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радови","文件夹","\u002Fportfolio","i-lucide-briefcase",114,[148,161,175,191,206],{"id":149,"title":150,"url":144,"target":61,"icon":159,"isActive":14,"type":63,"productId":10,"categoryId":10,"shopCategoryId":10,"articleId":10,"pageId":146,"portfolioId":10,"children":160},"item-24",{"de":151,"en":152,"es":153,"fr":154,"it":155,"ru":156,"sr":157,"zh":158},"Alle Projekte","All Projects","Todos los proyectos","Tous les projets","Tutti i progetti","Все проекты","Сви пројекти","所有项目","i-lucide-grid-3x3",[],{"id":162,"title":163,"url":171,"target":61,"icon":172,"isActive":14,"type":173,"productId":10,"categoryId":10,"shopCategoryId":10,"articleId":10,"pageId":10,"portfolioId":10,"children":174},"item-29",{"de":164,"en":165,"es":166,"fr":167,"it":168,"ru":169,"sr":170,"zh":143},"Local Roots, Global Reach","Local Roots - Global Reach","Empresa local ","Entreprise locale","Azienda locale","Местная компания","Локално предузеће глобално тржиште","\u002Fportfolio\u002Flocal-roots-global-reach-communication-media-systems-for-modern-business","i-lucide-globe-2","custom",[],{"id":176,"title":177,"url":186,"target":61,"icon":187,"isActive":14,"type":188,"productId":10,"categoryId":10,"shopCategoryId":10,"articleId":10,"pageId":10,"portfolioId":189,"children":190},"menu-2916d060-8681-482a-af9a-c107a8bc30d8",{"de":178,"en":179,"es":180,"fr":181,"it":182,"ru":183,"sr":184,"zh":185},"Von Rohdaten zu verifizierbarem KI-Kontext: Eine praktische Architektur für agentenbereite Informationen","From Raw Data to Verifiable AI Context:","De datos sin procesar a contexto de IA verificable: una arquitectura práctica para información lista para agentes","Des données brutes au contexte d'IA vérifiable : une architecture pratique pour des informations prêtes pour les agents","Dai dati grezzi a un contesto IA verificabile: Un'architettura pratica per informazioni pronte per gli agenti","От необработанных данных к верифицируемому контексту ИИ: практическая архитектура информации, готовой для агентов","Od sirovih podataka do proverljivog AI konteksta: Praktična arhitektura za informacije spremne za agente","从原始数据到可验证的 AI 上下文：面向智能体就绪信息的实用架构","\u002Fportfolio\u002Ffrom-raw-data-to-verifiable-ai-context-a-practical-architecture-for-agent-ready-information","i-lucide-database","portfolio",22,[],{"id":192,"title":193,"url":202,"target":61,"icon":203,"isActive":14,"type":188,"productId":10,"categoryId":10,"shopCategoryId":10,"articleId":10,"pageId":10,"portfolioId":204,"children":205},"menu-e2061cd7-ff53-49d5-a0e8-3d49090577c0",{"de":194,"en":195,"es":196,"fr":197,"it":198,"ru":199,"sr":200,"zh":201},"Präsenzplattformen — Unternehmenskommunikation","Presence Platforms — Enterprise Communication","Plataformas de presencia — Comunicación empresarial","Plateformes de présence — Communication d'entreprise","Presence Platforms — Comunicazione aziendale","Платформы присутствия — Корпоративные коммуникации","Presence platforme — Poslovna komunikacija","Presence 平台 — 企业通信","\u002Fportfolio\u002Fpresence-platforms-enterprise-communication-that-wins-without-competing","i-lucide-badge-percent",21,[],{"id":207,"title":208,"url":217,"target":61,"icon":218,"isActive":14,"type":188,"productId":10,"categoryId":10,"shopCategoryId":10,"articleId":10,"pageId":10,"portfolioId":219,"children":220},"menu-a781582e-e90d-418e-b8c9-b070bc37c37e",{"de":209,"en":210,"es":211,"fr":212,"it":213,"ru":214,"sr":215,"zh":216},"Maximierung des POS-ROI durch Konsistenz","Maximizing POS ROI Through Consistent","Maximizar el ROI del POS a través de constante","Maximiser le ROI du PDV grâce à la cohérence","Massimizzare il ROI del POS attraverso costante","Максимизация ROI POS за счет последовательного","Maksimizacija POS ROI-ja kroz dosledno","通过持续最大化 POS 投资回报率","\u002Fportfolio\u002Ffrom-vision-to-value-maximizing-pos-roi-through-consistent-ci-creation-and-strategic-branding","i-lucide-briefcase-business",11,[],{"statusCode":4,"data":222,"message":597},{"id":223,"title":184,"slug":224,"content":225,"contentJson":226,"excerpt":367,"featuredImage":368,"featuredImageAlt":369,"featuredImageCaption":10,"featuredImageTitle":10,"featuredImageCopyright":10,"featuredImageAuthor":10,"featuredImageSourceUrl":10,"featuredImageLicense":10,"featuredImageIsAiGenerated":43,"status":370,"publishedAt":371,"createdAt":372,"updatedAt":373,"seoLocalePaths":374,"author":382,"translations":387},"22","from-raw-data-to-verifiable-ai-context-a-practical-architecture-for-agent-ready-information","\u003Cnav class=\"editorjs-toc\" data-editorjs-toc=\"true\" aria-label=\"Sadržaj\">\u003Cstrong class=\"editorjs-toc__title\">Sadržaj\u003C\u002Fstrong>\u003Col class=\"editorjs-toc__list editorjs-toc__list--depth-0\">\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-5\" class=\"editorjs-toc__link\">Šta to zapravo znači\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-8\" class=\"editorjs-toc__link\">Najjednostavniji primer\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-11\" class=\"editorjs-toc__link\">Gde primer prestaje da funkcioniše\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-13\" class=\"editorjs-toc__link\">Informacioni objekat koji se može ponovo koristiti\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-15\" class=\"editorjs-toc__link\">Zašto RAG nije prvi korak\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-19\" class=\"editorjs-toc__link\">Spremno za agente ne znači kontrolisano od strane agenata\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-21\" class=\"editorjs-toc__link\">Zašto je ovo važno van jedne oblasti\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-23\" class=\"editorjs-toc__link\">Šta bi promenilo ovu arhitekturu?