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個台灣實際專案的做法與費用（2026）",{"type":97,"value":98,"toc":600},"minimark",[99,107,112,115,277,280,284,287,292,298,304,316,320,326,330,333,337,342,347,357,361,364,368,374,378,387,390,394,397,401,406,410,415,419,424,428,431,508,515,519,522,562,565,569,572,576,579,583,586,590,593,597],[100,101,102,106],"p",{},[103,104,105],"strong",{},"企業 AI 導入能不能成功，看的不是用了哪個模型，而是 AI 有沒有接進每天在跑的流程。"," 以下 8 個案例都是 JoinX 實際開發的專案，涵蓋研究機構、出版、保險、平台與醫療科技。未標名稱的專案，依保密約定以產業描述呈現。",[108,109,111],"h2",{"id":110},"這-8-個-ai-導入案例分別在做什麼","這 8 個 AI 導入案例分別在做什麼？",[100,113,114],{},"8 個案例可以依「AI 處理什麼資料」分成四類：訊號與檢測、文本與語音、AI Agent 與搜尋、模型資料與產品維運。先看總表，再看各案例的做法。",[116,117,118,140],"table",{},[119,120,121],"thead",{},[122,123,124,128,131,134,137],"tr",{},[125,126,127],"th",{},"案例",[125,129,130],{},"產業",[125,132,133],{},"AI 處理的資料",[125,135,136],{},"AI 在流程中的角色",[125,138,139],{},"期間",[141,142,143,161,178,195,212,228,243,260],"tbody",{},[122,144,145,149,152,155,158],{},[146,147,148],"td",{},"國家原子能科技研究院",[146,150,151],{},"研究機構",[146,153,154],{},"渦電流檢測訊號",[146,156,157],{},"自動標出疑似瑕疵，人員複核",[146,159,160],{},"2022–2025",[122,162,163,166,169,172,175],{},[146,164,165],{},"電子量測設備大廠",[146,167,168],{},"製造業",[146,170,171],{},"量測設備相關資料",[146,173,174],{},"AI 工具軟體",[146,176,177],{},"2023 起",[122,179,180,183,186,189,192],{},[146,181,182],{},"聯經數位",[146,184,185],{},"出版",[146,187,188],{},"電子書文本",[146,190,191],{},"拆解文本、辨識角色、生成配音",[146,193,194],{},"已上線",[122,196,197,200,203,206,209],{},[146,198,199],{},"保險公司",[146,201,202],{},"保險",[146,204,205],{},"業務情境對話",[146,207,208],{},"AI Agent 對話介面與操作流程",[146,210,211],{},"2026",[122,213,214,217,220,223,226],{},[146,215,216],{},"GoodParty 享派兑",[146,218,219],{},"平台",[146,221,222],{},"平台既有資料",[146,224,225],{},"AI 搜尋",[146,227,211],{},[122,229,230,233,235,238,241],{},[146,231,232],{},"台灣設計研究院",[146,234,151],{},[146,236,237],{},"設計應用情境",[146,239,240],{},"AI 應用研究，第一階段完成",[146,242,211],{},[122,244,245,248,251,254,257],{},[146,246,247],{},"醫療科技公司",[146,249,250],{},"醫療科技",[146,252,253],{},"穿戴裝置數據、深度影像",[146,255,256],{},"收集模型訓練資料",[146,258,259],{},"2025",[122,261,262,265,268,271,274],{},[146,263,264],{},"健康科技新創",[146,266,267],{},"健康科技",[146,269,270],{},"AI 健康 APP",[146,272,273],{},"APP 維運與新版後端 API",[146,275,276],{},"2024 起",[100,278,279],{},"從表上可以看出，8 個案例裡只有 3 個的核心是「訓練或呼叫模型」，其餘 5 個的關鍵工作在資料收集、介面設計或系統串接。