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. 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.
Automatically extract line items, quantities, pricing, ship-to addresses, and terms from incoming POs in any format — PDF, email, fax image, EDI. Validate against your item master and pricing rules before entry.
Extract vendor, line items, amounts, PO references, and payment terms from supplier invoices. Three-way match against PO and receipt automatically. Route exceptions for review instead of manually matching every invoice.
Extract technical requirements, material specifications, quantities, and tolerances from RFQs and engineering drawings. Structure the data so your estimating team starts with a populated template instead of a blank screen.
Extract shipment data from bills of lading, packing slips, and carrier documents. Automatically reconcile against expected shipments and flag discrepancies.
Handle PDFs, scanned images, email bodies, Excel attachments, and handwritten forms. Support for multi-language documents common in international supply chains.
Extracted data flows into your ERP with configurable validation rules — item number verification, price tolerance checks, quantity limits, and customer account validation. Errors are caught before entry, not after.
Inventory all document types processed manually — POs, invoices, RFQs, BOLs, spec sheets. Quantify volume, error rates, processing time, and cost per document. Prioritize by ROI.
Configure extraction templates for your most common document formats and train models on your specific document variations. Each supplier and customer may format documents differently — the model handles it.
Define the validation rules that catch errors before data enters your ERP — item master lookup, price tolerance, quantity checks, customer/vendor verification, and duplicate detection.
Connect extraction outputs to your ERP's order entry, AP, or purchasing modules. Configure exception routing for low-confidence extractions or validation failures.
Deploy with accuracy tracking, exception dashboards, and continuous model updates. Extraction accuracy improves as the model processes more of your specific document formats.
AI Document Intelligence for Charlotte automotive operations - configured around local workflows, data ownership, and implementation governance.
AI Document Intelligence for Charlotte energy infrastructure operations - configured around local workflows, data ownership, and implementation governance.
AI Document Intelligence for Charlotte aerospace & defense operations - configured around local workflows, data ownership, and implementation governance.
AI Document Intelligence for Charlotte food & beverage operations - configured around local workflows, data ownership, and implementation governance.
AI Document Intelligence for Charlotte financial services operations - configured around local workflows, data ownership, and implementation governance.
AI Document Intelligence for Charlotte healthcare & medical operations - configured around local workflows, data ownership, and implementation governance.
PDF (native and scanned), email bodies, Excel/CSV attachments, TIFF/JPG images (including fax), Word documents, and EDI. If your team currently reads it and types data from it, we can automate the extraction.
Typical results: 90–97% field-level accuracy out of the gate for well-structured documents (typed PDFs with consistent formats). Accuracy improves to 95–99% after model tuning on your specific document formats. Low-confidence extractions are routed for human review.
Yes. We integrate with Odoo and legacy ERP systems. Extracted data maps to Odoo's field structure and enters through the Odoo API or Python-based integration middleware.
At $15–$25 per manually processed document (labor + error correction), a manufacturer processing 500 POs and 500 invoices monthly saves $180K–$300K annually. Most deployments pay for themselves within 4–6 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.
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.
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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