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-25\" class=\"editorjs-toc__link\">Zaključak\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fnav>\n\u003Cp>Većina AI projekata počinje previše kasno u procesu. Počinju sa modelom, upitom ili slojem za pronalaženje podataka, a tek kasnije otkriju da su osnovni podaci nedosledni, duplirani, zastareli, privatni ili nemoguće ih je pratiti do pouzdanog izvora.\u003C\u002Fp>\n\u003Cp>Trajnija arhitektura počinje pre pronalaženja podataka. Prvi zadatak je odlučiti šta je informacioni objekat zapravo, odakle dolazi, kojoj identitetskoj celini pripada, da li mu neki drugi izvor protivreči i pod kojim uslovima je još uvek važeći.\u003C\u002Fp>\n\u003Caside class=\"editorjs-callout editorjs-callout--info my-6 rounded-xl border p-5 border-blue-300 bg-blue-50 dark:border-blue-900 dark:bg-blue-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Kontekst portfolija\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Ovaj članak dokumentuje arhitektonski obrazac koji se istražuje kroz multi-tenant sisteme za sadržaj, trgovinu i AI. To je tehnički koncept i smer prototipa, a ne tvrdnja da je svaka komponenta već dostupna kao produkciona usluga.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-5\">Šta to zapravo znači\u003C\u002Fh2>\n\u003Cp>Cilj nije stvoriti još jednu bazu znanja punu teksta. Cilj je stvoriti informacije koje ostaju korisne kada potrošač više nije čovek koji pregleda veb-sajt, već aplikacija ili AI agent koji donosi odluku.\u003C\u002Fp>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Od heterogenih podataka do kontrolisanog konteksta\u003C\u002Fh3>\u003Cdiv class=\"flex flex-col gap-3\">\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Ulaz\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Podaci kompanije, dokumenti i odobreni referentni podaci ulaze u sistem.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__arrow shrink-0 self-center text-xl text-gray-400 rotate-90\" aria-hidden=\"true\">→\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Identitet\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Zapisi se mapiraju na entitet koji zapravo opisuju.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__arrow shrink-0 self-center text-xl text-gray-400 rotate-90\" aria-hidden=\"true\">→\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Normalizacija\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Ekvivalentna polja, jedinice i strukture se usklađuju bez izmišljanja nedostajućih vrednosti.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__arrow shrink-0 self-center text-xl text-gray-400 rotate-90\" aria-hidden=\"true\">→\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Dokazi\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Svaka upotrebljiva tvrdnja zadržava svoj izvor, status i poreklo.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__arrow shrink-0 self-center text-xl text-gray-400 rotate-90\" aria-hidden=\"true\">→\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">5\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Otkrivanje konflikata\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Protivrečne vrednosti ostaju vidljive umesto da budu tiho spojene.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__arrow shrink-0 self-center text-xl text-gray-400 rotate-90\" aria-hidden=\"true\">→\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">6\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Verzionisanje\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Sistem beleži kada je tvrdnja uočena i gde se primenjuje.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__arrow shrink-0 self-center text-xl text-gray-400 rotate-90\" aria-hidden=\"true\">→\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">7\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Isporuka\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Rezultujući informacioni objekat može koristiti portal, API, interni sistem ili agent.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-8\">Najjednostavniji primer\u003C\u002Fh2>\n\u003Cp>Zamislite tri izvora podataka koji opisuju isti uređaj. Jedan dobavljač kaže da ima 32 GB memorije, stariji PDF kaže 16 GB, a proizvođačev feed kaže 32 GB za hardversku reviziju 4. Uobičajeni proces obogaćivanja podataka može izabrati jednu vrednost i prepisati ostale. Proverljiv proces zadržava neslaganje vidljivim dok se identitet i revizija ne razreše.\u003C\u002Fp>\n\u003Cp>Rezultat nije jednostavno \u003Cstrong>memorija = 32 GB\u003C\u002Fstrong>. Bliži je ovome: vrednost 32 GB, izvor proizvođačev feed, revizija 4, tržište EU, uočeno u poznatom trenutku, prisutan stariji izvor koji protivreči, trenutni status verifikovan za razrešenu reviziju.