這也是企業評估 AI 專案時最常低估的部分。",[108,281,283],{"id":282},"訊號與檢測ai-怎麼接手人員的重複判讀","訊號與檢測：AI 怎麼接手人員的重複判讀？",[100,285,286],{},"做法是讓 AI 先做初判、標出可疑的部分，人員只複核 AI 挑出來的結果。判讀規則越固定、資料量越大，AI 能省下的人力越多，但驗收標準一定要先定好。",[288,289,291],"h3",{"id":290},"國家原子能科技研究院渦電流訊號-ai-判讀與自動化系統","國家原子能科技研究院：渦電流訊號 AI 判讀與自動化系統",[100,293,294,297],{},[103,295,296],{},"起點："," 渦電流檢測用於核電廠金屬管路、蒸汽產生器管束與熱交換器的完整性檢測。傳統做法高度依賴檢測人員的經驗，人工分析大量訊號耗時，判讀結果也容易因人而異。",[100,299,300,303],{},[103,301,302],{},"我們做的："," 以 TensorFlow 建立卷積神經網路（CNN）模型，依頻道分別訓練，再整合三個波段的資訊做統一判斷；另外用 OpenCV 做 GUI 自動化，自動開檔、定位訊號點、擷取圖像並產出報告。開發策略是「先單頻道、後多頻道」，並把 AI 辨識和自動化控制拆成兩套獨立開發，降低風險。",[100,305,306,309,310,315],{},[103,307,308],{},"合作期間："," 2022 至 2025 年。完整做法見",[311,312,314],"a",{"href":313},"\u002Fcase-studies\u002Fnari\u002F","國家原子能科技研究院案例","。",[288,317,319],{"id":318},"電子量測設備大廠ai-工具軟體開發","電子量測設備大廠：AI 工具軟體開發",[100,321,322,325],{},[103,323,324],{},"起點與做法："," 為電子量測設備大廠開發 AI 工具軟體，2023 年開始合作。依保密約定，這個案例只揭露產業與專案類型。",[108,327,329],{"id":328},"文本與語音ai-能把內容生產做到多自動","文本與語音：AI 能把內容生產做到多自動？",[100,331,332],{},"可以做到從上傳原稿到產出配音的整條流程自動化，但每一步都要設成功率門檻，人工負責調整與審聽。生成式 AI 擅長拆解與標記，品質把關仍要留給人。",[288,334,336],{"id":335},"聯經數位ai-文本拆解與角色配音的有聲書平台","聯經數位：AI 文本拆解與角色配音的有聲書平台",[100,338,339,341],{},[103,340,296],{}," 傳統有聲書仰賴大量人工判讀與配音，成本高、產能有限。聯經數位希望把靜態電子書轉成角色鮮明、有情緒的有聲內容。",[100,343,344,346],{},[103,345,302],{}," 設計從原稿上傳、文本拆解、角色辨識、情緒分析、聲音標記到語音合成的完整流程。系統以平行運算串接多組 ChatGPT API，目標是在兩分鐘內完成整本小說的分析；驗收門檻設定為角色辨識成功率 90%、文本拆解成功率 75% 以上。角色與配音模組可以重複使用，同系列書籍的製作週期因此縮短。",[100,348,349,352,353,315],{},[103,350,351],{},"成果："," 這個平台獲得 iF 設計獎。完整做法見",[311,354,356],{"href":355},"\u002Fcase-studies\u002Faicast\u002F","聯經數位案例",[108,358,360],{"id":359},"ai-agent-與-ai-搜尋怎麼讓第一線人員真的用起來","AI Agent 與 AI 搜尋：怎麼讓第一線人員真的用起來？",[100,362,363],{},"關鍵在介面與流程，不在模型。AI Agent 要依第一線人員的工作順序設計，人負責最後確認；AI 搜尋則最好直接加在既有平台上，沿用原本的資料與後台。",[288,365,367],{"id":366},"保險公司ai-agent-介面設計與製作","保險公司：AI Agent 介面設計與製作",[100,369,370,373],{},[103,371,372],{},"做法："," 2026 年為保險業務情境設計 AI Agent 的對話介面與操作流程，讓第一線人員真的用得起來。