[{"data":1,"prerenderedAt":482},["ShallowReactive",2],{"site-info":3,"work-list-zh-TW":99},{"id":4,"company":5,"extension":16,"meta":17,"services":18,"social":96,"stem":97,"__hash__":98},"site\u002Fsite.yml",{"name":6,"nameEn":7,"email":8,"address":9,"addressEn":10,"lineUrl":11,"lineId":12,"duns":13,"youtube":14,"youtubeId":15},"銡策數位有限公司","Jecision Digital Co., Ltd.","contact@jecision.com","231 新北市新店區北新路二段 262 號 3 樓之 3","3F.-3, No. 262, Sec. 2, Beixin Rd., Xindian Dist., New Taipei City 231, Taiwan","https:\u002F\u002Fline.me\u002FR\u002Fti\u002Fp\u002F@374qnumi","@374qnumi","963905705","https:\u002F\u002Fwww.youtube.com\u002Fshorts\u002F6UE0WTmyjsg","6UE0WTmyjsg","yml",{},[19,43,69],{"key":20,"zh":21,"en":32},"audit",{"title":22,"tagline":23,"summary":24,"points":25,"deliverable":29,"detail":30,"duration":31},"企業 AI 導入健檢","先看清楚，再花錢","60 分鐘線上訪談，盤點最耗時、最依賴人力的營運環節；我們會告訴你哪些工作現在就能交給 AI、哪些還不成熟。",[26,27,28],"現況盤點：梳理日常營運中最耗時、最依賴人力的環節","痛點診斷：判斷哪些工作現在就能交給 AI，哪些建議先保留人力","可行性評估：依你的業務特性與預算，篩選適合的模型與工具","《企業 AI 導入建議報告書》（PDF）","含 1–2 個「開發成本最低、成效最明顯」的優先項目、技術棧建議、預期節省的人力時間與下一步","訪談 60 分鐘，報告書 3 個工作天內交付",{"title":33,"tagline":34,"summary":35,"points":36,"deliverable":40,"detail":41,"duration":42},"AI Readiness Audit","Look before you spend","A 60-minute remote audit of the workflows that eat the most time and people-power — and an honest answer on what AI can take over today, and what it cannot.",[37,38,39],"Audit the daily workflows that consume the most time and headcount","Triage which tasks AI can own today, and which should stay human","Shortlist the models and tools that fit your budget and constraints","AI Adoption Recommendation Report (PDF)","One or two highest-ROI entry points, a recommended tech stack, expected hours saved, and the concrete next step","60-minute call, report delivered within 3 business days",{"key":44,"zh":45,"en":56},"prompt",{"title":46,"tagline":47,"summary":48,"points":49,"deliverable":53,"detail":54,"duration":55},"提示詞工程","讓 AI 穩定產出，而不是碰運氣","針對你的實際任務設計提示詞與多步驟工作流，並做極限測試，確保換了資料、換了人操作也不會走鐘。",[50,51,52],"客製提示詞：ChatGPT、Claude、Gemini、Midjourney 等主流工具的專屬模板","除錯與優化：修掉幻覺、格式跑掉、時好時壞的問題","工作流建置：把複雜任務拆成多步驟提示詞與可重複的 SOP","提示詞文件或設定檔","附使用說明與範例，並提供 1 對 1 教學，確保你與團隊能自己維護","依任務複雜度報價，通常 3–7 個工作天",{"title":57,"tagline":58,"summary":59,"points":60,"deliverable":66,"detail":67,"duration":68},"Prompt Engineering","Make AI produce reliably, not by luck","Prompt sets and multi-step workflows designed for