Aurora AI Resources｜極光有序資源Enterprise AI workflow, controlled agent execution, and implementation resources from Aurora AI.https://www.aurora-ai.tw/resources2026-09-11T00:00:00+08:00極光有序有限公司aurora@aurora-ai.twDWG 與訂單 Excel，如何從圖號匹配走到可核對交付https://www.aurora-ai.tw/resources/dwg-excel-drawing-number-matching-workflow2026-09-11T00:00:00+08:00極光有序有限公司工程文件自動化不只是在兩個檔案間找相同字串。本文拆解圖號匹配、背景任務、來源保留、合併預覽與下載交付應如何被驗收。How DWG and order spreadsheets become a verifiable deliveryhttps://www.aurora-ai.tw/en/resources/dwg-excel-drawing-number-matching-workflow2026-09-11T00:00:00+08:00極光有序有限公司Engineering-document automation is more than finding the same text in two files. This article explains drawing-number matching, background-task state, source retention, merged preview, and delivery acceptance.製造業詢價流程怎麼自動化？從 Email、工程圖到技術確認https://www.aurora-ai.tw/resources/manufacturing-rfq-intake-engineering-review2026-09-02T00:00:00+08:00極光有序有限公司製造業詢價不只是回一封信。本文拆解詢價 Email、工程附件、需求整理、圖面查找、技術確認與回覆草稿如何進入同一項可追蹤任務。工程圖面怎麼找得快？圖號、料號、版次與候選圖搜尋方法https://www.aurora-ai.tw/resources/engineering-drawing-search-revision-control2026-09-02T00:00:00+08:00極光有序有限公司工程圖面搜尋不能只靠檔名關鍵字。本文說明圖號、料號、版次、圖框與歷史資料如何形成查找條件，並讓候選圖與例外交由工程人員確認。製造業 AI 導入效益怎麼算？從工時、等待、返工與錯版建立基準https://www.aurora-ai.tw/resources/manufacturing-ai-workflow-roi-metrics2026-09-02T00:00:00+08:00極光有序有限公司製造業 AI ROI 不能只看模型回答速度。本文提供工時、等待、返工與錯版四類基準，協助團隊用一條真實流程比較導入前後。How to automate manufacturing RFQ intake from email and drawings to engineering reviewhttps://www.aurora-ai.tw/en/resources/manufacturing-rfq-intake-engineering-review2026-09-02T00:00:00+08:00極光有序有限公司A manufacturing RFQ is more than an email reply. This guide maps email, engineering attachments, requirement extraction, drawing search, technical review, and response drafting into one traceable task.How to find the right engineering drawing by number, part, revision, and candidate searchhttps://www.aurora-ai.tw/en/resources/engineering-drawing-search-revision-control2026-09-02T00:00:00+08:00極光有序有限公司Engineering drawing search cannot rely on filename keywords alone. Learn how drawing numbers, part numbers, revisions, title blocks, and historical records become explainable search conditions and reviewable candidates.How to measure manufacturing AI ROI with labor, waiting, rework, and wrong-revision baselineshttps://www.aurora-ai.tw/en/resources/manufacturing-ai-workflow-roi-metrics2026-09-02T00:00:00+08:00極光有序有限公司Manufacturing AI ROI cannot be measured by model response time alone. Use labor, waiting, rework, and wrong-revision baselines to compare one real workflow before and after implementation.第一條企業 AI 流程，先回答六個問題https://www.aurora-ai.tw/resources/enterprise-ai-workflow-assessment-checklist2026-08-25T00:00:00+08:00極光有序有限公司企業導入 AI Agent 前，應先定義成功結果、輸入來源、決策邊界、工具權限、失敗處理與負責人。這份清單協助團隊選出可驗收的第一條流程。AI Agent、RPA 與工作流程自動化，應該怎麼分工https://www.aurora-ai.tw/resources/ai-agent-vs-rpa-workflow-automation2026-08-25T00:00:00+08:00極光有序有限公司RPA 適合重複且可預先定義的操作，工作流程負責固定順序與狀態，AI Agent 則處理需要理解與判斷的步驟。本文說明三者如何搭配。企業 AI Agent 導入前，資料、權限與工具要先盤點什麼https://www.aurora-ai.tw/resources/enterprise-ai-agent-data-permission-checklist2026-08-25T00:00:00+08:00極光有序有限公司AI Agent 能否安全進入企業流程，取決於資料來源、工具權限、人工確認與失敗接續是否明確。本文提供第一階段導入盤點框架。背景任務不該因為關掉頁面就消失https://www.aurora-ai.tw/resources/background-task-state-recovery-enterprise-workflow2026-08-25T00:00:00+08:00極光有序有限公司企業檔案處理與工具執行可能需要數十秒到更久。本文說明排隊、處理、完成、失敗、離開後返回與重新執行應如何被設計和驗收。圖號比對以前，先處理格式與未匹配項目https://www.aurora-ai.tw/resources/drawing-number-normalization-unmatched-items2026-08-25T00:00:00+08:00極光有序有限公司DWG 檔名、圖框與訂單欄位常使用不同格式。本文說明如何定義圖號正規化、區分精確與規則匹配，並讓未匹配項目安全進入人工確認。企業 AI 工作流驗收，不能只測成功路徑https://www.aurora-ai.tw/resources/enterprise-ai-workflow-failure-recovery-acceptance2026-08-25T00:00:00+08:00極光有序有限公司檔案錯誤、工具無權限、外部服務逾時與人工拒絕都可能中斷流程。本文提供企業 AI 工作流失敗、重試、接手與交付驗收框架。