Asset-to-part linkage, critical spare coverage, obsolete asset spares, and plant risk heatmaps.
AssetMind AI explains how exported asset registers, hierarchy, BOM, criticality, material links, work orders, and spare-part context become readiness evidence before EAM cleanup, APM scope, or reliability analytics expansion.
Turns asset register, BOM, hierarchy, and part-linkage data into asset-to-part readiness evidence. It stays inside the Industrial IQ evidence model: exported source data, source-backed findings, confidence tiers, owner review, reports, action tracking, and no ERP write-back.
Asset-to-Part Diagnostic
Start with the minimum viable export for AssetMind AI.
Asset register, Material master
Asset Id, Description
Map aliases, required coverage, source-fit, and limitations before analysis.
Findings show asset ID, hierarchy, part reference, BOM coverage, criticality, review level, and EAM owner action.
AssetMind AI Asset-to-Part Risk Report
Asset intelligence score: lower values mean weaker asset-to-part linkage, critical-spare coverage, plant relevance, and obsolete-asset-spare control.
Asset Integrity, Maintenance, Reliability, and Operations leaders
Read-only diagnostic, no ERP write-back, source-file purge, human review.
HTML/PDF report, CSV sample, mapping template, and data dictionary for Asset-to-Part Intelligence.
Run Asset-to-Part Intelligence, inspect the sample report, or request a founder-led pilot.
These cards show the decision frames, inputs, outputs, and evidence boundary a buyer committee should expect. Planning and sample frames are labeled; uploaded data replaces assumptions with customer-specific evidence.
Data centers · Asset register, criticality, material master, and equipment-class exports
"AssetMind shows where critical assets have clear spare coverage, weak linkage, or no defensible part relationship."
COO, facilities, and asset integrity
Review this evidence frame, then run the engine with uploaded data when ready.Utilities and power generation · Asset register, BOM-like references, material master, and usage history
"Maintenance teams can prioritize which asset classes need BOM cleanup before outage or reliability work."
Maintenance, reliability, and asset governance
Review this evidence frame, then run the engine with uploaded data when ready.Claims discipline: public examples are planning or sample frames unless marked as approved customer-specific evidence. Financial or remediation outcomes require uploaded-data diagnostics and human review.
Findings are recommendations and review items, not autonomous ERP changes.
Trust controlEvery finding links back to source records, mapped fields, and reason codes.
Trust controlHigh, medium, low, and needs-review logic is visible before action.
Trust controlOwners can accept, reject, defer, assign, or request more data.
Trust controlReport exports, reviewer actions, and status changes remain traceable.
It diagnoses weak asset-to-part relationships, BOM gaps, orphan spares, obsolete-asset stock, and critical spare coverage before EAM cleanup or reliability action.
Start with asset register, equipment hierarchy, asset status, criticality, BOM rows, material master, site, stock, value, and work-order references where available.
No. It inspects exports from those systems and produces evidence before cleanup, maintenance, or performance programs. It does not manage assets or predict health.
No. It flags linkage and coverage evidence for qualified owner review. Engineering, maintenance, reliability, and governance owners decide final action.
An asset intelligence score, asset-to-part coverage map, BOM readiness report, critical spare gap register, site risk view, and EAM action queue.
Maintenance, reliability, asset integrity, EAM/CMMS owners, engineering, inventory, and ERP/data governance should review the findings.
An asset dashboard shows known records. AssetMind AI diagnoses whether asset, BOM, and material relationships are complete and strong enough to support action.
Run an asset-to-part Snapshot using asset, BOM, material, criticality, and site exports before changing EAM records or rationalizing spares.
AssetMind AI helps a buying committee answer one practical question: what can exported data prove, which findings are confidence-tiered, who must review them, and what action is safe after the report. Use the demo path for a product-led walkthrough, then inspect the sample report or run a bounded snapshot when the data path is ready.
Industrial IQ is designed for evidence-first buyers. Review sample proof, run a bounded Snapshot, align the buyer committee, or request a founder-led diagnostic pilot when the operating problem is ready for review.
See what the report looks like before sharing internal data.
Have a data export Get AssetMind AI DemoStart with an export-first diagnostic path and no ERP write-back.
Need committee alignment Download Buyer Evaluation GuideGive finance, operations, procurement, ERP, security, and maintenance the same evaluation frame.
Ready for review Request Founder-Led PilotAsk for a founder-led pilot review when the problem has an owner and source data is available.
Engine evaluation separates the operating problem, required source files, diagnostic logic, evidence outputs, and review boundary so buyers understand what the engine does and what remains human-owned.
Asset Integrity, Maintenance, Reliability, and Operations leaders
Source exports mapped to the engine, required fields, optional context, assumptions, exclusions, and confidence-tier evidence.
Engine-specific findings, score context, report sections, open findings, action-tracker items, and next review steps.
Read-only diagnostics, no ERP write-back, source-file purge after report generation, and human review before action.
Grounded in approved AI2COE content only. No unsupported claims.