your actual tasks, stress-tested so they keep working when the data or the operator changes.",[61,62,64],"Custom prompts for ChatGPT, Claude, Gemini, Midjourney and friends",{"Debugging and tuning":63},"kill hallucinations, broken formats and flaky output",{"Workflow design":65},"break complex work into repeatable multi-step SOPs","Prompt library or exportable config","Ships with usage notes and examples, plus a 1:1 walkthrough so your team can maintain it","Quoted by complexity, typically 3–7 business days",{"key":70,"zh":71,"en":82},"poc",{"title":72,"tagline":73,"summary":74,"points":75,"deliverable":79,"detail":80,"duration":81},"極速 PoC","五天，看到能跑的東西","不談架構藍圖，直接做一個能動的雛形。五個工作天內交付線上可操作的智能體，用你真實的資料驗證。",[76,77,78],"Day 1：15 分鐘對焦，鎖定單一痛點與資料盤點","Day 2–4：模型串接、邏輯設計與雛形開發","Day 5：Demo 展示與 AI 擴展建議書","可線上操作的智能體雛形","交付範圍為概念驗證與展示，不含後續維運或大規模伺服器部署；要進階成完整系統會另行報價","5 個工作天",{"title":83,"tagline":84,"summary":85,"points":86,"deliverable":93,"detail":94,"duration":95},"Rapid PoC","Five days to something that runs","No architecture decks — a working prototype built in five business days, validated against your real data.",[87,89,91],{"Day 1":88},"15-minute scoping call, one pain point, data inventory",{"Day 2–4":90},"model integration, logic design, prototype build",{"Day 5":92},"live demo plus an AI expansion recommendation","A runnable online agent prototype","Proof of concept and demo scope only; ongoing operations and large-scale deployment are quoted separately","5 business days",[],"site","XTwARfVAOcrXNiKehsM6V6cdkd2HXy4d5yZVrSWrI3Q",[100,250,372],{"id":101,"title":102,"body":103,"client":220,"cover":221,"coverAlt":222,"description":223,"draft":224,"extension":225,"industry":226,"lang":227,"meta":228,"navigation":229,"order":230,"path":231,"results":232,"seo":242,"services":243,"slug":246,"stem":247,"translationKey":248,"year":248,"__hash__":249},"work\u002Fwork\u002Fvideo-transcript-pipeline.md","把 B 站與 TikTok 的影片，變成一份可讀的摘要",{"type":104,"value":105,"toc":211},"minimark",[106,110,114,122,125,128,156,159,165,168,178,184,187,190,196,199],[107,108,109],"h2",{"id":109},"問題",[111,112,113],"p",{},"追蹤的創作者一多，影片就用「支」在長。想從裡面找一個觀點，等於要把幾十支短片從頭看完，而且看完也記不住。",[111,115,116,117,121],{},"真正麻煩的不是「下載」，是",[118,119,120],"strong",{},"判斷哪一支值得看","——平台的推薦是為了留住你，不是為了回答你的問題。",[107,123,124],{"id":124},"做法",[111,126,127],{},"整條管線分成四段，每一段都可以單獨重跑：",[129,130,131,138,144,150],"ol",{},[132,133,134,137],"li",{},[118,135,136],{},"題材篩選","：先用使用者設定的關注主題（產業、關鍵字、創作者）過濾，把不相關的影片擋在最前面，省掉後續所有成本。",[132,139,140,143],{},[118,141,142],{},"抓取","：把影片與 metadata 存下來，保留原始連結以便回溯。",[132,145,146,149],{},[118,147,148],{},"轉錄","：在地端跑 WhisperX，產出帶時間戳的逐字稿。