Answer six questions before choosing the first enterprise AI workflowhttps://www.aurora-ai.tw/en/resources/enterprise-ai-workflow-assessment-checklist2026-08-25T00:00:00+08:00極光有序有限公司Before adopting an AI agent, define the successful result, input sources, decision boundary, tool access, failure handling, and accountable owner. This checklist helps a team choose a verifiable first workflow.How AI agents, RPA, and workflow automation should divide the workhttps://www.aurora-ai.tw/en/resources/ai-agent-vs-rpa-workflow-automation2026-08-25T00:00:00+08:00極光有序有限公司RPA fits repeatable predefined operations, workflows manage sequence and state, and AI agents handle steps that require interpretation and judgment. This article explains how the three approaches work together.What to inventory before an enterprise AI agent can use data and toolshttps://www.aurora-ai.tw/en/resources/enterprise-ai-agent-data-permission-checklist2026-08-25T00:00:00+08:00極光有序有限公司Safe enterprise-agent execution depends on explicit data sources, tool permissions, human confirmation, and recovery ownership. This article provides a practical first-phase inventory.Background work should not disappear when a page closeshttps://www.aurora-ai.tw/en/resources/background-task-state-recovery-enterprise-workflow2026-08-25T00:00:00+08:00極光有序有限公司Enterprise file processing and tool execution can outlast a browser request. Learn how to accept queued, processing, completed, failed, return, and rerun behavior.Normalize drawing numbers before matching and expose what remains unmatchedhttps://www.aurora-ai.tw/en/resources/drawing-number-normalization-unmatched-items2026-08-25T00:00:00+08:00極光有序有限公司DWG filenames, title blocks, and order columns often format the same identifier differently. Learn how to define normalization, separate exact and rules-based matches, and route uncertain items for review.Enterprise AI workflow acceptance must test more than the happy pathhttps://www.aurora-ai.tw/en/resources/enterprise-ai-workflow-failure-recovery-acceptance2026-08-25T00:00:00+08:00極光有序有限公司Invalid files, missing tool permissions, external timeouts, and human rejection can interrupt a workflow. Use this framework to accept failure, retry, handoff, and delivery behavior.企業需要的不是另一個聊天視窗https://www.aurora-ai.tw/resources/enterprise-ai-agent-platform2026-07-02T00:00:00+08:00極光有序有限公司企業導入 AI Agent，真正困難的是如何承接任務、執行工具、保留人工決策與交付結果。本文說明 Aurora 為什麼從工作流程，而不是聊天介面出發。不是所有步驟都要核准，關鍵動作才需要https://www.aurora-ai.tw/resources/tool-approval-enterprise-ai-agent2026-07-02T00:00:00+08:00極光有序有限公司一般整理工作可以持續執行；當 Agent 即將呼叫高影響工具時，Aurora 才把工具、主要參數與決策帶回使用者面前。工具與工作方法不能只藏在提示詞裡https://www.aurora-ai.tw/resources/mcp-skills-governance-enterprise2026-07-02T00:00:00+08:00極光有序有限公司MCP、Skills、模型 Provider 與計畫工具範圍都會影響 Agent 能做什麼。本文說明為什麼這些能力需要獨立、可查看的管理位置。製造業第一條 AI 流程，先從能驗收的工作開始https://www.aurora-ai.tw/resources/manufacturing-ai-agent-first-workflow2026-07-02T00:00:00+08:00極光有序有限公司工程附件整理、圖號匹配與檔案交付具有清楚的輸入和結果，適合用來建立第一條可驗收、可逐步擴充的 AI 流程。Enterprises do not need another chat windowhttps://www.aurora-ai.tw/en/resources/enterprise-ai-agent-platform2026-07-02T00:00:00+08:00極光有序有限公司The hard part of adopting AI agents is carrying a task through tool execution, human decisions, and a verifiable result. This article explains why Aurora starts with workflow, not chat.Not every step needs approval — only consequential actions dohttps://www.aurora-ai.tw/en/resources/tool-approval-enterprise-ai-agent2026-07-02T00:00:00+08:00極光有序有限公司Routine work can continue. When an agent is about to invoke a consequential tool, Aurora brings the tool, primary parameters, and decision back to the user.Tools and working methods cannot live only in promptshttps://www.aurora-ai.tw/en/resources/mcp-skills-governance-enterprise2026-07-02T00:00:00+08:00極光有序有限公司MCP, skills, model providers, and plan-tool scope all determine what an agent can do. This article explains why each capability needs an explicit, reviewable administrative surface.Start manufacturing AI with work the team can verifyhttps://www.aurora-ai.tw/en/resources/manufacturing-ai-agent-first-workflow2026-07-02T00:00:00+08:00極光有序有限公司Engineering-document organization, drawing-number matching, and file delivery have clear inputs and results, making them a practical first AI workflow to verify and expand.