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 →
About Industrial IQ

We build AI products that prove their value before asking for a commitment.

Industrial IQ is a product company for asset-intensive industries. AI2COE is the portal. The platform includes eight governed diagnostic engines across catalog, inventory, procurement, finance, asset, reliability, readiness, and governance decisions. Every decision we make is governed by one question: does this produce a finding that an operations or finance leader can act on?

Product-firstDiagnostics, not consulting retainers
Evidence-ledQuantified findings before any transformation commitment
Industrial-gradeBuilt for real operational data complexity
Evidence summary

Buyer evidence resource

About AI2COE Industrial IQ: Use this page to understand the operating question, exported-data evidence path, review boundary, and next Industrial IQ action. AI2COE Industrial IQ is a governed Industrial Decision Intelligence platform with eight diagnostic engines for asset-intensive operations, anchored by.

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.
Our operating belief

AI earns trust when it produces a governed, reviewable finding.

Most AI programs in industrial operations begin with vendors, platforms, and roadmap workshops -- before a single measurable finding has been produced. Industrial IQ is built around the opposite sequence: diagnose first, quantify the evidence, govern the review, then decide whether to scale.

PartsCleanse AI applies that discipline to MRO spare-parts catalog quality -- the most concrete, accessible, and financially material AI adoption problem in asset-intensive operations. The product does not ask a client to believe in AI. It asks them to upload a catalog export and inspect the result.

Industrial IQ is a founder-led product company. It is not a consulting practice with an AI wrapper. Every product decision is tested against a single governance standard: can an operations leader, a finance director, or a procurement head review this finding and authorize an action based on it?
What we have built
AI2COEThe governed AI adoption portal -- diagnostic products, adoption framework, and intelligence library
Industrial IQ enginesEight diagnostic engines for catalog, inventory, procurement, finance, asset, reliability, readiness, and governance evidence
AI Adoption FrameworkThe six-stage governance sequence -- Diagnose, Quantify, Prioritize, Govern, Pilot, Scale
6 industry pathwaysOil & Gas, Manufacturing, Mining, Food & Beverage, Pharmaceutical, Utilities
What makes the company different

Industrial IQ is building from the data problem outward.

Many AI programs start with a model and search for a use case. Industrial IQ starts with a measurable operating failure: duplicated, fragmented, and ungoverned master data that undermines procurement, maintenance, and finance decisions.

The company's product strategy is intentionally governed. Each Industrial IQ engine must prove the same discipline: diagnostic first, evidence visible, value quantified, governance explicit, action controlled.

Operating principles
SpecificityEvery product must attach to a real industrial workflow and data source
EvidenceEvery claim must be inspectable in a report, workbook, or source-record trail
GovernanceEvery recommendation must make ownership, approval, and risk visible
Why MRO catalog quality is the right first problem

The catalog is where finance, maintenance, procurement, and reliability intersect.

Spare-parts item-master entropy is structural. It accumulates through acquisitions, ERP migrations, plant-level purchasing autonomy, and years of inconsistent description entry. By the time an organization deploys predictive maintenance or autonomous procurement AI, the catalog disorder has already created excess inventory, duplicate procurement spend, and planner search failures.

PartsCleanse AI remains the catalog intelligence engine because MRO catalog quality has a concrete scope, an accessible input, and a reviewable output that finance, operations, and procurement can evaluate together. InventoryMind AI, ProcureMind AI, FinanceMind AI, AssetMind AI, ReliabilityMind AI, ReadyMind AI, and GovernanceMind AI extend the same discipline into the operating questions that follow.

Run Free Industrial IQ SnapshotExplore 8 Engines
Built by

Founder-led. Operator-focused. Domain-specific by design.

Industrial IQ is led by a founder with over two decades of operating experience across enterprise software, industrial AI, and go-to-market architecture for asset-intensive sectors. The company's product thesis was not constructed in a consulting engagement. It was derived from direct observation of how catalog data disorder compounds across ERP migrations, maintenance systems, and procurement workflows in industrial operations.

Domain focusMRO spare-parts catalog quality — the intersection of finance, maintenance, procurement, and reliability in asset-intensive operations
Product disciplineDiagnostic-first, evidence-visible, governance-explicit — no recommendation without a reviewable finding
Industrial coverage18 asset-intensive sectors spanning Oil & Gas, Mining, Manufacturing, Utilities, Aviation MRO, Pharmaceutical, Data Centers, and 11 additional verticals
Company structureFounder-led. Not a consulting practice with an AI wrapper. Not a platform vendor. A product company with a single governing discipline.
Evidence discipline

The industrial data problem must be classified before it is claimed.

The case for MRO catalog intelligence should be grounded in observed, derived, estimated, or hypothesis-classified evidence. Industrial IQ products are designed around this evidence discipline rather than unsupported proof claims.

Duplicate-rate assumptions Estimated until replaced by uploaded item-master evidence and owner-reviewed duplicate-family findings.
Capital-exposure assumptions Estimated from buyer-entered or uploaded cost context; not treated as realized value or savings before review.
ERP migration risk MRO catalog defects can propagate through SAP S/4HANA and Oracle Cloud migrations when data readiness is not reviewed before cutover.
Storeroom data-quality signals Duplicate, orphaned, obsolete, and incomplete item records should be classified as candidates until the source evidence is reviewed.
Read the MethodologyRun the Capital Exposure Model