不上傳到任何第三方語音服務。",[132,151,152,155],{},[118,153,154],{},"彙整","：把逐字稿全部丟給大模型，要求它輸出「這批影片在講什麼、哪些觀點重複出現、哪裡互相矛盾」。",[107,157,158],{"id":158},"幾個關鍵決定",[111,160,161,164],{},[118,162,163],{},"為什麼轉錄要放地端。"," 逐字稿等於影片的全文，裡面常有還沒公開的商業資訊。丟到雲端語音 API 等於把內容送出去，這對內容團隊是不可接受的。",[111,166,167],{},"而且實測下來，地端跑一次的成本比按分鐘計價的語音 API 更低——影片量一大，差距就出來了。安全和成本這次剛好站在同一邊。",[111,169,170,173,174,177],{},[118,171,172],{},"為什麼不是「每支影片各自摘要」。"," 單支摘要的價值有限，真正有用的是",[118,175,176],{},"跨影片的彙整","——同一個話題，十支影片裡有幾支在講同一件事、有幾支在反駁。這件事只能把文本集中起來做。",[111,179,180,183],{},[118,181,182],{},"為什麼要保留時間戳。"," 摘要只給結論，但人要驗證。保留時間戳，看到有興趣的段落可以直接跳回原片。",[107,185,186],{"id":186},"結果",[111,188,189],{},"工作的形狀變了：原本是「今天要看幾支影片」，現在是「今天要讀一份摘要，有興趣的再點回原片」。",[191,192,193],"blockquote",{},[111,194,195],{},"這條管線沒有做「自動判斷影片好壞」。那件事目前還不可靠，硬做只會讓使用者失去信任。我們讓它做它做得到的事：轉錄、去重、抓重點。",[107,197,198],{"id":198},"目前沒做的",[200,201,202,205,208],"ul",{},[132,203,204],{},"沒有做即時（incremental）更新，是批次跑",[132,206,207],{},"沒有做跨語言翻譯，逐字稿維持原語言",[132,209,210],{},"摘要品質靠 prompt 與人工抽查，還沒有系統性的評估指標",{"title":212,"searchDepth":213,"depth":213,"links":214},"",2,[215,216,217,218,219],{"id":109,"depth":213,"text":109},{"id":124,"depth":213,"text":124},{"id":158,"depth":213,"text":158},{"id":186,"depth":213,"text":186},{"id":198,"depth":213,"text":198},"內容研究團隊（客戶不公開）","\u002Fimages\u002Fwork\u002Fvideo-transcript-pipeline.svg","影片轉錄與摘要管線示意圖","短影音平台的內容量太大，人工逐支看不可能。我們做了一條全地端管線：先篩出使用者真正關心的題材，抓下來、用 WhisperX 轉成逐字稿，再交給大模型彙整成主題摘要。",false,"md","媒體與內容","zh-TW",{},true,1,"\u002Fwork\u002Fvideo-transcript-pipeline",[233,236,239],{"label":234,"value":235},"轉錄方式","全地端",{"label":237,"value":238},"轉錄模型","WhisperX",{"label":240,"value":241},"輸出","逐字稿 + 主題摘要",{"title":102,"description":223},[244,245],"automation","edge","video-transcript-pipeline","work\u002Fvideo-transcript-pipeline",null,"PB1cKrxqzjBbRF-lQkSUtIk_t1kHtvzz7_Tujm3xveA",{"id":251,"title":252,"body":253,"client":350,"cover":351,"coverAlt":352,"description":353,"draft":224,"extension":225,"industry":354,"lang":227,"meta":355,"navigation":229,"order":213,"path":356,"results":357,"seo":367,"services":368,"slug":369,"stem":370,"translationKey":248,"year":248,"__hash__":371},"work\u002Fwork\u002Freceipt-invoice-ocr.md","台灣的收據與發票，直接讀進資料庫",{"type":104,"value":254,"toc":338},[255,257,260,263,270,272,277,280,284,287,294,298,301,304,308,311,313,320,325,327],[107,256,109],{"id":109},[111,258,259],{},"台灣的收據不是一種東西。",[111,261,262],{},"電子發票有 QR code 與固定欄位，傳統三聯式發票是印刷格線，手寫收據則是另一個世界——字跡、印章、螢光筆、折痕、拍照角度，每一項都會讓通用 OCR 出錯。",[111,264,265,266,269],{},"出錯的方式還很討厭：",[118,267,268],{},"它不會說自己錯了","。