[{"data":1,"prerenderedAt":497},["ShallowReactive",2],{"site-info":3,"work-list-en":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",[100,255,384],{"id":101,"title":102,"body":103,"client":225,"cover":226,"coverAlt":227,"description":228,"draft":229,"extension":230,"industry":231,"lang":232,"meta":233,"navigation":234,"order":235,"path":236,"results":237,"seo":247,"services":248,"slug":251,"stem":252,"translationKey":253,"year":253,"__hash__":254},"work\u002Fwork\u002Fen\u002Fvideo-transcript-pipeline.md","From Bilibili and TikTok to one readable digest",{"type":104,"value":105,"toc":216},"minimark",[106,111,115,123,127,130,158,162,168,171,181,187,191,194,200,204],[107,108,110],"h2",{"id":109},"the-problem","The problem",[112,113,114],"p",{},"Follow a few creators and the backlog grows by the dozen. Finding one specific point of view means watching everything, and remembering none of it.",[112,116,117,118,122],{},"The hard part is not downloading. It is ",[119,120,121],"strong",{},"deciding which video is worth watching"," — platform recommendations are optimised for keeping you there, not for answering your question.",[107,124,126],{"id":125},"what-we-built","What we built",[112,128,129],{},"A four-stage pipeline where every stage can be re-run on its own:",[131,132,133,140,146,152],"ol",{},[134,135,136,139],"li",{},[119,137,138],{},"Topic filter"," — match against the themes, keywords and creators a user actually cares about, and drop everything else before it costs anything downstream.",[134,141,142,145],{},[119,143,144],{},"Fetch"," — store the video plus metadata, keeping the original link for traceability.",[134,147,148,151],{},[119,149,150],{},"Transcribe"," — run WhisperX on-premise to produce a timestamped transcript. Nothing goes to a third-party speech API.",[134,153,154,157],{},[119,155,156],{},"Summarise"," — feed the whole transcript set to a large model and ask for what the videos are saying, which points repeat, and where they contradict each other.",[107,159,161],{"id":160},"three-decisions-worth-explaining","Three decisions worth explaining",[112,163,164,167],{},[119,165,166],{},"Why transcription stays on-premise."," A transcript is the full text of the video, and it often contains commercial information that has not been published yet. Sending that to a cloud speech API is not something a content team can accept.",[112,169,170],{},"It also turns out to be cheaper: per-minute speech APIs add up quickly, and at volume the on-premise run wins. This time security and cost happened to point the same way.",[112,172,173,176,177,180],{},[119,174,175],{},"Why not summarise each video separately."," A single-video summary has limited value. The useful signal is ",[119,178,179],{},"cross-video",": across ten videos, which claims repeat, and which ones disagree. That only works when the text is collected in one place.",[112,182,183,186],{},[119,184,185],{},"Why keep timestamps."," A summary gives conclusions, but people need to verify them. With timestamps, an interesting paragraph links straight back to the source.",[107,188,190],{"id":189},"the-outcome","The outcome",[112,192,193],{},"The shape of the work changed: from \"how many videos do I have to watch today\" to \"read the digest, click back into whatever sounds interesting.\"",[195,196,197],"blockquote",{},[112,198,199],{},"We deliberately did not build \"automatic quality judgement\". It is not reliable yet, and shipping it would cost the user's trust. The pipeline does what it can actually do: transcribe, deduplicate, extract.",[107,201,203],{"id":202},"what-is-not-done","What is not done",[205,206,207,210,213],"ul",{},[134,208,209],{},"No incremental updates — it runs in batches",[134,211,212],{},"No cross-language translation; transcripts stay in the source language",[134,214,215],{},"Summary quality depends on prompting plus spot checks; there is no systematic evaluation yet",{"title":217,"searchDepth":218,"depth":218,"links":219},"",2,[220,221,222,223,224],{"id":109,"depth":218,"text":110},{"id":125,"depth":218,"text":126},{"id":160,"depth":218,"text":161},{"id":189,"depth":218,"text":190},{"id":202,"depth":218,"text":203},"Content research team (client not disclosed)","\u002Fimages\u002Fwork\u002Fvideo-transcript-pipeline-en.svg","Video to transcript to summary pipeline","Short-form video grows faster than anyone can watch. We built an on-premise pipeline that filters by topic, downloads, transcribes with WhisperX, and summarises across videos.",false,"md","Media & content","en",{},true,1,"\u002Fwork\u002Fen\u002Fvideo-transcript-pipeline",[238,241,244],{"label":239,"value":240},"Transcription","On-premise",{"label":242,"value":243},"Model","WhisperX",{"label":245,"value":246},"Output","Transcript + digest",{"title":102,"description":228},[249,250],"automation","edge","video-transcript-pipeline","work\u002Fen\u002Fvideo-transcript-pipeline",null,"KjrCe6cdhRIPO9A5wVdtfRr3XDCYpq_sPAku9LIyCKE",{"id":256,"title":257,"body":258,"client":364,"cover":365,"coverAlt":366,"description":367,"draft":229,"extension":230,"industry":368,"lang":232,"meta":369,"navigation":234,"order":218,"path":370,"results":371,"seo":379,"services":380,"slug":381,"stem":382,"translationKey":253,"year":253,"__hash__":383},"work\u002Fwork\u002Fen\u002Freceipt-invoice-ocr.md","Reading Taiwanese receipts straight into the database",{"type":104,"value":259,"toc":352},[260,262,265,268,275,277,282,285,289,292,299,303,306,314,318,321,323,334,339,341],[107,261,110],{"id":109},[112,263,264],{},"A receipt in Taiwan is not one thing.",[112,266,267],{},"E-invoices have QR codes and fixed fields. Traditional triplicate invoices are printed grids. Handwritten receipts are a different world entirely — handwriting, stamps, highlighter, creases, camera angle. Every one of those breaks generic OCR.",[112,269,270,271,274],{},"The failure mode is the nasty kind: ",[119,272,273],{},"it does not know it is wrong",". A missing line item or an extra zero in the tax field looks perfectly normal until month-end reconciliation.",[107,276,126],{"id":125},[278,279,281],"h3",{"id":280},"_1-fix-the-image-before-talking-about-models","1. Fix the image before talking about models",[112,283,284],{},"Pre-processing matters more than the model. Deskew, correct perspective, normalise contrast, crop to the document — so the model sees a clean, upright page. The gap in recognition quality after this step is large.",[278,286,288],{"id":287},"_2-extract-fields-with-a-local-vision-model","2. Extract fields with a local vision model",[112,290,291],{},"The model outputs structured fields directly: date, vendor, tax ID, line items, quantity, unit price, tax, total.",[112,293,294,295,298],{},"Running on-premise is not about API cost. ",[119,296,297],{},"Receipts contain a customer's purchase details",", and that is not data you hand to a third party casually.",[278,300,302],{"id":301},"_3-write-to-the-database-not-to-a-spreadsheet","3. Write to the database, not to a spreadsheet",[112,304,305],{},"The output does not become an Excel file that someone re-types later. It goes through an automated write step that puts the values into the right fields — the accountant opens the system and the data is already there.",[112,307,308,309,313],{},"Under the hood, that step is a capability (a ",[310,311,312],"em",{},"skill",") we wrote for the client's AI agent: it validates the tax ID and date, then writes into the matching tables. From the user's side it is simply \"no more manual entry\".",[278,315,317],{"id":316},"_4-confidence-decides-who-reviews-what","4. Confidence decides who reviews what",[112,319,320],{},"Low-confidence fields are flagged. The accountant only looks at those; the rest flow through.",[107,322,190],{"id":189},[112,324,325,326,329,330,333],{},"The human role changes from ",[119,327,328],{},"data entry"," to ",[119,331,332],{},"review",". That is not only faster — it means errors are actually seen, instead of surfacing at reconciliation.",[195,335,336],{},[112,337,338],{},"We do not claim \"fully automatic\". The reality of receipt recognition in Taiwan is that a small fraction will always need a human. Designing for \"the human reviews that fraction\" is what makes it operable.",[107,340,203],{"id":202},[205,342,343,346,349],{},[134,344,345],{},"No dedicated model for handwritten amounts; receipts with heavy handwriting remain manual",[134,347,348],{},"No multi-page stitching (one receipt photographed across two images)",[134,350,351],{},"Accuracy numbers have not been measured systematically, so we do not publish them",{"title":217,"searchDepth":218,"depth":218,"links":353},[354,355,362,363],{"id":109,"depth":218,"text":110},{"id":125,"depth":218,"text":126,"children":356},[357,359,360,361],{"id":280,"depth":358,"text":281},3,{"id":287,"depth":358,"text":288},{"id":301,"depth":358,"text":302},{"id":316,"depth":358,"text":317},{"id":189,"depth":218,"text":190},{"id":202,"depth":218,"text":203},"Accounting firm (client not disclosed)","\u002Fimages\u002Fwork\u002Freceipt-invoice-ocr-en.svg","Receipt and invoice recognition","Receipts in Taiwan come in too many shapes for generic