent asset hierarchies.

• “Engineering teams spend weeks consolidating information for capital planning.”

  One-click capital forecasts and board packs generated straight from live condition data.

Modules

• ARRB-Aligned Inspection Management

  Level 1 (routine), Level 2 (detailed visual) and Level 3 (structural investigation) with structured data capture and photo evidence.

• Component-Level Condition Register

  Deck, superstructure, substructure, bearings, joints, parapets, drainage, approach; tunnel lining, pavement, ventilation, lighting, fire suppression, ITS.

• 3D Digital Twin for Structures

  Navigate a digital representation and select individual elements to access inspection records and condition grades.

• Structural Health Monitoring Integration

  Connect sensor data to the digital twin with alerts when measured behaviour deviates from baseline.

• Tunnel Safety System Compliance

  Track ventilation, fire detection, emergency lighting and drainage against performance standards and proof-test intervals.

• Shutdown Maintenance Planning

  Coordinate structural, mechanical, electrical and pavement works in a single integrated schedule.

• Dynamic Forward Works Programme

  Model bridge rehabilitation and tunnel refurbishment based on actual condition, not static age-based schedules.

Problems solved

• “Bridge inspections live in consultant PDFs with no link to photos.”

  Every inspection photograph geotagged, dated and linked to the specific asset element.

• “Tunnel safety systems are maintained on separate schedules with no connection to structural condition.”

  Integrated tunnel safety system compliance with proof-test tracking and overdue alerts.

• “Pavement resurfacing is planned by age, not by actual ride quality.”

  Pavement condition linked to traffic loading, climate exposure and maintenance history.

• “After a weather event, damage assessment takes weeks.”

  Before/after comparison in hours using baseline condition data and post-event inspection.

• “Our forward works programme is a static spreadsheet rebuilt every 3 to 5 years.”

  Dynamic programme that updates when new Level 2 inspection data arrives.

• “We have no integrated view of which structures, tunnels and safety systems are compromised.”

  One governed operating picture with network condition summaries, risk maps and asset drilldown.

Modules

• Predictive Maintenance

  ML failure prediction and remaining useful life per asset with auto-generated work orders.

• AI Maintenance Scheduler

  Optimises timing across trades, access windows and amenity closures.

• Resident Services

  Defect intake with guided reporting and photo upload; amenity bookings with automatic closure blocking; move-in/move-out requests.

• Defects, Handover and Recovery

  Snagging across QA, handover, walkthrough and DLP; voice-to-text capture; digital sign-off; warranty claim management.

• Body Corporate and Committee Portal

  Standalone committee view of building summary, compliance, forward works and announcements.

• Finance and Capital

  20-year sinking fund modelling, capital plan with risk matrix, funding optimiser and scenario modeller.

• Compliance and Governance

  Compliance register with certificates and statutory inspection cycles; role-based permissions and audit logging.

• AssetMind AI Assistant

  Plain-English questions about the building, its snags, claims or finances.