\u003C\u002Fp>\n\u003Ch2 id=\"section-11\">Gde primer prestaje da funkcioniše\u003C\u002Fh2>\n\u003Cp>Stvarni poslovni podaci retko su ovako čisti. Nazivi proizvoda se menjaju, identifikatori nedostaju, PDF-ovi koriste drugačiju terminologiju, polja menjaju značenje između tržišta, a privatni podaci jednog zakupca ne smeju da procure u kontekst drugog zakupca. Zato sama normalizacija nije dovoljna.\u003C\u002Fp>\n\u003Ch2 id=\"section-13\">Informacioni objekat koji se može ponovo koristiti\u003C\u002Fh2>\n\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Polje\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Svrha\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Identitet\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Kojem entitetu ili verziji pripada ova tvrdnja?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Vrednost\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Šta se zapravo tvrdi?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Izvor\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Odakle dolazi?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Status\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Verifikovani izvor, tvrdnja trgovca, korisnički unos, izvedeno, zaključivanje, nepoznato ili protivrečno\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Uočeno u\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Kada je uočeno?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Važenje\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Gde, kada i za koju verziju se primenjuje?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Prava\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Sme li se ovaj izvor čuvati, ponovo koristiti ili distribuirati?\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Zakupac\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Koji klijent ili vlasnik podataka sme da ga koristi?\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Ch2 id=\"section-15\">Zašto RAG nije prvi korak\u003C\u002Fh2>\n\u003Cp>Pronalaženje podataka može pronaći samo ono što je indeksirano. Ako se protivrečni, zastareli ili pravno ograničeni podaci indeksiraju bez upravljanja, RAG može učiniti da odgovor izgleda bolje, dok osnovna odluka postaje manje pouzdana.\u003C\u002Fp>\n\u003Cp>Sigurniji redosled je: provera prava → klasifikacija izvora → razrešavanje identiteta → izdvajanje tvrdnji → poreklo → otkrivanje konflikata → skladište dokaza → pronalaženje podataka → zaključivanje.\u003C\u002Fp>\n\u003Caside class=\"editorjs-callout editorjs-callout--info my-6 rounded-xl border p-5 border-blue-300 bg-blue-50 dark:border-blue-900 dark:bg-blue-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Osnovno pravilo\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">Bez dokaza znači nepoznato. Protivrečni dokazi znače konflikt. AI zaključivanje ne sme tiho postati činjenica.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-19\">Spremno za agente ne znači kontrolisano od strane agenata\u003C\u002Fh2>\n\u003Cp>Agent treba da dobije kontrolisani kontekst, a ne neograničen autoritet. Model može da rasuđuje na osnovu dostupnih informacija, ali ne treba da mu bude dozvoljeno da izmišlja nedostajuće činjenice o proizvodu, ublažava obavezna ograničenja, zaobilazi pravila o pravima izvora ili pretvara nesigurnost u sigurnost.\u003C\u002Fp>\n\u003Ch2 id=\"section-21\">Zašto je ovo važno van jedne oblasti\u003C\u002Fh2>\n\u003Cp>Ista arhitektura se primenjuje na tehničke proizvode, planiranje događaja, softversku dokumentaciju, kataloge dobavljača, interno znanje i trgovinu. Pravila oblasti se menjaju, ali osnovni problem ostaje: identitet, poreklo, konflikti, validnost i kontrolisana ponovna upotreba.\u003C\u002Fp>\n\u003Ch2 id=\"section-23\">Šta bi promenilo ovu arhitekturu?\u003C\u002Fh2>\n\u003Cp>Arhitektura bi trebalo da postane jednostavnija kada izvorni sistem već obezbeđuje kanonske identifikatore, strogo verzionisanje i ugovorni kvalitet podataka. Trebalo bi da postane stroža kada odluke utiču na regulisane proizvode, bezbednost, privatne podatke ili pravno osetljive ishode.\u003C\u002Fp>\n\u003Ch2 id=\"section-25\">Zaključak\u003C\u002Fh2>\n\u003Cp>Korisna jedinica za buduće AI sisteme nije samo dokument ili deo teksta. To je proverljiv informacioni objekat sa identitetom, poreklom, statusom, konfliktima i granicom validnosti. To je sloj koji omogućava portalima, poslovnim sistemima i agentima da ponovo koriste isto znanje bez pretvaranja da je nesiguran podatak siguran.\u003C\u002Fp>",{"time":227,"blocks":228,"version":366},1790741969310,[229,235,239,242,248,251,254,281,284,287,290,293,296,299,329,332,335,338,342,345,348,351,354,357,360,363],{"data":230,"type":234},{"title":231,"maxLevel":232,"minLevel":233},"Sadržaj",3,2,"tableOfContents",{"data":236,"type":238},{"text":237},"Većina AI projekata počinje previše kasno u procesu. Počinju sa modelom, upitom ili slojem za pronalaženje podataka, a tek kasnije otkriju da su osnovni podaci nedosledni, duplirani, zastareli, privatni ili nemoguće ih je pratiti do pouzdanog izvora.","paragraph",{"data":240,"type":238},{"text":241},"Trajnija arhitektura počinje pre pronalaženja podataka. Prvi zadatak je odlučiti šta je informacioni objekat zapravo, odakle dolazi, kojoj identitetskoj celini pripada, da li mu neki drugi izvor protivreči i pod kojim uslovima je još uvek važeći.",{"data":243,"type":247},{"body":244,"title":245,"variant":246},"Ovaj članak dokumentuje arhitektonski