Every order that arrives by email, fax, phone, or portal and gets manually typed into the ERP is a cost leak and an error source. We automate order capture from every channel with validation rules that catch problems before they reach the warehouse. Austin is where engineering culture meets business scale. Tesla Gigafactory, Samsung's $17B chip fab, a booming tech sector, and one of the fastest-growing business markets in the country. Austin businesses demand AI systems built to engineering standards — not chatbots, not SaaS features. Custom AI agents that operate inside complex business processes, trained on your data, and owned by you permanently.
Austin businesses are tech-native and engineering-literate. They recognize the difference between an AI chatbot bolted onto a SaaS platform and a custom AI system built for their specific operation.
Capture orders from EDI, email, portal, phone (with structured intake), and fax \u2014 all routed into the same automated pipeline. No channel gets manual treatment when it doesn\u2019t have to.
Extract line items, quantities, pricing, ship-to, and PO references from emailed PDFs, typed emails, and scanned documents. Map extracted data to your ERP\u2019s order fields automatically.
Validate every order against your item master, pricing tables, customer credit status, inventory availability, and business rules before it enters the ERP. Errors are flagged and routed \u2014 not entered and discovered later.
Detect duplicate POs, conflicting quantities, and orders that reference discontinued or substituted items. Route conflicts to the right person with context instead of silently entering bad data.
Validated orders enter Odoo with all fields populated -- item, quantity, pricing, ship-to, and PO reference. No human touches the order between capture and ERP entry unless a validation rule flags it.
Automated order acknowledgments, status updates, and shipment notifications sent to customers through their preferred channel. No manual "let me check on that" calls.
Map every order channel, document the current processing workflow, measure cycle times and error rates, and quantify the cost per manually processed order.
Define the business rules that orders must pass before ERP entry \u2014 item validation, pricing checks, credit holds, inventory availability, and ship-to verification.
Build the capture, extraction, validation, and ERP entry pipeline. Integrate with your ERP\u2019s order management module and customer communication channels.
Run automated and manual processing in parallel. Compare accuracy, cycle time, and exception rates. Tune extraction and validation rules until automation meets or exceeds manual accuracy.
Deploy with dashboards tracking automation rate, exception rate, cycle time, and error rate. Continuous tuning as new order formats and edge cases are encountered.
Order Processing Automation for Austin technology & saas operations - configured around local workflows, data ownership, and implementation governance.
Order Processing Automation for Austin clean energy operations - configured around local workflows, data ownership, and implementation governance.
Order Processing Automation for Austin healthcare & life sciences operations - configured around local workflows, data ownership, and implementation governance.
Order Processing Automation for Austin semiconductor & electronics operations - configured around local workflows, data ownership, and implementation governance.
Order Processing Automation for Austin construction & development operations - configured around local workflows, data ownership, and implementation governance.
Order Processing Automation for Austin distribution & logistics operations - configured around local workflows, data ownership, and implementation governance.
Typically 60\u201380% of orders can be fully automated (no human touch from capture to ERP entry). The remaining 20\u201340% require human review for exceptions \u2014 but even those are pre-populated and partially validated, cutting processing time by 50%+.
Yes. We train extraction models on your specific customer PO formats. Each customer\u2019s document layout is mapped once, then processed automatically going forward. New formats are added as they appear.
It\u2019s routed to the appropriate person with the specific failure reason, the original document, and a pre-populated order ready for correction. No order is silently dropped or entered with known errors.
At $15\u2013$25 per manually processed order (labor + error correction), a manufacturer processing 200 orders/day saves $750K\u2013$1.25M annually at 70% automation. Most deployments pay for themselves within 4\u20136 months.
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.
Talk To Metrotechs