What leaders see
Promising pilots that do not change daily work.
Teams test tools, get useful output, and still copy results into spreadsheets, tickets, emails, or ERP screens by hand.
Services
Data · AI Foundation
AI Data Foundation
Product data scattered across ERP, spreadsheets, and legacy systems is an operational liability. 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.
01
The Problem
AI Projects Fail Before the Model Ever Gets Built
The problem is rarely that the model cannot generate an answer. The real problem is that the data, permissions, exception rules, and action boundaries are not governed well enough for AI to affect production work.
What is actually happening
The automation cannot reach the operating record.
Source data, permissions, business rules, exception handling, and audit trails are not clean enough for the system to take action.
What gets worse
Automation scales uncertainty.
Bad inputs move faster, decisions become harder to trace, and teams lose confidence before AI becomes operationally useful.
02
What Changes
What AI Data Foundation includes.
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.
01
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.
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 so every downstream system — dealer portal, CPQ, AI agent — reads from one governed source. Changes propagate automatically.
04
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.
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.
03
How It Fits Your Operations
How AI Data Foundation fits your operation.
Intelligence layerWhich decisions can be automated, which need review, and which should stay human-owned.
Governance dependencyThe agent needs governed inputs, clear action boundaries, and audit logging before it can touch production workflows.
Data the model must trust
ERP history
exception queues
pricing rules
quality records
fulfillment events
What Launchpad defines 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 Foundations
What to evaluate next.
Follow the dependencies behind this service instead of treating it as an isolated project.
01
Data engineering and integration
Build the governed records, pipelines, and definitions AI needs to produce reliable results.
Explore next step02
Cloud architecture and operations
Prepare secure infrastructure, access controls, monitoring, and recovery for production AI workloads.
Explore next step03
AI on Your Data readiness
Evaluate the use case, owned data, permissions, review model, and workflow outcome before implementation.
Explore next step04
Launchpad readiness and Roadmap
Assess readiness, dependencies, risk, architecture, and implementation order before engineering begins.
Explore next stepLaunchpad Before Engineering
Decide what to build and in what order.
Launchpad assesses the business and turns discovery into priorities, risks, readiness, architecture, and an implementation Roadmap. Metrotechs then engineers and supports the approved solution.
Metrotechs designs, builds, integrates, and supports the approved solution.
Launchpad keeps priorities, risks, owners, decisions, and delivery governance connected.
04
Delivery sequence
How Metrotechs delivers AI Data Foundation.
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.
01
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.
02
Architecture Design
Define the authoritative source for each data domain, the integration contracts between systems, and the governance model that keeps them aligned.
03
Cleansing & Enrichment
Execute the cleanup — deduplication, standardization, attribute enrichment, and conflict resolution — with business stakeholder sign-off at every stage.
04
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.
05
Validation
Test data quality against AI system requirements. Run trial deployments against the cleaned data to confirm outputs are accurate before production launch.
06
Governance Handoff
Document ownership, update procedures, and monitoring for each data domain. The infrastructure stays clean because the process stays governed.
05
FAQ
Questions that usually decide the scope.
Straight answers to what operators ask before committing budget to this work.
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.