設計重點是讓 AI 依業務的工作順序協助查詢與整理資料，最後由人確認。",[288,375,377],{"id":376},"goodparty-享派兑在既有平台加入-ai-搜尋","GoodParty 享派兑：在既有平台加入 AI 搜尋",[100,379,380,382,383,315],{},[103,381,372],{}," GoodParty 平台由 JoinX 開發並持續維運。2026 年在平台上加入 AI 搜尋功能，沿用原本的資料與後台，沒有另外更換系統。這類「在既有系統上加 AI」的做法，通常比另建一套 AI 系統更快上線，也更容易維運。平台本身的開發過程見",[311,384,386],{"href":385},"\u002Fcase-studies\u002Fgoodparty\u002F","GoodParty 案例",[388,389],"article-cta",{},[108,391,393],{"id":392},"模型資料與產品維運模型之外ai-專案還要做哪些工程","模型資料與產品維運：模型之外，AI 專案還要做哪些工程？",[100,395,396],{},"AI 專案的工程量，很大一部分在模型之外：先把訓練資料收齊，上線後還要長期維運 APP 與 API，研究型專案則要分階段驗證。這些工作做不好，模型再好也用不起來。",[288,398,400],{"id":399},"醫療科技公司apple-watch-數據串接與深度影像資料收集","醫療科技公司：Apple Watch 數據串接與深度影像資料收集",[100,402,403,405],{},[103,404,372],{}," 2025 年串接 Apple Watch 的健康數據，並用 iPhone 的深度鏡頭收集影像資料，作為模型訓練的資料基礎。這個案例說明了 AI 專案常被忽略的第一步：在訓練模型之前，資料要先收得到、收得齊、格式一致。",[288,407,409],{"id":408},"健康科技新創ai-健康-app-的維運與後端-api","健康科技新創：AI 健康 APP 的維運與後端 API",[100,411,412,414],{},[103,413,372],{}," 2024 年起接手這款 AI 健康 APP 的持續維運，並開發新版後端 API，陪著產品從改名走到下一個版本。AI 產品上線之後，APP、API 與資料流程都要有人長期負責。",[288,416,418],{"id":417},"台灣設計研究院設計應用情境創新之研究","台灣設計研究院：設計應用情境創新之研究",[100,420,421,423],{},[103,422,372],{}," 2026 年與台灣設計研究院合作，以 AI 探索設計的新應用情境，目前已完成第一階段。研究型 AI 專案適合分階段進行，每一階段確認方向後再決定下一步。",[108,425,427],{"id":426},"企業-ai-導入要花多少錢要多久","企業 AI 導入要花多少錢、要多久？",[100,429,430],{},"依 JoinX 整理的台灣客製專案行情，單一場景 PoC 約 NT$30 萬至 80 萬，正式上線的單一場景約 NT$80 萬至 250 萬，多 Agent 或跨系統專案從 NT$250 萬起。金額主要取決於資料狀態、系統串接數量與驗收要求。",[116,432,433,450],{},[119,434,435],{},[122,436,437,440,444,447],{},[125,438,439],{},"階段",[125,441,443],{"align":442},"right","預估費用",[125,445,446],{},"常見內容",[125,448,449],{},"參考時程",[141,451,452,466,480,494],{},[122,453,454,457,460,463],{},[146,455,456],{},"場景評估與技術驗證",[146,458,459],{"align":442},"NT$10 萬～30 萬",[146,461,462],{},"流程盤點、資料抽樣、風險與架構建議",[146,464,465],{},"2～4 週",[122,467,468,471,474,477],{},[146,469,470],{},"單一場景 PoC",[146,472,473],{"align":442},"NT$30 萬～80 萬",[146,475,476],{},"一項任務、有限資料、人工監督",[146,478,479],{},"4～8 週",[122,481,482,485,488,491],{},[146,483,484],{},"正式上線（單一場景）",[146,486,487],{"align":442},"NT$80 萬～250 萬",[146,489,490],{},"權限、整合、監控、測試與部署",[146,492,493],{},"2～4 個月",[122,495,496,499,502,505],{},[146,497,498],{},"多 Agent 或跨系統",[146,500,501],{"align":442},"NT$250 萬以上",[146,503,504],{},"跨部門、多系統、完整治理",[146,506,507],{},"4 個月以上",[100,509,510,511,315],{},"以上是預算規劃的起點，不是固定報價。