obrazac koji se istražuje kroz multi-tenant sisteme za sadržaj, trgovinu i AI. To je tehnički koncept i smer prototipa, a ne tvrdnja da je svaka komponenta već dostupna kao produkciona usluga.","Kontekst portfolija","info","callout",{"data":249,"type":42},{"text":250,"level":233},"Šta to zapravo znači",{"data":252,"type":238},{"text":253},"Cilj nije stvoriti još jednu bazu znanja punu teksta. Cilj je stvoriti informacije koje ostaju korisne kada potrošač više nije čovek koji pregleda veb-sajt, već aplikacija ili AI agent koji donosi odluku.",{"data":255,"type":280},{"steps":256,"title":278,"orientation":279},[257,260,263,266,269,272,275],{"label":258,"description":259},"Ulaz","Podaci kompanije, dokumenti i odobreni referentni podaci ulaze u sistem.",{"label":261,"description":262},"Identitet","Zapisi se mapiraju na entitet koji zapravo opisuju.",{"label":264,"description":265},"Normalizacija","Ekvivalentna polja, jedinice i strukture se usklađuju bez izmišljanja nedostajućih vrednosti.",{"label":267,"description":268},"Dokazi","Svaka upotrebljiva tvrdnja zadržava svoj izvor, status i poreklo.",{"label":270,"description":271},"Otkrivanje konflikata","Protivrečne vrednosti ostaju vidljive umesto da budu tiho spojene.",{"label":273,"description":274},"Verzionisanje","Sistem beleži kada je tvrdnja uočena i gde se primenjuje.",{"label":276,"description":277},"Isporuka","Rezultujući informacioni objekat može koristiti portal, API, interni sistem ili agent.","Od heterogenih podataka do kontrolisanog konteksta","vertical","processFlow",{"data":282,"type":42},{"text":283,"level":233},"Najjednostavniji primer",{"data":285,"type":238},{"text":286},"Zamislite tri izvora podataka koji opisuju isti uređaj. Jedan dobavljač kaže da ima 32 GB memorije, stariji PDF kaže 16 GB, a proizvođačev feed kaže 32 GB za hardversku reviziju 4. Uobičajeni proces obogaćivanja podataka može izabrati jednu vrednost i prepisati ostale. Proverljiv proces zadržava neslaganje vidljivim dok se identitet i revizija ne razreše.",{"data":288,"type":238},{"text":289},"Rezultat nije jednostavno \u003Cstrong>memorija = 32 GB\u003C\u002Fstrong>. Bliži je ovome: vrednost 32 GB, izvor proizvođačev feed, revizija 4, tržište EU, uočeno u poznatom trenutku, prisutan stariji izvor koji protivreči, trenutni status verifikovan za razrešenu reviziju.",{"data":291,"type":42},{"text":292,"level":233},"Gde primer prestaje da funkcioniše",{"data":294,"type":238},{"text":295},"Stvarni poslovni podaci retko su ovako čisti. Nazivi proizvoda se menjaju, identifikatori nedostaju, PDF-ovi koriste drugačiju terminologiju, polja menjaju značenje između tržišta, a privatni podaci jednog zakupca ne smeju da procure u kontekst drugog zakupca. Zato sama normalizacija nije dovoljna.",{"data":297,"type":42},{"text":298,"level":233},"Informacioni objekat koji se može ponovo koristiti",{"data":300,"type":328},{"content":301,"stretched":14,"withHeadings":14},[302,305,307,310,313,316,319,322,325],[303,304],"Polje","Svrha",[261,306],"Kojem entitetu ili verziji pripada ova tvrdnja?",[308,309],"Vrednost","Šta se zapravo tvrdi?",[311,312],"Izvor","Odakle dolazi?",[314,315],"Status","Verifikovani izvor, tvrdnja trgovca, korisnički unos, izvedeno, zaključivanje, nepoznato ili protivrečno",[317,318],"Uočeno u","Kada je uočeno?",[320,321],"Važenje","Gde, kada i za koju verziju se primenjuje?",[323,324],"Prava","Sme li se ovaj izvor čuvati, ponovo koristiti ili distribuirati?",[326,327],"Zakupac","Koji klijent ili vlasnik podataka sme da ga koristi?","table",{"data":330,"type":42},{"text":331,"level":233},"Zašto RAG nije prvi korak",{"data":333,"type":238},{"text":334},"Pronalaženje podataka može pronaći samo ono što je indeksirano. Ako se protivrečni, zastareli ili pravno ograničeni podaci indeksiraju bez upravljanja, RAG može učiniti da odgovor izgleda bolje, dok osnovna odluka postaje manje pouzdana.",{"data":336,"type":238},{"text":337},"Sigurniji redosled je: provera prava → klasifikacija izvora → razrešavanje identiteta → izdvajanje tvrdnji → poreklo → otkrivanje konflikata → skladište dokaza → pronalaženje podataka → zaključivanje.",{"data":339,"type":247},{"body":340,"title":341,"variant":246},"Bez dokaza znači nepoznato. Protivrečni dokazi znače konflikt. AI zaključivanje ne sme tiho postati činjenica.","Osnovno pravilo",{"data":343,"type":42},{"text":344,"level":233},"Spremno za agente ne znači kontrolisano od strane agenata",{"data":346,"type":238},{"text":347},"Agent treba da dobije kontrolisani kontekst, a ne neograničen autoritet. Model može da rasuđuje na osnovu dostupnih informacija, ali ne treba da mu bude dozvoljeno da izmišlja nedostajuće činjenice o proizvodu, ublažava obavezna ograničenja, zaobilazi pravila o pravima izvora ili pretvara nesigurnost u sigurnost.",{"data":349,"type":42},{"text":350,"level":233},"Zašto je ovo važno van jedne oblasti",{"data":352,"type":238},{"text":353},"Ista arhitektura se primenjuje na tehničke proizvode, planiranje događaja, softversku dokumentaciju, kataloge dobavljača, interno znanje i trgovinu. Pravila oblasti se menjaju, ali osnovni problem ostaje: identitet, poreklo, konflikti, validnost i kontrolisana ponovna upotreba.",{"data":355,"type":42},{"text":356,"level":233},"Šta bi promenilo ovu arhitekturu?",{"data":358,"type":238},{"text":359},"Arhitektura