品項少抓一欄、稅額多一個 0，看起來都很正常，等到對帳才發現。",[107,271,124],{"id":124},[273,274,276],"h3",{"id":275},"_1-先把圖修好再談辨識","1. 先把圖修好，再談辨識",[111,278,279],{},"影像前處理比模型更關鍵。歪掉的、逆光的、皺掉的單據，先做裁切、透視校正、對比增強，讓模型看到的是「一張正面的、乾淨的紙」。這一步做好，後面的辨識率差距非常明顯。",[273,281,283],{"id":282},"_2-地端-qwen-做欄位抽取","2. 地端 Qwen 做欄位抽取",[111,285,286],{},"模型直接輸出結構化欄位：日期、店家、統編、品項、數量、單價、稅額、總額。",[111,288,289,290,293],{},"跑在地端不只是為了省 API 費用，而是",[118,291,292],{},"收據裡有客戶的消費明細","，那是不能隨便送出去的資料。",[273,295,297],{"id":296},"_3-寫回資料庫而不是丟一個-excel","3. 寫回資料庫，而不是丟一個 Excel",[111,299,300],{},"辨識完不是產出一份表格讓人工再貼一次。我們把輸出接上一個自動寫入的流程，直接寫進資料庫的對應欄位——會計打開系統，資料已經在裡面。",[111,302,303],{},"這個流程的技術形式，是我們替客戶的 AI agent 寫的一個「技能」（skill）：它收到結構化資料後，先驗證統編與日期，再寫進對應的資料表。對使用者來說，它就是「不用再手動輸入」而已。",[273,305,307],{"id":306},"_4-信心分數決定誰要覆核","4. 信心分數決定誰要覆核",[111,309,310],{},"低信心的欄位會被標記出來，會計只需要看那幾欄。高信心的直接放行。",[107,312,186],{"id":186},[111,314,315,316,319],{},"人工的角色從「輸入」變成「覆核」。這件事的差別不只是快，而是",[118,317,318],{},"錯誤會被看見","——低信心的欄位一定有人看過，而不是像以前一樣，錯了也要等到月底對帳才爆出來。",[191,321,322],{},[111,323,324],{},"我們沒有宣稱「全自動」。單據辨識在台灣的現實是：做得再好，還是會有一小部分需要人看。把它設計成「人看那一小部分」才是可維運的作法。",[107,326,198],{"id":198},[200,328,329,332,335],{},[132,330,331],{},"沒有做手寫金額的專門模型，手寫比例高的單據仍以人工為主",[132,333,334],{},"沒有做多頁合併（一張收據拍成兩張照片）",[132,336,337],{},"準確率數字尚未做系統性量測，不敢寫在網站上",{"title":212,"searchDepth":213,"depth":213,"links":339},[340,341,348,349],{"id":109,"depth":213,"text":109},{"id":124,"depth":213,"text":124,"children":342},[343,345,346,347],{"id":275,"depth":344,"text":276},3,{"id":282,"depth":344,"text":283},{"id":296,"depth":344,"text":297},{"id":306,"depth":344,"text":307},{"id":186,"depth":213,"text":186},{"id":198,"depth":213,"text":198},"會計與記帳單位（客戶不公開）","\u002Fimages\u002Fwork\u002Freceipt-invoice-ocr.svg","收據與發票辨識示意圖","台灣的單據格式太雜——電子發票、傳統三聯式、手寫收據混在一起，通用 OCR 經常抓不到品項與稅額。我們在地端跑 Qwen 視覺模型辨識，再直接寫進資料庫，會計只需要覆核。","財會作業",{},"\u002Fwork\u002Freceipt-invoice-ocr",[358,361,364],{"label":359,"value":360},"推論位置","地端",{"label":362,"value":363},"辨識模型","Qwen（視覺）",{"label":365,"value":366},"人工角色","覆核，不重新輸入",{"title":252,"description":353},[244,245],"receipt-invoice-ocr","work\u002Freceipt-invoice-ocr","_A9zt0dilnQ4Cfaxq0CFeepL_s3k9i_DsUwocDv97v0",{"id":373,"title