AI Agent 的細部費用與選商方式，可以參考",[311,512,514],{"href":513},"\u002Farticle\u002Fchoose-ai-agent-company-2026\u002F","AI Agent 開發公司怎麼選",[108,516,518],{"id":517},"企業要怎麼挑第一個-ai-導入場景","企業要怎麼挑第一個 AI 導入場景？",[100,520,521],{},"從「資料拿得到、結果能由人複核、做錯代價可控」的流程開始。上面 8 個案例的共同點，是每一個都有明確的輸入資料，也有人負責確認 AI 的輸出。",[523,524,525,532,538,544,550,556],"ol",{},[526,527,528,531],"li",{},[103,529,530],{},"列出重複性高的判讀或整理工作。"," 例如訊號判讀、文件整理、客戶資料摘要。",[526,533,534,537],{},[103,535,536],{},"確認資料拿得到。"," 資料在哪套系統、格式是否一致、有沒有權限可以讀；拿不到資料，就要先規劃資料收集。",[526,539,540,543],{},[103,541,542],{},"定義成功率門檻。"," 先說清楚多少準確率算過關，例如聯經數位的角色辨識 90%。",[526,545,546,549],{},[103,547,548],{},"設計人工複核的位置。"," 決定哪些結果由人確認、做錯時怎麼回復。NIST 的 AI 風險管理框架與 ISO\u002FIEC 42001 都把人為監督列為治理重點。",[526,551,552,555],{},[103,553,554],{},"確認哪些資料不能送進生成式 AI。"," 機敏資料與個資要先分級；公部門可參考國科會的生成式 AI 參考指引。",[526,557,558,561],{},[103,559,560],{},"先做 PoC，通過門檻再接進正式系統。"," 正式上線時再補齊權限、監控與維運。",[108,563,564],{"id":564},"常見問題",[288,566,568],{"id":567},"台灣企業最常見的-ai-導入類型是什麼","台灣企業最常見的 AI 導入類型是什麼？",[100,570,571],{},"從 JoinX 的專案來看，最常見的是文件與文本處理、訊號或影像判讀、AI Agent 與 AI 搜尋，以及為模型收集資料。共同點是 AI 都接在既有流程裡，而不是獨立的聊天機器人。",[288,573,575],{"id":574},"沒有自己的資料可以做-ai-導入嗎","沒有自己的資料，可以做 AI 導入嗎？",[100,577,578],{},"可以，但第一步通常是收集資料，而不是訓練模型。例如醫療科技公司的案例，就是先串接穿戴裝置與深度鏡頭，把訓練資料收齊。",[288,580,582],{"id":581},"可以在既有系統上加-ai-功能不用整套重做嗎","可以在既有系統上加 AI 功能，不用整套重做嗎？",[100,584,585],{},"可以。GoodParty 的 AI 搜尋就是加在既有平台上，沿用原本的資料與後台。既有系統有 API、資料結構清楚時，這通常是最快上線的做法。",[288,587,589],{"id":588},"ai-判讀的結果可以直接用嗎","AI 判讀的結果可以直接用嗎？",[100,591,592],{},"不建議直接用。比較穩當的做法是 AI 先做初判、標出可疑項目，人員再複核。上線前要先定好成功率門檻，驗收時用同一批測試資料比對。",[288,594,596],{"id":595},"ai-導入專案上線後還要維運嗎","AI 導入專案上線後還要維運嗎？",[100,598,599],{},"要。模型版本、API、APP 與資料流程都會變動，需要有人長期負責。健康科技新創的案例，就是在 AI APP 上線後持續維運，並開發新版後端 API。",{"title":601,"searchDepth":602,"depth":602,"links":603},"",2,[604,605,610,613,617,622,623,624],{"id":110,"depth":602,"text":111},{"id":282,"depth":602,"text":283,"children":606},[607,609],{"id":290,"depth":608,"text":291},3,{"id":318,"depth":608,"text":319},{"id":328,"depth":602,"text":329,"children":611},[612],{"id":335,"depth":608,"text":336},{"id":359,"depth":602,"text":360,"children":614},[615,616],{"id":366,"depth