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.
Why AI Projects Fail Before They Start
- →Product master data split across ERP, spreadsheets, and legacy systems with no single source of truth
- →Item attributes incomplete or inconsistent — AI surfaces wrong specs, wrong pricing, wrong availability
- →No data governance — the same cleanup work gets done repeatedly with no lasting fix
- →AI built on bad data delivers confident wrong answers, which is worse than no AI at all
AI Data Foundation
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.
The Engagement Process
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.
Frequently Asked Questions
AI Prerequisite · Data Architecture
Every engagement starts with an assessment.
We scope work after we understand your operation — not before. The Launchpad assessment maps where you are, quantifies what it's costing you, and sequences what to do first.
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