Business outcome
Use this capability when the framework shows that people cannot trust the records, measures, or exceptions needed to make decisions across demand, supply, production, inventory, fulfillment, delivery, or service.
Services
Data & Analytics · Data · AI Foundation
AI Data Foundation
Give product, material, and inventory data governed ownership before applying AI.
Manufacturing product data crosses ERP, PLM, ecommerce, spreadsheets, supplier files, and legacy systems. We define identifiers, ownership, validation, relationships, and distribution so analytics, automation, portals, and AI use dependable records.
Launchpad assesses the operating need and creates the Roadmap. We engineer this capability when the approved plan calls for it.
A strong fit when
- Product, customer, and vendor data lives in different systems with different definitions of the same thing
- Master data has duplicates, missing fields, and inconsistent units that break automation before it starts
- Nobody owns data quality, so problems get rediscovered every time a new project starts
- AI and reporting projects stall waiting on data that was never built to be trusted
Why this service exists
Connect the technology decision to the work the manufacturing business must control.
System responsibility
Data engineering turns records from ERP and connected systems into governed information for shared visibility, reporting, reconciliation, automation, and accountable action.
Ownership and control
The manufacturer must own the definitions, source authority, access, lineage, quality rules, reconciliations, and decision context behind every important measure.
01
The Problem
AI and automation cannot compensate for fragmented manufacturing product data.
Analytics problems begin when a dashboard is built before records, definitions, timing, lineage, and accountability have been reconciled across the operation.
What leaders see
Reports disagree about current conditions.
Teams debate demand, workload, capacity, inventory, service, delivery, and margin instead of acting on them.
What is actually happening
Measures inherit unresolved record conflicts.
Sources, identifiers, definitions, refresh timing, adjustments, and ownership differ across systems and teams.
What gets worse
Faster reporting accelerates the wrong answer.
More dashboards spread inconsistent measures and make exception ownership harder to establish.
02
What changes
Make the operating responsibility visible and governable.
Manufacturing product data crosses ERP, PLM, ecommerce, spreadsheets, supplier files, and legacy systems. We define identifiers, ownership, validation, relationships, and distribution so.
01
Operating outcome
Give product, material, and inventory data governed ownership before applying AI.
02
Operating records to reconcile
demand and orders, products, services, and inventory, work and capacity
03
Decision and exception path
Which decision the view supports, which records establish it, and how disagreement or missing data becomes visible.
04
Ownership and continuity
Measures need defined ownership, timing, lineage, reconciliation, and access before dashboards can become a basis for operating decisions.
03
Architecture
Build the service around the business record and decision.
Which decision the view supports, which records establish it, and how disagreement or missing data becomes visible.
01Source record
02Governed connection
03Validation
04Business system
05Accountable owner
Operating records to reconcile
demand and ordersproducts, services, and inventorywork and capacityquality and customer servicedelivery and financial results
04
Engineering scope
What Metrotechs engineers for AI Data Foundation.
The exact scope follows the approved business objective, source records, dependencies, controls, and delivery sequence.
01
Data Architecture Design
Map the product and operating data domains in scope — items, materials, BOMs, customers, pricing, and inventory — and define which system is authoritative for each record and attribute.
02
Master Data Cleansing
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.
03
ERP + PIM Integration
Connect ERP operational data to PIM product content through governed contracts so downstream portals, CPQ, analytics, and AI receive approved records with traceable update timing.
04
Data Governance Framework
Define ownership, update procedures, validation, exception handling, and quality standards for each data domain so cleanup becomes a maintained operating practice.
05
AI Readiness Validation
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.
06
Ongoing Data Operations
Establish the operational processes and tooling that keep data clean over time — import workflows, validation rules, exception handling, and quality monitoring dashboards.
05
Delivery sequence
From operating reality to a solution the business can own.
Data Audit
Inventory the product and operating data domains in scope and profile completeness, consistency, duplication, relationships, and accuracy before architecture or cleanup decisions are made.
Architecture Design
Define the authoritative source for each data domain, the integration contracts between systems, and the governance model that keeps them aligned.
Cleansing & Enrichment
Execute deduplication, standardization, attribute enrichment, and conflict resolution with accountable data-owner review at the agreed acceptance gates.
Integration Build
Build the integrations that keep data synchronized across ERP, PIM, and operational systems. API or middleware, real-time or batch, governed by data contracts.
Validation
Test data quality against AI system requirements. Run trial deployments against the cleaned data to confirm outputs are accurate before production launch.
Governance Handoff
Document ownership, update procedures, and monitoring for each data domain. The infrastructure stays clean because the process stays governed.
Related services and systems
Continue through the connected operating environment.
Use these connected services and references to understand the records, workflows, and systems surrounding this work.
Systems explained
ERPUnderstand the operating records, workflows, and system-of-record boundaries an ERP should own.PIMUnderstand how product information should be governed and distributed across commerce and operating systems.PLMUnderstand how product definition, revisions, engineering changes, and release records should reach downstream systems.
06
FAQ
Questions to answer before implementation begins.
Clear answers for manufacturing leaders evaluating the work, operating responsibility, and delivery path.
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.