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. 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.
Handles buyer-facing transactions — product configuration, pricing, quote generation, order entry, and confirmation. Buyers get instant, accurate responses. Your sales team handles relationships, not administration.
Monitors market signals, cost inputs, and margin thresholds in real time. Adjusts pricing dynamically, flags margin risk, and enforces pricing rules across every channel — without manual intervention.
Reads sales velocity, seasonal patterns, supplier lead times, and external demand signals. Generates reorder recommendations, flags stockout risk, and adjusts purchasing before the problem hits the warehouse floor.
Monitors order status, carrier performance, and delivery windows in real time. Re-routes exceptions, triggers customer notifications, and resolves fulfillment issues without a human touching the queue.
Catches the edge cases — credit holds, substitution requests, freight changes, approval escalations — and resolves them through rules-based reasoning. Handles what used to fill inboxes.
Monitors your entire operation — order flow, inventory levels, fulfillment performance, margin trends — and surfaces anomalies, risks, and opportunities to leadership before they become problems.
Map every manual workflow that represents a bottleneck — exceptions, approvals, data entry, routing decisions. Identify which ones are high-volume, rules-driven, and safe to automate.
Define the decision logic, data inputs, and action boundaries for each agent. Agents operate within governance rules — they do not make decisions outside their defined scope.
Connect agents to your ERP, OMS, WMS, and pricing systems so they read and write live operational data. Agents that cannot access real data cannot make accurate decisions.
Deploy agents in shadow mode first — they process real workflows but route outputs to a review queue before taking action. You validate accuracy before live autonomy is enabled.
Enable live operation with full audit logging. Every agent decision is recorded — what it saw, what it decided, what it did. Monitoring dashboards surface anomalies immediately.
Agent logic improves over time as edge cases are identified and decision rules are refined. We operate the agents post-deployment and tune them against production outcomes.
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We build direct integrations to Odoo via its Python API, and to legacy ERP systems via Python-based middleware on AWS. Agents read and write live operational data without requiring an ERP replacement or upgrade.
Every agent operates within defined boundaries and logs every decision. For high-stakes workflows, agents can be configured to flag for human review rather than act autonomously. In shadow mode during deployment, every decision is reviewed before going live. Wrong decisions are caught and the logic is corrected before they recur.
Yes. Most engagements start with one high-volume, clearly-scoped workflow — a commerce agent, an exception resolution agent, or a fulfillment routing agent. Each deployment builds the integration infrastructure and governance framework that makes subsequent agents faster to deploy.
A single, well-scoped agent typically takes 6–10 weeks from workflow audit to live operation. Complex agents requiring deep ERP integration or multi-system data reads take 10–14 weeks. Shadow mode deployment runs 2–3 weeks before live autonomy is enabled.
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.
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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