Manufacturing data \u2014 ERP databases, file shares, document archives, and transaction histories \u2014 is the most critical asset in a cloud migration. We migrate it with replication, validation gates, and integrity checks at every stage so nothing is lost, corrupted, or orphaned. Charlotte's manufacturing identity is splitting in two directions at once. Legacy energy suppliers built around Duke Energy's grid infrastructure are retooling for EV components, while Siemens Energy and ABB push turbine and switchgear production along the I-85 corridor toward full digital thread adoption. The result is a workforce fluent in heavy electrical assembly but largely unfamiliar with connected factory operations.
Charlotte's Tier 1 automotive suppliers are being forced into digital compliance by OEM mandates from BMW Spartanburg and the incoming VinFast plant, but most are still running disconnected Epicor and SAP instances with no real-time shop floor visibility.
Migrate SQL Server, Oracle, PostgreSQL, MySQL, and AS/400 databases to cloud-native or cloud-hosted instances. Replication-based migration minimizes cutover downtime to minutes.
Migrate shared drives, document libraries, and file archives with full permission and metadata preservation. Tiered storage policies applied during migration \u2014 active files on fast storage, archives on cold storage.
Record counts, checksums, and sample comparisons at every migration stage. Data does not go live until source and target match within defined accuracy thresholds.
Continuous database replication from on-premise to cloud during the migration period. Cutover happens when replication is in sync \u2014 downtime measured in minutes, not hours.
Define what data migrates to production cloud, what goes to cloud archive (cold storage), and what gets decommissioned. Reduce storage costs while maintaining compliance retention requirements.
Data encrypted in transit and at rest. Secure transfer protocols, access controls during migration, and audit logging for compliance requirements.
Inventory all databases, file shares, and data stores. Measure volumes, document access patterns, and identify compliance requirements for retention and residency.
Design the migration approach for each data source \u2014 replication method, transfer mechanism, target storage architecture, and validation procedures.
Execute test migrations and validate integrity. Measure transfer times, verify data accuracy, and confirm application functionality against migrated data.
Execute production migration with replication sync, cutover, and post-migration validation. Verify every data source before decommissioning on-premise copies.
Cloud Data Migration for Charlotte automotive operations - configured around local workflows, data ownership, and implementation governance.
Cloud Data Migration for Charlotte energy infrastructure operations - configured around local workflows, data ownership, and implementation governance.
Cloud Data Migration for Charlotte aerospace & defense operations - configured around local workflows, data ownership, and implementation governance.
Cloud Data Migration for Charlotte food & beverage operations - configured around local workflows, data ownership, and implementation governance.
Cloud Data Migration for Charlotte financial services operations - configured around local workflows, data ownership, and implementation governance.
Cloud Data Migration for Charlotte healthcare & medical operations - configured around local workflows, data ownership, and implementation governance.
It depends on volume and bandwidth. A 500GB ERP database migrates in hours with replication. Multi-terabyte file shares may take days of background sync. Cutover downtime is typically under 30 minutes for replication-based migrations.
Yes. We extract and migrate AS/400 data to cloud-hosted databases \u2014 SQL Server, PostgreSQL, or the database your target ERP requires. Data transformation and cleansing happen during the migration process.
Record counts, field-level checksums, referential integrity checks, and application-level validation. We run reconciliation reports and don\u2019t sign off until source and target match within defined thresholds.
We design archive policies that meet your retention requirements \u2014 7-year financial records, ITAR-controlled data, and customer contract data. Cloud archive storage (S3 Glacier, Azure Archive) reduces retention costs by 80%+ vs. on-premise.
Most manufacturers are still running workflows that require a person to touch every exception, every order, every routing decision. AI agents eliminate that bottleneck — not by replacing your people, but by handling the work that was always below their pay grade.
Most manufacturers forecast demand with spreadsheets, gut feel, and last year's numbers adjusted by 5%. ML models trained on your actual order history, seasonality patterns, and market signals replace guesswork with predictions your planning team can act on.
Odoo Maintenance captures work orders, failure reasons, repair times, and equipment history. We build AI models on top of that data to identify failure patterns and recommend maintenance windows before breakdowns occur — no new hardware, no IoT infrastructure required.
Odoo Quality captures inspection results, non-conformances, scrap reasons, and lot traceability across every production order. We build AI models on top of that data to surface defect patterns, predict quality risk, and trigger alerts before scrap accumulates — no cameras, no hardware.
Most manufacturers price by cost-plus formula or by whatever the sales rep negotiated last time. AI pricing models factor in material costs, competitive positioning, customer segment, order size, inventory position, and market conditions — governed by business rules so every price stays within approved boundaries.
When an order hits your system, someone decides which warehouse ships it — usually based on habit, proximity, or whoever answered the phone. AI order routing makes that decision in real time, optimizing across inventory availability, shipping cost, delivery speed, and warehouse workload.
Manufacturers still process thousands of POs, invoices, RFQs, spec sheets, and BOLs manually — reading PDFs, retyping data into the ERP, and fixing the errors that come with it. Document intelligence extracts structured data from unstructured documents automatically, with validation rules that catch errors before they enter your systems.
Your dealers call or email to check stock before placing orders because they can't see what's available. We give them live ATP visibility across all your warehouses — available, allocated, in-transit, and expected replenishment dates — straight from your ERP and WMS.
We govern cloud migration in phases — every dependency mapped, every workload sequenced, every cutover window defined. Zero-downtime migration for manufacturers who can't afford an outage.
Most manufacturing AI projects die in the pilot phase. We deploy AI that integrates into your actual workflows -- demand forecasting, predictive maintenance, pricing optimization, and intelligent routing -- governed by operational data contracts.
Your demand planning process runs on last year\u2019s sales adjusted by a gut-feel percentage. ML models trained on your actual order history, seasonal patterns, and market signals produce forecasts that are measurably more accurate \u2014 and they improve automatically as more data accumulates.
Your legacy system holds critical data that modern applications need -- but it has no APIs, no webhooks, and no modern integration points. We build a REST/GraphQL API layer on top of your legacy system so new applications can access data without touching the core.
Metrotechs starts with the operating questions: which records are trusted, which workflows are manual, which systems own each decision, and where AI can safely improve throughput.
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