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. Atlanta's manufacturing economy is inseparable from its logistics infrastructure. Kia Georgia's West Point assembly plant, Lockheed Martin's Marietta F-35 line, and Coca-Cola's bottling network all depend on the same congested I-75/I-85 freight corridor and Hartsfield-Jackson air cargo hub. The challenge isn't just making things — it's synchronizing production with one of the most complex distribution networks in the country.
Atlanta's manufacturers don't just compete on production efficiency — they compete on logistics integration, and most mid-market firms along Fulton Industrial Boulevard are still running production scheduling and shipping as two separate systems.
Clean, documented REST APIs that expose legacy data and operations. Standard HTTP methods, JSON responses, and OpenAPI/Swagger documentation.
Abstract legacy data models into modern, clean interfaces. Consumers see logical business objects, not cryptic legacy table structures.
Real-time APIs for transactional operations plus batch endpoints for bulk data extraction. Match the integration pattern to the use case.
OAuth 2.0, API keys, and role-based access control. Secure access to legacy data with modern security standards.
Protect legacy systems from being overwhelmed by API traffic. Intelligent caching reduces load on the legacy system while keeping data fresh.
API usage dashboards, error tracking, and performance monitoring. Know who\'s calling what, how often, and whether it\'s working.
Define which data and operations need to be exposed. Prioritize by business value -- what integrations are blocked today?
Analyze how to extract data from the legacy system -- direct database, stored procedures, file interfaces, or screen automation.
Design API contracts, build the middleware layer, and implement data mapping between legacy formats and modern JSON/REST.
Load testing to ensure legacy system stability, security testing, and integration testing with consuming applications.
Publish API documentation, developer guides, and sample code. Onboard internal teams and third-party integrators.
API Layer Development for Atlanta automotive operations - configured around local workflows, data ownership, and implementation governance.
API Layer Development for Atlanta food & beverage operations - configured around local workflows, data ownership, and implementation governance.
API Layer Development for Atlanta logistics & distribution operations - configured around local workflows, data ownership, and implementation governance.
API Layer Development for Atlanta aerospace & defense operations - configured around local workflows, data ownership, and implementation governance.
API Layer Development for Atlanta financial services operations - configured around local workflows, data ownership, and implementation governance.
API Layer Development for Atlanta technology & software operations - configured around local workflows, data ownership, and implementation governance.
We design the API layer to minimize impact -- read replicas for queries, connection pooling, caching, and rate limiting. Most legacy systems handle API traffic with no noticeable performance impact.
Yes. We build APIs on top of AS/400 using ODBC/JDBC connections to DB2/400, data queues, program calls, and IFS file interfaces. Your AS/400 data becomes accessible via standard REST APIs.
It can be either. Some organizations use the API layer as a permanent integration strategy. Others use it as a bridge during phased migration -- decoupling consumers from the legacy system so it can be replaced without disrupting integrations.
We support both read and write APIs. Write operations use the legacy system\'s own transaction mechanisms (stored procedures, program calls) to ensure data integrity and business rule enforcement.
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.
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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