bi trebalo da postane jednostavnija kada izvorni sistem već obezbeđuje kanonske identifikatore, strogo verzionisanje i ugovorni kvalitet podataka. Trebalo bi da postane stroža kada odluke utiču na regulisane proizvode, bezbednost, privatne podatke ili pravno osetljive ishode.",{"data":361,"type":42},{"text":362,"level":233},"Zaključak",{"data":364,"type":238},{"text":365},"Korisna jedinica za buduće AI sisteme nije samo dokument ili deo teksta. To je proverljiv informacioni objekat sa identitetom, poreklom, statusom, konfliktima i granicom validnosti. To je sloj koji omogućava portalima, poslovnim sistemima i agentima da ponovo koriste isto znanje bez pretvaranja da je nesiguran podatak siguran.","2.31","AI sistemi uglavnom ne zakažu zato što im nedostaje više teksta. Oni zakažu zato što identitet, poreklo, konflikti, nepoznanice i validnost često nedostaju u kontekstu koji dobijaju. Ovaj članak dokumentuje praktičnu arhitekturu za pretvaranje heterogenih poslovnih podataka u kontrolisane, višekratno upotrebljive informacione objekte za ljude, aplikacije i agente.","\u002Fuploads\u002F2026\u002F09\u002Ffrom-raw-data-to-verifiable-ai-context-a-practical-architecture-for-agent-ready-information-1790741906168-mo6sw7.webp","from-raw-data-to-verifiable-ai-context-a-practical-architecture-for-agent-ready-information-1790741906168-mo6sw7","PUBLISHED","2026-09-30T00:16:00.000Z","2026-09-30T04:16:21.250Z","2026-09-30T04:31:07.526Z",{"en":186,"de":375,"sr":376,"es":377,"fr":378,"it":379,"ru":380,"zh":381},"\u002Fde\u002Fportfolio\u002Ffrom-raw-data-to-verifiable-ai-context-a-practical-architecture-for-agent-ready-information","\u002Fsr\u002Fportfolio\u002Ffrom-raw-data-to-verifiable-ai-context-a-practical-architecture-for-agent-ready-information","\u002Fes\u002Fportfolio\u002Ffrom-raw-data-to-verifiable-ai-context-a-practical-architecture-for-agent-ready-information","\u002Ffr\u002Fportfolio\u002Ffrom-raw-data-to-verifiable-ai-context-a-practical-architecture-for-agent-ready-information","\u002Fit\u002Fportfolio\u002Ffrom-raw-data-to-verifiable-ai-context-a-practical-architecture-for-agent-ready-information","\u002Fru\u002Fportfolio\u002Ffrom-raw-data-to-verifiable-ai-context-a-practical-architecture-for-agent-ready-information","\u002Fzh\u002Fportfolio\u002Ffrom-raw-data-to-verifiable-ai-context-a-practical-architecture-for-agent-ready-information",{"id":383,"login":384,"email":385,"displayName":386},"20","rooth8233","","Aleksandar Stajić",[388,461],{"lang":7,"title":184,"content":225,"contentJson":389,"excerpt":367},{"time":227,"blocks":390,"version":366},[391,393,395,397,399,401,403,413,415,417,419,421,423,425,437,439,441,443,445,447,449,451,453,455,457,459],{"data":392,"type":234},{"title":231,"maxLevel":232,"minLevel":233},{"data":394,"type":238},{"text":237},{"data":396,"type":238},{"text":241},{"data":398,"type":247},{"body":244,"title":245,"variant":246},{"data":400,"type":42},{"text":250,"level":233},{"data":402,"type":238},{"text":253},{"data":404,"type":280},{"steps":405,"title":278,"orientation":279},[406,407,408,409,410,411,412],{"label":258,"description":259},{"label":261,"description":262},{"label":264,"description":265},{"label":267,"description":268},{"label":270,"description":271},{"label":273,"description":274},{"label":276,"description":277},{"data":414,"type":42},{"text":283,"level":233},{"data":416,"type":238},{"text":286},{"data":418,"type":238},{"text":289},{"data":420,"type":42},{"text":292,"level":233},{"data":422,"type":238},{"text":295},{"data":424,"type":42},{"text":298,"level":233},{"data":426,"type":328},{"content":427,"stretched":14,"withHeadings":14},[428,429,430,431,432,433,434,435,436],[303,304],[261,306],[308,309],[311,312],[314,315],[317,318],[320,321],[323,324],[326,327],{"data":438,"type":42},{"text":331,"level":233},{"data":440,"type":238},{"text":334},{"data":442,"type":238},{"text":337},{"data":444,"type":247},{"body":340,"title":341,"variant":246},{"data":446,"type":42},{"text":344,"level":233},{"data":448,"type":238},{"text":347},{"data":450,"type":42},{"text":350,"level":233},{"data":452,"type":238},{"text":353},{"data":454,"type":42},{"text":356,"level":233},{"data":456,"type":238},{"text":359},{"data":458,"type":42},{"text":362,"level":233},{"data":460,"type":238},{"text":365},{"lang":462,"title":463,"content":464,"contentJson":465,"excerpt":596},"en","From Raw Data to Verifiable AI Context: A Practical Architecture for Agent-Ready Information","{\"time\":1790741414448,\"blocks\":[{\"data\":{\"title\":\"Table of Contents\",\"maxLevel\":3,\"minLevel\":2},\"type\":\"tableOfContents\"},{\"data\":{\"text\":\"Most AI projects start too late in the pipeline. They begin with a model, a prompt, or a retrieval layer and only later discover that the underlying data is inconsistent, duplicated, outdated, private, or impossible to trace back to a reliable source.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"A more durable architecture starts before retrieval. The first task is to decide what an information object actually is, where it came from, which identity it belongs to, whether another source contradicts it, and under which conditions it is still valid.