Works from CSV or workbook exports produced from SAP, IBM Maximo, Oracle ERP, Hexagon EAM, Infor, and other ERP, EAM, or CMMS systems. No direct integration or write-back is required — Review data requirements →
Safe Claims Registry

Every public claim should say what kind of evidence supports it.

This registry protects buyer trust by making AI2COE language auditable. It tells executives, procurement, CIOs, and AI search systems how to interpret numbers, controls, product outputs, and roadmap statements.

Open Proof PackRead Methodology
5 classesClaim evidence types
Audit-readySource page per claim
No ambiguityBoundary stated
Evidence summary

Buyer evidence resource

AI2COE Safe Claims Registry: Use this page to understand the operating question, exported-data evidence path, review boundary, and next Industrial IQ action. Review how AI2COE classifies public claims as benchmark assumptions, calculator estimates, diagnostic evidence, platform controls, or roadmap statements.

Run Free Industrial IQ Snapshot
Review ownerExecutive sponsors, operating leaders, finance reviewers, procurement teams, maintenance owners, ERP/data leaders, and governance reviewers who need evidence before approving action.
Input file contextOperational CSV exports, item master fields, inventory, procurement, asset, work-order, finance, readiness, or governance data depending on the page.
Diagnostic evidenceSource-backed evidence, scores, confidence tiers, report outputs, action tracking, score history, and governance context.
Recommended actionRun Free Industrial IQ Snapshot and select the diagnostic engine that matches the operating question.
Benchmark Useful for early discussion, but must say it is an assumption.
Calculator Useful for lead capture and CFO sizing, but not an audit finding.
Diagnostic Stronger evidence because it uses uploaded catalog data and report logic.
Control A product behavior or governance posture, such as no write-back or source purge.
Roadmap Future or controlled-beta capability; must not be sold as current production scope.
Claims registry

Current public claims and their interpretation boundary.

ClaimEvidence classSource surfaceBoundary language
Duplicate-rate planning assumption Estimated input MRO planning-assumption methodology Use for initial sizing only; actual reports replace this with uploaded-data findings.
Material exposure band on 50K-SKU scenarios Benchmark assumption Scorecard and methodology Planning range based on stated assumptions; not a customer guarantee.
50K SKU sample-mode runtime Engine validation evidence Engine validation boundary Public performance language should stay tied to controlled sample conditions and not imply customer-specific runtime.
No ERP write-back Platform control Data handling and trust center Product posture: diagnostic evidence only; client owners control ERP remediation.
Source catalog purge Data lifecycle control Data handling commitment Uploaded source files are processed and purged after report generation.
Browser, Excel, Word, PDF, CSV outputs Report capability Report gallery Available output types depend on the active run/session policy.
Free diagnostic runs Commercial policy Pricing Subject to current quota and account policy shown in the portal.
ReliabilityMind AI and ProcureMind AI Diagnostic engines Products Position as evidence-first diagnostics with role-specific readiness and procurement leakage outputs.
Why this matters

Enterprise authority comes from being specific about proof.

A buyer can accept a benchmark if it is labelled as a benchmark. They lose trust when a benchmark is made to sound like a customer-specific outcome. AI2COE should keep this registry current as the product earns more live evidence.

Operating rule: use this registry before publishing new landing pages, competitor pages, emails, reports, or sales collateral.