Data · AI Foundation

AI Data Foundation

Q: How long does a data architecture project take?

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.

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?

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.

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.

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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 inventory, production, margin, delivery, and quality numbers 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 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.

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 so every downstream system — dealer portal, CPQ, AI agent — reads from one governed source. Changes propagate automatically.

Data Governance Framework

Define ownership, update procedures, and quality standards for each data domain. Without governance, data quality degrades within 90 days of any cleanup effort.

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 Your Operations

How AI Data Foundation fits your 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

materials and inventory

operations and capacity

quality and service

delivery and financial results

What must be defined before engineering begins

What the 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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Launchpad assessment and Roadmap

Use Launchpad when the operating problem needs a structured assessment, readiness evidence, architecture, and implementation Roadmap.

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Start With the Operating Problem

Define the smallest sound response and delivery sequence.

Metrotechs determines what the operation actually requires before selecting technology. When a structured assessment is warranted, Launchpad turns evidence into priorities, risks, architecture, and an implementation Roadmap.

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Metrotechs designs, builds, integrates, and supports the approved solution.

Launchpad is available when the engagement needs assessment evidence, a Roadmap, and ongoing delivery governance.

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 every data domain and profile quality across completeness, consistency, duplicates, and accuracy. You know exactly what you are working with before any work starts.

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 the cleanup — deduplication, standardization, attribute enrichment, and conflict resolution — with business stakeholder sign-off at every stage.

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 that usually decide the scope.

Straight answers to what operators 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?