\"},\"type\":\"paragraph\"},{\"data\":{\"body\":\"This article documents an architecture pattern being explored across multi-tenant content, commerce and AI systems. It is a technical concept and prototype direction, not a claim that every component is already available as a production service.\",\"title\":\"Portfolio context\",\"variant\":\"info\"},\"type\":\"callout\"},{\"data\":{\"text\":\"What This Really Means\",\"level\":2},\"type\":\"header\"},{\"data\":{\"text\":\"The goal is not to create another knowledge base full of text. The goal is to create information that remains useful when the consumer is no longer a human browsing a website but an application or AI agent making a decision.\"},\"type\":\"paragraph\"},{\"data\":{\"steps\":[{\"label\":\"Input\",\"description\":\"Company data, documents and approved reference data enter the system.\"},{\"label\":\"Identity\",\"description\":\"Records are mapped to the entity they actually describe.\"},{\"label\":\"Normalization\",\"description\":\"Equivalent fields, units and structures are aligned without inventing missing values.\"},{\"label\":\"Evidence\",\"description\":\"Every usable claim keeps its source, status and provenance.\"},{\"label\":\"Conflict detection\",\"description\":\"Contradictory values remain visible instead of being silently merged.\"},{\"label\":\"Versioning\",\"description\":\"The system records when a claim was observed and where it applies.\"},{\"label\":\"Delivery\",\"description\":\"The resulting information object can be used by a portal, API, internal system or agent.\"}],\"title\":\"From heterogeneous data to controlled context\",\"orientation\":\"vertical\"},\"type\":\"processFlow\"},{\"data\":{\"text\":\"The Simplest Example\",\"level\":2},\"type\":\"header\"},{\"data\":{\"text\":\"Imagine three data sources describing the same device. One supplier says it has 32 GB of memory, an older PDF says 16 GB, and the manufacturer feed says 32 GB for hardware revision 4. A normal enrichment pipeline may choose one value and overwrite the others. A verifiable pipeline keeps the disagreement visible until identity and revision are resolved.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"The result is not simply \u003Cstrong>memory = 32 GB\u003C\u002Fstrong>. It is closer to: value 32 GB, source manufacturer feed, revision 4, market EU, observed at a known time, conflicting older source present, current status verified for the resolved revision.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"Where the Example Stops Working\",\"level\":2},\"type\":\"header\"},{\"data\":{\"text\":\"Real business data is rarely this clean. Product names drift, identifiers are missing, PDFs use different terminology, fields change meaning between markets, and private tenant data must not leak into another tenant's context. That is why normalization alone is not enough.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"A Reusable Information Object\",\"level\":2},\"type\":\"header\"},{\"data\":{\"content\":[[\"Field\",\"Purpose\"],[\"Identity\",\"What entity or version does this claim belong to?\"],[\"Value\",\"What is actually asserted?\"],[\"Source\",\"Where did it come from?\"],[\"Status\",\"Verified source, merchant claim, user-provided, derived, inference, unknown or conflicting\"],[\"Observed at\",\"When was it seen?\"],[\"Validity\",\"Where, when and for which version does it apply?\"],[\"Rights\",\"May this source be stored, reused or redistributed?\"],[\"Tenant\",\"Which customer or data owner may use it?\"]],\"stretched\":true,\"withHeadings\":true},\"type\":\"table\"},{\"data\":{\"text\":\"Why RAG Is Not the First Step\",\"level\":2},\"type\":\"header\"},{\"data\":{\"text\":\"Retrieval can only retrieve what has been indexed. If conflicting, stale or legally restricted data is indexed without governance, RAG can make the answer look better while making the underlying decision less reliable.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"A safer order is: rights gate → source classification → identity resolution → claim extraction → provenance → conflict detection → evidence store → retrieval → reasoning.\"},\"type\":\"paragraph\"},{\"data\":{\"body\":\"No evidence means unknown. Conflicting evidence means conflict. An AI inference must never silently become a fact.\",\"title\":\"Core rule\",\"variant\":\"info\"},\"type\":\"callout\"},{\"data\":{\"text\":\"Agent-Ready Does Not Mean Agent-Controlled\",\"level\":2},\"type\":\"header\"},{\"data\":{\"text\":\"An agent should receive controlled context, not unrestricted authority. The model may reason over the available information, but it should not be allowed to fabricate missing product facts, relax mandatory constraints, bypass source-rights rules or turn uncertainty into certainty.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"Why This Matters Beyond One Domain\",\"level\":2},\"type\":\"header\"},{\"data\":{\"text\":\"The same architecture applies to technical products, event planning, software documentation, supplier catalogs, internal knowledge and commerce. The domain rules change, but the core problem remains: identity, provenance, conflicts, validity and controlled reuse.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"What Would Change This Architecture?