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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. PartsCleanse AI is the first diagnostic product. Every decision we make is governed by one question: does this produce a finding that an operations or finance leader can act on?

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
PartsCleanse AIThe first diagnostic product -- MRO catalog deduplication with industrial false-positive controls and five executive report artifacts
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 narrow at launch. PartsCleanse AI proves the 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 is the first product because MRO catalog quality has a concrete scope, an accessible input (a single CSV export), and a reviewable output that finance, operations, and procurement can evaluate together -- before any ERP change is authorized.

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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.
Validated benchmarks

The industrial data problem is documented, not assumed.

The case for MRO catalog intelligence is grounded in published industrial benchmarks, not vendor assertions. Industrial IQ products are designed around these documented operating realities.

5–10% duplicate rate Industry benchmark for duplicate and near-duplicate item master records in unmanaged MRO catalogs — APICS Supply Chain Management Body of Knowledge; industrial ERP migration audits
$50K–$500K+ per 1,000 duplicates Estimated capital exposure per thousand duplicate records, accounting for excess inventory, redundant procurement, and planner labor — Industrial IQ capital exposure model, calibrated to sector benchmarks
ERP migration risk MRO catalog defects propagate through SAP S/4HANA and Oracle Cloud migrations, compounding the cost of post-cutover remediation — SAP migration best-practice literature; field observations from asset-intensive sector deployments
72% of storerooms Proportion of industrial storerooms that report significant duplicate or orphaned item records, based on aggregated MRO audit literature — Industrial MRO catalog benchmarking studies, 2019–2024
Read the MethodologyRun the Capital Exposure Model
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