":374,"body":375,"client":460,"cover":461,"coverAlt":462,"description":463,"draft":224,"extension":225,"industry":464,"lang":227,"meta":465,"navigation":229,"order":344,"path":466,"results":467,"seo":477,"services":478,"slug":479,"stem":480,"translationKey":248,"year":248,"__hash__":481},"work\u002Fwork\u002Fembedded-vision-background.md","開公司之前：二十年嵌入式與影像辨識的底子",{"type":104,"value":376,"toc":453},[377,380,387,390,393,407,414,417,423,429,435,438,445,448,450],[107,378,379],{"id":379},"為什麼要寫這一頁",[111,381,382,383,386],{},"很多人以為「AI 顧問」的工作是選模型、寫 prompt。但真正讓專案失敗的，通常不是模型不夠強，而是",[118,384,385],{},"沒人處理模型以外的那 90%","：裝置跑不動、光線一變就失效、資料格式對不上、上線後沒有人會維護。",[111,388,389],{},"我們的底子是在那裡練出來的。",[107,391,392],{"id":392},"當時的條件",[200,394,395,398,401,404],{},[132,396,397],{},"沒有 GPU，推論要擠進 CPU 的預算裡",[132,399,400],{},"記憶體以百 MB 計，模型不能「先載入再說」",[132,402,403],{},"裝置在現場：會震、會熱、會斷電",[132,405,406],{},"沒有人會在你旁邊看 log，錯了就是產線停",[111,408,409,410,413],{},"在這種環境裡，「準確率 95%」不是終點，",[118,411,412],{},"「連續跑三個月不出事」才是","。",[107,415,416],{"id":416},"這段經歷留下來的三個習慣",[111,418,419,422],{},[118,420,421],{},"一、先量測，再優化。"," 在邊緣裝置上，憑感覺優化通常會優化錯地方。先找出真正的瓶頸——是前處理、是模型、還是 I\u002FO。",[111,424,425,428],{},[118,426,427],{},"二、為失敗設計。"," 現場沒有人會看 exception。模型信心不足時要能退回一個安全的行為，而不是整個流程卡住。",[111,430,431,434],{},[118,432,433],{},"三、交付要能被接手。"," 代碼、參數、資料格式、怎麼重新訓練——如果對方接不了，這個專案就沒有結束的一天。",[107,436,437],{"id":437},"和現在的工作有什麼關係",[111,439,440,441,444],{},"現在做 LLM 專案，面對的其實是同一組問題：模型只是其中一塊，",[118,442,443],{},"資料從哪來、錯的時候怎麼辦、誰來維護","才是決勝點。",[111,446,447],{},"這也是我們堅持「先健檢、再 PoC」的原因——先確認問題是真的、資料是有的、人願意改流程，再去談模型。",[107,449,198],{"id":198},[111,451,452],{},"這一頁還沒有放具體的專案細節（客戶、裝置、量測數字）。那些需要取得客戶同意才能公開；如果你們手上也有類似條件的專案，歡迎直接聊。",{"title":212,"searchDepth":213,"depth":213,"links":454},[455,456,457,458,459],{"id":379,"depth":213,"text":379},{"id":392,"depth":213,"text":392},{"id":416,"depth":213,"text":416},{"id":437,"depth":213,"text":437},{"id":198,"depth":213,"text":198},"團隊背景","\u002Fimages\u002Fwork\u002Fembedded-vision-background.svg","嵌入式影像辨識示意圖","在成立銡策之前，團隊做的是資源受限裝置上的即時影像辨識——沒有 GPU、記憶體只有幾百 MB，還要在真實的光線與震動下跑得穩。這段經歷決定了我們後來做 AI 專案的方式。","嵌入式系統",{},"\u002Fwork\u002Fembedded-vision-background",[468,471,474],{"label":469,"value":470},"累積時間","20+ 年",{"label":472,"value":473},"主要場景","資源受限裝置",{"label":475,"value":476},"擅長","把模型調到能上線",{"title":374,"description":463},[245],"embedded-vision-background","work\u002Fembedded-vision-background","fS1oozwrTs7BE73bTi2eqs5QigRLHVpHU5ZmOvOodR8",1792775650057]