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. 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.
Design AWS architecture around your specific workloads — ERP, WMS, file servers, databases, and custom applications. Right-sized from day one.
Dependency mapping, migration sequencing, cutover planning, and rollback procedures. Every application gets a migration strategy: rehost, refactor, or replace.
Secure data migration with integrity validation at every stage. Database replication, file system migration, and archive strategy for historical data.
Cloud security architecture with IAM, encryption, network segmentation, backup, and disaster recovery. Designed for SOC 2, ITAR, CMMC, and industry-specific compliance.
Right-sized instances, reserved capacity planning, auto-scaling policies, and ongoing cost monitoring. Cloud should cost less than on-premise, not more.
Post-migration monitoring, patching, backup validation, and incident response. Your team focuses on the business while we keep the infrastructure running.
Inventory all on-premise systems, map dependencies, and define migration priorities based on business impact and technical readiness.
Design target cloud architecture with networking, security, compute, storage, and DR. Present cost model and migration timeline.
Migrate a non-critical workload first to validate the architecture, networking, and migration tooling. Prove the pattern before scaling it.
Migrate production workloads in planned waves with cutover windows approved by operations. Parallel running where needed.
Post-migration performance validation, cost optimization, and security hardening. Handoff to managed operations or your internal team.
AWS Hosting & Infrastructure for Charlotte automotive operations - configured around local workflows, data ownership, and implementation governance.
AWS Hosting & Infrastructure for Charlotte energy infrastructure operations - configured around local workflows, data ownership, and implementation governance.
AWS Hosting & Infrastructure for Charlotte aerospace & defense operations - configured around local workflows, data ownership, and implementation governance.
AWS Hosting & Infrastructure for Charlotte food & beverage operations - configured around local workflows, data ownership, and implementation governance.
AWS Hosting & Infrastructure for Charlotte financial services operations - configured around local workflows, data ownership, and implementation governance.
AWS Hosting & Infrastructure for Charlotte healthcare & medical operations - configured around local workflows, data ownership, and implementation governance.
AWS is our primary platform — it has the broadest service breadth for manufacturing workloads. Most Odoo implementations run on AWS EC2 with RDS PostgreSQL. We also design hybrid architectures that keep sensitive workloads on-premise while moving others to cloud.
We use phased migration with database replication, DNS cutover, and rollback plans. Most migrations achieve zero downtime for end users. We schedule cutovers during planned maintenance windows and validate before switching traffic.
We design cloud architectures for SOC 2, ITAR, CMMC, HIPAA, and industry-specific compliance. This includes encryption, access controls, audit logging, data residency, and backup retention policies.
When properly architected, cloud infrastructure typically costs 20–40% less than equivalent on-premise when you factor in hardware refresh, power, cooling, and IT labor. We model total cost of ownership before migration starts.
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.
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.
Generic cloud architectures built from a vendor\u2019s reference design don\u2019t account for your ERP\u2019s latency requirements, your WMS\u2019s throughput demands, or your compliance obligations. We design cloud architecture around your actual workloads so everything performs on day one.
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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