\",\"level\":2},\"type\":\"header\"},{\"data\":{\"text\":\"The architecture should become simpler when the source system already provides canonical identifiers, strict versioning and contractual data quality. It should become stricter when decisions affect regulated products, safety, private data or legally sensitive outcomes.\"},\"type\":\"paragraph\"},{\"data\":{\"text\":\"Conclusion\",\"level\":2},\"type\":\"header\"},{\"data\":{\"text\":\"The useful unit for future AI systems is not just a document or chunk of text. It is a verifiable information object with identity, provenance, status, conflicts and a validity boundary. That is the layer that allows portals, business systems and agents to reuse the same knowledge without pretending that uncertain data is certain.\"},\"type\":\"paragraph\"}],\"version\":\"2.31.0\"}",{"time":466,"blocks":467,"version":595},1790741414448,[468,471,474,477,481,484,487,512,515,518,521,524,527,530,558,561,564,567,571,574,577,580,583,586,589,592],{"data":469,"type":234},{"title":470,"maxLevel":232,"minLevel":233},"Table of Contents",{"data":472,"type":238},{"text":473},"Most AI projects start too late in the pipeline. They begin with a model, a prompt, or a retrieval layer and only later discover that the underlying data is inconsistent, duplicated, outdated, private, or impossible to trace back to a reliable source.",{"data":475,"type":238},{"text":476},"A more durable architecture starts before retrieval. The first task is to decide what an information object actually is, where it came from, which identity it belongs to, whether another source contradicts it, and under which conditions it is still valid.",{"data":478,"type":247},{"body":479,"title":480,"variant":246},"This article documents an architecture pattern being explored across multi-tenant content, commerce and AI systems. It is a technical concept and prototype direction, not a claim that every component is already available as a production service.","Portfolio context",{"data":482,"type":42},{"text":483,"level":233},"What This Really Means",{"data":485,"type":238},{"text":486},"The goal is not to create another knowledge base full of text. The goal is to create information that remains useful when the consumer is no longer a human browsing a website but an application or AI agent making a decision.",{"data":488,"type":280},{"steps":489,"title":511,"orientation":279},[490,493,496,499,502,505,508],{"label":491,"description":492},"Input","Company data, documents and approved reference data enter the system.",{"label":494,"description":495},"Identity","Records are mapped to the entity they actually describe.",{"label":497,"description":498},"Normalization","Equivalent fields, units and structures are aligned without inventing missing values.",{"label":500,"description":501},"Evidence","Every usable claim keeps its source, status and provenance.",{"label":503,"description":504},"Conflict detection","Contradictory values remain visible instead of being silently merged.",{"label":506,"description":507},"Versioning","The system records when a claim was observed and where it applies.",{"label":509,"description":510},"Delivery","The resulting information object can be used by a portal, API, internal system or agent.","From heterogeneous data to controlled context",{"data":513,"type":42},{"text":514,"level":233},"The Simplest Example",{"data":516,"type":238},{"text":517},"Imagine three data sources describing the same device. One supplier says it has 32 GB of memory, an older PDF says 16 GB, and the manufacturer feed says 32 GB for hardware revision 4. A normal enrichment pipeline may choose one value and overwrite the others. A verifiable pipeline keeps the disagreement visible until identity and revision are resolved.",{"data":519,"type":238},{"text":520},"The result is not simply \u003Cstrong>memory = 32 GB\u003C\u002Fstrong>. It is closer to: value 32 GB, source manufacturer feed, revision 4, market EU, observed at a known time, conflicting older source present, current status verified for the resolved revision.",{"data":522,"type":42},{"text":523,"level":233},"Where the Example Stops Working",{"data":525,"type":238},{"text":526},"Real business data is rarely this clean. Product names drift, identifiers are missing, PDFs use different terminology, fields change meaning between markets, and private tenant data must not leak into another tenant's context. That is why normalization alone is not enough.",{"data":528,"type":42},{"text":529,"level":233},"A Reusable Information Object",{"data":531,"type":328},{"content":532,"stretched":14,"withHeadings":14},[533,536,538,541,544,546,549,552,555],[534,535],"Field","Purpose",[494,537],"What entity or version does this claim belong to?",[539,540],"Value","What is actually asserted?",[542,543],"Source","Where did it come from?",[314,545],"Verified source, merchant claim, user-provided, derived, inference, unknown or conflicting",[547,548],"Observed at","When was it seen?",[550,551],"Validity","Where, when and for which version does it apply?",[553,554],"Rights","May this source be stored, reused or redistributed?",[556,557],"Tenant","Which customer or data owner may use it?",{"data":559,"type":42},{"text":560,"level":233},"Why RAG Is Not the First Step",{"data":562,"type":238},{"text":563},"Retrieval can only retrieve what has been indexed. If conflicting, stale or legally restricted data is indexed without governance, RAG can make the answer look