OCR to handle. We run a vision model on-premise, extract structured fields, and write them straight into the database.","Accounting operations",{},"\u002Fwork\u002Fen\u002Freceipt-invoice-ocr",[372,374,376],{"label":373,"value":240},"Inference",{"label":242,"value":375},"Qwen (vision)",{"label":377,"value":378},"Human role","Review, not retype",{"title":257,"description":367},[249,250],"receipt-invoice-ocr","work\u002Fen\u002Freceipt-invoice-ocr","TsaAOnPGgZchfoD1ssogv4-xrz96YyKqdO4NspGRSug",{"id":385,"title":386,"body":387,"client":475,"cover":476,"coverAlt":477,"description":478,"draft":229,"extension":230,"industry":479,"lang":232,"meta":480,"navigation":234,"order":358,"path":481,"results":482,"seo":492,"services":493,"slug":494,"stem":495,"translationKey":253,"year":253,"__hash__":496},"work\u002Fwork\u002Fen\u002Fembedded-vision-background.md","Before the company — twenty years of embedded vision",{"type":104,"value":388,"toc":468},[389,393,400,403,407,421,427,431,437,443,449,453,460,463,465],[107,390,392],{"id":391},"why-this-page-exists","Why this page exists",[112,394,395,396,399],{},"People assume an \"AI consultant\" is someone who picks models and writes prompts. But projects rarely fail because the model is not strong enough. They fail because ",[119,397,398],{},"nobody handled the other 90%",": the device cannot run it, the lighting changes and it breaks, the data format does not match, and after go-live nobody maintains it.",[112,401,402],{},"That is where our instincts came from.",[107,404,406],{"id":405},"the-conditions-we-worked-in","The conditions we worked in",[205,408,409,412,415,418],{},[134,410,411],{},"No GPU — inference had to fit inside a CPU budget",[134,413,414],{},"Memory measured in hundreds of megabytes; \"just load the model\" was not an option",[134,416,417],{},"Devices in the field: vibrating, hot, occasionally losing power",[134,419,420],{},"Nobody watching the logs; when it is wrong, the line stops",[112,422,423,424],{},"In that environment, \"95% accuracy\" is not the finish line. ",[119,425,426],{},"Running for three months without an incident is.",[107,428,430],{"id":429},"three-habits-it-left-behind","Three habits it left behind",[112,432,433,436],{},[119,434,435],{},"Measure before you optimise."," On an edge device, optimising by intuition usually optimises the wrong thing. Find the real bottleneck first — pre-processing, the model, or I\u002FO.",[112,438,439,442],{},[119,440,441],{},"Design for failure."," Nobody on site reads exceptions. When confidence is low, the system needs a safe fallback, not a stalled pipeline.",[112,444,445,448],{},[119,446,447],{},"Deliver something that can be taken over."," Code, parameters, data formats, how to retrain. If the client cannot maintain it, the project never really ended.",[107,450,452],{"id":451},"how-it-connects-to-the-work-we-do-now","How it connects to the work we do now",[112,454,455,456,459],{},"LLM projects face the same shape of problem: the model is one piece. ",[119,457,458],{},"Where the data comes from, what happens when it is wrong, and who maintains it"," are what decide the outcome.",[112,461,462],{},"That is why we insist on audit-before-PoC: confirm the problem is real, the data exists, and someone is willing to change the process — before talking about models.",[107,464,203],{"id":202},[112,466,467],{},"This page does not yet include specific project details (clients, hardware, measured numbers). Those need client consent to publish. If you have a project with similar constraints, let's talk.",{"title":217,"searchDepth":218,"depth":218,"links":469},[470,471,472,473,474],{"id":391,"depth":218,"text":392},{"id":405,"depth":218,"text":406},{"id":429,"depth":218,"text":430},{"id":451,"depth":218,"text":452},{"id":202,"depth":218,"text":203},"Team background","\u002Fimages\u002Fwork\u002Fembedded-vision-background-en.svg","Embedded vision on constrained hardware","Before Jecision existed, the team built real-time vision on constrained devices — no GPU, a few hundred MB of RAM, and real light and vibration. That background shapes how we run AI projects today.","Embedded systems",{},"\u002Fwork\u002Fen\u002Fembedded-vision-background",[483,486,489],{"label":484,"value":485},"Experience","20+ years",{"label":487,"value":488},"Typical setting","Constrained devices",{"label":490,"value":491},"Strength","Getting models into production",{"title":386,"description":478},[250],"embedded-vision-background","work\u002Fen\u002Fembedded-vision-background","xa8RvuMExuycOr-qLpI4bUHzUxNo3uTEqUoigg8FYlw",1792775651149]