Manufacturing · Data · AI Foundation

AI Data Foundation

Q: How long does a data architecture project take?

The schedule depends on catalog size, source access, attribute quality, relationships, ownership decisions, enrichment needs, integrations, and acceptance rules. We separate discovery, remediation, and connection work so estimates follow evidence from your records.

Q: Can you work with our existing ERP without replacing it?

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.

Q: What happens to data quality after the project ends?

Sustained quality requires documented ownership, update procedures, validation rules, exception handling, and monitoring for each data domain. A one-time cleanup without those operating controls allows the same patterns to return.

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.

Discuss the Business Objective Start Your Transformation Roadmap

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.

What Changes

What AI Data Foundation includes.

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.

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.

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.

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.

Data Governance Framework

Define ownership, update procedures, validation, exception handling, and quality standards for each data domain so cleanup becomes a maintained operating practice.

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.

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.

How It Fits Manufacturing

How this stage fits the manufacturing operation.

Data & AnalyticsWhich decision the view supports, which records establish it, and how disagreement or missing data becomes visible.

Governance dependencyMeasures need defined ownership, timing, lineage, reconciliation, and access before dashboards can become a basis for operating decisions.

Operating records to reconcile

demand and orders

products, services, and inventory

work and capacity

quality and customer service

delivery and financial results

What must be defined before engineering begins

What the manufacturing business needs to change and why.
Which systems, records, risks, and readiness gaps shape the work.
What should be built, how it fits the architecture, and in what order.

Related Services and Planning

What to evaluate next.

Follow the dependencies behind this service instead of treating it as an isolated project.

Data Services

Connect this reporting need to source records, governance, integration, analytics, and adoption.

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Integration Engineering

Acquire and reconcile the operating records the decision requires.

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AI on Your Data

Evaluate when governed data can support practical AI as well as reporting.

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Start Your Transformation Roadmap

New material work begins in Launchpad so Metrotechs can validate the operating need, evidence, feasibility, architecture direction, and sequence before engineering begins.

Explore next step

Start With the Manufacturing Continuity Objective

Define the smallest sound response and delivery sequence.

Metrotechs determines what the operation actually requires before selecting technology. New material work begins in Launchpad so the evidence, feasibility, architecture direction, priorities, and sequence can be validated before engineering begins.

Discuss the Business Objective Start Your Transformation Roadmap

Metrotechs designs, builds, integrates, and supports the approved solution so demand, fulfillment, and service commitments remain reliable.

A technical request does not bypass discovery. The proposed solution remains a working hypothesis until Metrotechs validates it in Launchpad and maps continuity before build.

Delivery sequence

How Metrotechs delivers AI Data Foundation.

Manufacturing product data crosses ERP, PLM, ecommerce, spreadsheets, supplier files, and legacy systems. We define identifiers, ownership, validation, relationships, and.

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.

FAQ

Questions to answer before changing this stage.

Straight answers to what manufacturing leaders ask before committing budget to this work.

Do we need a PIM system, or can ERP handle product data?

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.

How long does a data architecture project take?

Can you work with our existing ERP without replacing it?

What happens to data quality after the project ends?