":608,"text":367},{"id":376,"depth":608,"text":377},{"id":392,"depth":602,"text":393,"children":618},[619,620,621],{"id":399,"depth":608,"text":400},{"id":408,"depth":608,"text":409},{"id":417,"depth":608,"text":418},{"id":426,"depth":602,"text":427},{"id":517,"depth":602,"text":518},{"id":564,"depth":602,"text":564,"children":625},[626,627,628,629,630],{"id":567,"depth":608,"text":568},{"id":574,"depth":608,"text":575},{"id":581,"depth":608,"text":582},{"id":588,"depth":608,"text":589},{"id":595,"depth":608,"text":596},"技術分享","\u002Fimages\u002Fblog\u002Ftaiwan-enterprise-ai-adoption-cases-2026.webp",null,"台灣企業的 AI 導入，實際做成的多半不是聊天機器人，而是把 AI 接進既有流程：檢測訊號判讀、有聲書生成、AI Agent 介面、平台 AI 搜尋與模型資料收集。本文整理 JoinX 8 個實際開發的 AI 專案，說明每個案例的起點、做法與現況，並附上依專案規模整理的費用區間與挑選第一個場景的步驟。","md",false,"zh-tw",{},true,"客製化系統","\u002Fzh-tw\u002Farticle\u002Ftaiwan-enterprise-ai-adoption-cases-2026",{"title":95,"description":634},[644,647,650],{"name":645,"issuer":646},"AI Risk Management Framework（AI RMF 1.0）","美國國家標準與技術研究院（NIST）",{"name":648,"issuer":649},"ISO\u002FIEC 42001:2023 人工智慧管理系統","國際標準化組織（ISO）",{"name":651,"issuer":652},"行政院及所屬機關（構）使用生成式 AI 參考指引","國家科學及技術委員會","zh-tw\u002Farticle\u002Ftaiwan-enterprise-ai-adoption-cases-2026",[655,656,657,658,659],"台灣企業做成的 AI 專案，多半是把 AI 接進既有流程，而不是另外做一個聊天機器人。","8 個案例裡，有 5 個的關鍵工作在資料、介面或系統串接，不在模型本身。","AI 判讀類專案要先定義成功率門檻，例如角色辨識 90%、文本拆解 75%，驗收才有依據。","第一個 AI 場景應挑資料拿得到、結果能由人複核、做錯代價可控的流程。","依 JoinX 的台灣客製專案行情，單一場景 PoC 約 NT$30 萬至 80 萬，正式上線約 NT$80 萬至 250 萬。","2026\u002F11\u002F03","blog","vj9VPpj9bLJI_vEGxfB43wDrGGkgOqgm3bPJ8DAvLR0",[664,669,673],{"slug":665,"title":666,"time":667,"cover":668,"need":640},"ai-assisted-development-cost-reduction","AI 協作開發是什麼？在控制成本的同時提升品質的方法","2026\u002F09\u002F01","\u002Fimages\u002Fblog\u002Fai-assisted-development-cost-reduction.webp",{"slug":670,"title":671,"time":667,"cover":672,"need":640},"lab-type-development-guide","Lab 型開發是什麼？與承攬型（請負）的差異、費用、適合的企業完整解析","\u002Fimages\u002Fblog\u002Flab-type-development-guide.webp",{"slug":674,"title":675,"time":667,"cover":676,"need":640},"offshore-development-failure-patterns","境外開發失敗的 5 種模式與對策","\u002Fimages\u002Fblog\u002Foffshore-development-failure-patterns.webp",1793765011899]