We design and build the data architecture that connects your product catalog, attributes, pricing, and inventory into a single governed source — accurate, complete, and owned by you. TSMC's $40 billion fab complex in north Phoenix and Intel's ongoing Chandler expansion have turned the Valley of the Sun into America's semiconductor fabrication epicenter. But the boom extends far beyond chips — Honeywell Aerospace's Tempe turbine operations, Raytheon's missile assembly in Tucson-adjacent Mesa facilities, and a growing cluster of defense electronics firms along the Price Corridor all compete for the same constrained engineering talent and face ITAR compliance demands that most local ERP deployments weren't designed to handle.
Phoenix is adding manufacturing capacity faster than any metro in the country, but the supply chain to support those mega-fabs is still being built — creating a narrow window where mid-market suppliers can lock in OEM relationships if they can demonstrate digital readiness.
Map every data domain — items, BOMs, customers, pricing, inventory — and design the architecture that makes each system the authoritative source for what it owns. No duplication, no conflicts.
Audit, deduplicate, and enrich your product master data. Item attributes, classification hierarchies, unit of measure consistency, and pricing logic — cleaned to the standard your AI requires.
Connect ERP operational data to PIM product content so every downstream system — dealer portal, CPQ, AI agent — reads from one governed source. Changes propagate automatically.
Define ownership, update procedures, and quality standards for each data domain. Without governance, data quality degrades within 90 days of any cleanup effort.
Test data quality against the specific requirements of the AI systems being built — completeness, consistency, latency, and format. Confirm the foundation before the AI is deployed.
Establish the operational processes and tooling that keep data clean over time — import workflows, validation rules, exception handling, and quality monitoring dashboards.
Inventory every data domain and profile quality across completeness, consistency, duplicates, and accuracy. You know exactly what you are working with before any work starts.
Define the authoritative source for each data domain, the integration contracts between systems, and the governance model that keeps them aligned.
Execute the cleanup — deduplication, standardization, attribute enrichment, and conflict resolution — with business stakeholder sign-off at every stage.
Build the integrations that keep data synchronized across ERP, PIM, and operational systems. API or middleware, real-time or batch, governed by data contracts.
Test data quality against AI system requirements. Run trial deployments against the cleaned data to confirm outputs are accurate before production launch.
Document ownership, update procedures, and monitoring for each data domain. The infrastructure stays clean because the process stays governed.
AI Data Foundation for Phoenix semiconductors operations - configured around local workflows, data ownership, and implementation governance.
AI Data Foundation for Phoenix aerospace & defense operations - configured around local workflows, data ownership, and implementation governance.
AI Data Foundation for Phoenix electronics operations - configured around local workflows, data ownership, and implementation governance.
AI Data Foundation for Phoenix medical devices operations - configured around local workflows, data ownership, and implementation governance.
AI Data Foundation for Phoenix financial services operations - configured around local workflows, data ownership, and implementation governance.
AI Data Foundation for Phoenix healthcare operations operations - configured around local workflows, data ownership, and implementation governance.
ERP handles operational product data well — pricing, inventory, BOMs, order processing. It handles rich product content (attributes, images, classifications, descriptions) poorly. Whether you need a dedicated PIM depends on the volume and complexity of your product catalog and where that data needs to flow. We assess this as part of every data architecture engagement.
Data audits and architecture design typically take 2-4 weeks. Cleansing and enrichment depends on catalog size — a 5,000-SKU cleanup takes 4-6 weeks. Full ERP-to-PIM integration build takes 6-10 weeks. We scope to deliver clean, connected data before any AI development begins.
Yes. We design architecture around your existing systems — ERP, legacy databases, spreadsheets. The goal is not to replace what works but to govern what connects to it. We build integrations and governance layers on top of your current stack.
Nothing sustainable happens without a governance framework. We deliver documented ownership, update procedures, validation rules, and monitoring for every data domain. The framework is the work — cleanup without governance just creates the same problem again in 6 months.
Most manufacturers are still running workflows that require a person to touch every exception, every order, every routing decision. AI agents eliminate that bottleneck — not by replacing your people, but by handling the work that was always below their pay grade.
Most manufacturers forecast demand with spreadsheets, gut feel, and last year's numbers adjusted by 5%. ML models trained on your actual order history, seasonality patterns, and market signals replace guesswork with predictions your planning team can act on.
Odoo Maintenance captures work orders, failure reasons, repair times, and equipment history. We build AI models on top of that data to identify failure patterns and recommend maintenance windows before breakdowns occur — no new hardware, no IoT infrastructure required.
Odoo Quality captures inspection results, non-conformances, scrap reasons, and lot traceability across every production order. We build AI models on top of that data to surface defect patterns, predict quality risk, and trigger alerts before scrap accumulates — no cameras, no hardware.
Most manufacturers price by cost-plus formula or by whatever the sales rep negotiated last time. AI pricing models factor in material costs, competitive positioning, customer segment, order size, inventory position, and market conditions — governed by business rules so every price stays within approved boundaries.
When an order hits your system, someone decides which warehouse ships it — usually based on habit, proximity, or whoever answered the phone. AI order routing makes that decision in real time, optimizing across inventory availability, shipping cost, delivery speed, and warehouse workload.
Manufacturers still process thousands of POs, invoices, RFQs, spec sheets, and BOLs manually — reading PDFs, retyping data into the ERP, and fixing the errors that come with it. Document intelligence extracts structured data from unstructured documents automatically, with validation rules that catch errors before they enter your systems.
Your dealers call or email to check stock before placing orders because they can't see what's available. We give them live ATP visibility across all your warehouses — available, allocated, in-transit, and expected replenishment dates — straight from your ERP and WMS.
We govern cloud migration in phases — every dependency mapped, every workload sequenced, every cutover window defined. Zero-downtime migration for manufacturers who can't afford an outage.
Most manufacturing AI projects die in the pilot phase. We deploy AI that integrates into your actual workflows -- demand forecasting, predictive maintenance, pricing optimization, and intelligent routing -- governed by operational data contracts.
Your demand planning process runs on last year\u2019s sales adjusted by a gut-feel percentage. ML models trained on your actual order history, seasonal patterns, and market signals produce forecasts that are measurably more accurate \u2014 and they improve automatically as more data accumulates.
Your legacy system holds critical data that modern applications need -- but it has no APIs, no webhooks, and no modern integration points. We build a REST/GraphQL API layer on top of your legacy system so new applications can access data without touching the core.
Metrotechs starts with the operating questions: which records are trusted, which workflows are manual, which systems own each decision, and where AI can safely improve throughput.
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