better while making the underlying decision less reliable.",{"data":565,"type":238},{"text":566},"A safer order is: rights gate → source classification → identity resolution → claim extraction → provenance → conflict detection → evidence store → retrieval → reasoning.",{"data":568,"type":247},{"body":569,"title":570,"variant":246},"No evidence means unknown. Conflicting evidence means conflict. An AI inference must never silently become a fact.","Core rule",{"data":572,"type":42},{"text":573,"level":233},"Agent-Ready Does Not Mean Agent-Controlled",{"data":575,"type":238},{"text":576},"An agent should receive controlled context, not unrestricted authority. The model may reason over the available information, but it should not be allowed to fabricate missing product facts, relax mandatory constraints, bypass source-rights rules or turn uncertainty into certainty.",{"data":578,"type":42},{"text":579,"level":233},"Why This Matters Beyond One Domain",{"data":581,"type":238},{"text":582},"The same architecture applies to technical products, event planning, software documentation, supplier catalogs, internal knowledge and commerce. The domain rules change, but the core problem remains: identity, provenance, conflicts, validity and controlled reuse.",{"data":584,"type":42},{"text":585,"level":233},"What Would Change This Architecture?",{"data":587,"type":238},{"text":588},"The architecture should become simpler when the source system already provides canonical identifiers, strict versioning and contractual data quality. It should become stricter when decisions affect regulated products, safety, private data or legally sensitive outcomes.",{"data":590,"type":42},{"text":591,"level":233},"Conclusion",{"data":593,"type":238},{"text":594},"The useful unit for future AI systems is not just a document or chunk of text. It is a verifiable information object with identity, provenance, status, conflicts and a validity boundary. That is the layer that allows portals, business systems and agents to reuse the same knowledge without pretending that uncertain data is certain.","2.31.0","AI systems do not mainly fail because they lack more text. They fail because identity, provenance, conflicts, unknowns and validity are often missing from the context they receive. This article documents a practical architecture for turning heterogeneous business data into controlled, reusable information objects for humans, applications and agents.","Portfolio erfolgreich abgerufen",{"items":599,"source":682,"manualIds":683,"manualMatchedIds":684},[600,607,614,621,628,635,642,648,655,662,669,675],{"id":601,"slug":602,"title":603,"excerpt":604,"featuredImage":605,"publishedAt":606},"19","from-global-business-to-the-kitchen-a-reverse-communication-system-that-still-scales","Od globalnog poslovanja do kuhinje — sistem reverzne komunikacije koji i dalje skalira","Prestanite sa skaliranjem na osnovu buke. Počnite od globalnih signala, svedite ih na nekoliko stabilnih istina i pretvorite ih u nedeljne šablone, kontrolne liste i ponovljive “menije” koji isporučuju stvarnu vrednost.","\u002Fuploads\u002F2026\u002F02\u002Ffrom-global-business-to-the-kitchen-a-reverse-communication-system-that-still-scales-1771251820950-zhp2bz.webp","2026-02-16T10:13:00.000Z",{"id":608,"slug":609,"title":610,"excerpt":611,"featuredImage":612,"publishedAt":613},"1","nuxtwo-theme-showcase","Korporativna proizvodna platforma","Platforma za CMS i portale korporativnog nivoa sa višebaznom arhitekturom, pravom višejezičnošću i profesionalnom migracijom WordPress-a. 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Ova studija slučaja istražuje okvir za eliminisanje kognitivnog trenja radi poboljšanja ROI-ja.","\u002Fuploads\u002F2026\u002F02\u002Ffrom-vision-to-value-maximizing-pos-roi-through-consistent-ci-creation-and-strategic-branding-1771265752834-ha552f.webp","2025-10-30T08:36:00.000Z",{"id":663,"slug":664,"title":665,"excerpt":666,"featuredImage":667,"publishedAt":668},"13","mobile-app-creation-with-apache-cordova-cross-platform-case-study","Kreiranje mobilnih aplikacija sa Apache Cordova: Višeplatformska studija slučaja","Istražite kako Apache Cordova transformiše veb aplikacije u višeplatformska mobilna rešenja za Android, iOS i još mnogo toga, sa detaljnim procesom, tehnološkim skupom i prednostima.","\u002Fuploads\u002F2018\u002F01\u002FApache-Ubuntu-Install-Certbot_stajic_de_Suchmaschinen_Webcrawler_Suchergebnissen.webp","2025-10-30T12:36:00.000Z",{"id":670,"slug":671,"title":672,"excerpt":10,"featuredImage":673,"publishedAt":674},"16","automobile-bauer-joomla","Veb-sajt sa stavom - Automobile Bauer Joomla","\u002Fuploads\u002F2014\u002F09\u002F2019-11-21_145554_automobile-bauer.de_.webp","2025-10-30T14:36:08.121Z",{"id":676,"slug":677,"title":678,"excerpt":679,"featuredImage":680,"publishedAt":681},"15","solr-fuzzy-suggester-und-solr-infix-suggester-abfrage-ueber-ajax-und-filterung","Solr Fuzzy Suggester und Solr Infix Suggester über Ajax","За ефикасну претрагу на веб-сајту важно је да корисници брзо и једноставно пронађу одговарајуће резултате. Solr Fuzzy Suggester и Solr Infix Suggester нуде ефикасно рјешење за то. Они се могу позивати и филтрирати преко Ajax упита.","\u002Fuploads\u002F2019\u002F11\u002FSolr-Suggester-Fuzzy-infix.webp","2025-10-30T13:36:00.000Z","fallback",[],[]]