01
01. Configuration & Master Governance
Establishes one corporate risk matrix, standardized rate bases (labor, spares, tools), and catalog rules so sites share identical criteria.
Featured project
FEATURED PROJECT | INDUSTRIAL & MINING TECH
Nexaan APM: Asset Performance Management for Mining
An enterprise Asset Performance Management platform that standardizes maintenance strategy engineering, generates CMMS load sheets, and continuously improves strategy using field failure history.
Nexaan APM
250%
ROI Across Brownfield Ops
500%
ROI on Operational Readiness
70%
Reduction in Strategy Time
A$1.12M
Annual Cost/Risk Opportunity
Challenge
The Operating Problem in Heavy Industry Maintenance
Mining operations face severe maintenance inefficiencies, including duplicate work tasks, unoptimized shutdown scopes, and high operational risks. Traditional asset management fails at the handoffs: engineering logic gets lost in manual data entry, free-text work order history goes unanalyzed, and local-only maintenance fixes are rarely scaled across identical fleet assets.
- Disconnected Lifecycles: Disjointed workflows between greenfield strategy setup, CMMS execution, and post-failure engineering reviews.
- Unstructured History & Noise: Years of free-text work order records in SAP/Maximo cannot be analyzed systematically for true failure causes.
- Duplicate & Inconsistent Engineering: Identical equipment across multiple sites maintained differently with no standardized risk or cost basis.
Solution
What We Built: Governed APM Platform with Unified Data Model
An 11-module governed APM platform built on a single unified data model. Nexaan standardizes corporate risk matrices and component-level asset libraries, automatically generates execution-ready task bundles, and uses ML models to transform unstructured work order history into actionable strategy updates.
Capabilities
From master governance and AI-drafted RCM to work-order analysis and CMMS load.
02
02. Asset Library & AI-Drafted RCM
Features a 3-tier classification hierarchy down to maintainable items and parts, utilizing engineer-trained AI models to draft RCM specs.
03
03. Work Order Analyser
Processes years of unstructured free-text work order history to map component, failure mechanism, and root cause with confidence scoring.
04
04. PM Optimisation & CMMS Load
Optimizes task intervals on failure evidence, converting approved changes directly into SAP PM or IBM Maximo load sheets.
User journey
From Structural Setup to Continuous Self-Improving Strategy
01
Build & Classify Asset Hierarchy
Ingest drawings and registers into the Functional Location Builder to establish a standardized asset hierarchy with visible change impact.
02
Inherit & Tune Strategy
Link functional locations to master asset classes; inherit pre-built RCM analysis and tune for local operating context.
03
Bundle & Export to CMMS
Automatically group tasks into execution-ready packages, generate maintenance documents, and export structured load sheets to SAP PM/Maximo.
04
Analyze Execution & Self-Improve
Field execution data is ingested by the Work Order Analyser, surfacing failure trends and pushing approved optimizations back into core strategy.
Architecture & Technology Stack
Engineered for Heavy Industrial & Regulated Enterprise Environments
- 01AWS Cloud Infrastructure featuring dedicated single-tenant client environments with end-to-end encryption.
- 02Single Unified Data Model connecting 11 strategy, governance, and history-optimization modules.
- 03Engineer-Trained ML Engines for free-text NLP classification and statistical reliability modeling (RAM & FTA).
- 04Native Integration Loaders structured for SAP PM, IBM Maximo, and enterprise CMMS pathways.
- 05Audit-First Governance Logging tracking every strategy edit, cost model change, and named engineer sign-off.
Outcomes & Business Impact
Outcomes & Business Impact
Reclaimed Engineering Hours
Eliminates manual data entry and duplicate analysis, shifting engineering time toward high-value risk decisions.
Defensible Board Reporting
Downtime, availability, and production modeling (RBD/RAM) are backed by auditable engineering evidence.
Enterprise Knowledge Retention
Failure logic and strategy corrections accumulate in the client's tenant rather than walking out the door with individual staff.
Python / Machine Learning EngineAWS Private CloudSAP PM & IBM Maximo IntegrationRAM & Fault Tree Analysis ModulesRCM Automation
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