[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"post-where-ai-actually-fits-in-a-company":3,"$fXeC77ibo_YuG-ZnFG3hwyaNC83zFAKw-FvBNoL2JrIM":15},{"slug":4,"title":5,"excerpt":6,"category":7,"author":8,"readingTime":9,"coverImage":10,"bodyHtml":11,"metaTitle":12,"metaDescription":13,"date":14},"where-ai-actually-fits-in-a-company","Where AI actually fits in a company (and where it is just flashy)","Concrete applications across departments — what works today, what needs preparation, and what is still a demo rather than a tool.","For business","MightCore","8 min",null,"\u003Cp>Lists titled \"50 ways to use AI\" are useless, because they do not separate what works from what looks good on a conference slide. Let me try differently: we will walk the departments and, for each, say what is a settled thing today and what is not.\u003C\u002Fp>\n\n\u003Ch2>Paperwork and documents\u003C\u002Fh2>\n\u003Cp>This is the safest bet and it excites nobody. Invoices, delivery notes, contracts, customer orders arriving as PDFs. A model reads the document and pulls out the fields somebody has been retyping by hand.\u003C\u002Fp>\n\u003Cp>It works even on documents that look different every time — precisely where template-based approaches (OCR with fixed field positions) failed for years. You do not have to define where on the page the total sits. The model finds it.\u003C\u002Fp>\n\u003Cp>One condition: verify what it pulled. The line items must add up; the registration number must exist in your database. That is done in code, not by the model.\u003C\u002Fp>\n\n\u003Ch2>Customer support\u003C\u002Fh2>\n\u003Cp>The second safest thing, with an important distinction.\u003C\u002Fp>\n\u003Cp>What works: the model reads an incoming message, classifies it (complaint \u002F enquiry \u002F invoice \u002F noise), routes it to the right person, and drafts a reply. An agent reads it, edits it, sends it. That removes most of the time spent writing the same thing over and over.\u003C\u002Fp>\n\u003Cp>What works less well: a bot answering customers unsupervised. We can build one and sometimes it makes sense — but only when it has access to your real data (RAG over your documentation, a live order-status lookup) and a clear boundary at which it hands over to a person. A bot inventing your returns policy is a legal problem, not a saving.\u003C\u002Fp>\n\n\u003Ch2>Sales and CRM\u003C\u002Fh2>\n\u003Cp>Call notes into the CRM. Summarising a hundred-message email thread for the colleague taking over an account. Extracting next steps from a meeting transcript.\u003C\u002Fp>\n\u003Cp>These are small things nobody enjoys and everybody postpones. That is exactly why they pay off — not by saving hours, but by finally getting done.\u003C\u002Fp>\n\u003Cp>What not to believe: a deal-probability score computed by a language model. Better statistical methods exist, and more importantly you need enough historical data. Most companies do not have it.\u003C\u002Fp>\n\n\u003Ch2>Marketing and content\u003C\u002Fh2>\n\u003Cp>Product copy generated from specifications works well, especially when you have thousands of items that would otherwise get no copy at all. Translation into further languages too — bearing in mind it does not replace a native proofreader, it just makes one cheaper.\u003C\u002Fp>\n\u003Cp>Visuals are a real thing now. Product photography, variants for A\u002FB tests, a consistent AI model across a campaign. We do this and it works — on condition that it is labelled as AI content. Platforms increasingly require it, and it is basic honesty toward the customer.\u003C\u002Fp>\n\u003Cp>What not to believe: \"AI will write your whole blog\". It will. It will be average, it will sound like everything else, and Google will have no reason to prefer it. Writing needs somebody who genuinely knows the subject.\u003C\u002Fp>\n\n\u003Ch2>Software engineering\u003C\u002Fh2>\n\u003Cp>This is where the change is most visible. Writing tests, documentation, refactoring, explaining unfamiliar code, a first draft of a function. On routine work the speed-up is substantial.\u003C\u002Fp>\n\u003Cp>On non-standard work — architecture, performance problems, concurrency bugs — the benefit is far smaller and occasionally negative. The model writes something that looks right and you spend an hour discovering that it is not.\u003C\u002Fp>\n\n\u003Ch2>Operations and inventory\u003C\u002Fh2>\n\u003Cp>Demand forecasting, stock optimisation, route planning. It works, but note: this is usually not a job for a language model. These are classical statistical and optimisation problems, solved by a different kind of tool — often a simpler and cheaper one.\u003C\u002Fp>\n\u003Cp>If someone offers you an LLM for demand forecasting, ask why.\u003C\u002Fp>\n\n\u003Ch2>How to read all this\u003C\u002Fh2>\n\u003Cp>Notice what the working cases share: plenty of examples exist, a human checks the output, and nothing breaks when it occasionally fails.\u003C\u002Fp>\n\u003Cp>And what the problematic ones share: they decide instead of a person, they touch money or the law, and nobody reviews them.\u003C\u002Fp>\n\u003Cp>That is the whole filter. You do not need a list of fifty ideas — you need one that survives those three questions.\u003C\u002Fp>","AI applications in business — what actually works","A survey of real AI applications in a company: documents, support, sales, marketing, engineering and operations. What works today and what does not.","2026-05-06T00:00:00.000Z",[16,23,29,35,41,48,54,59,63,64,70,75,80,85,90,95,100,105,111,117,122,127,132,137,142,147,152,157,162,167,172,177,182,187,191,196,201],{"slug":17,"title":18,"excerpt":19,"category":20,"author":8,"readingTime":21,"coverImage":10,"date":22},"how-to-talk-to-a-language-model-prompting-in-practice","How to talk to a model: prompting without incantations","Prompt engineering is not a list of magic phrases. It is the ability to say exactly what you want — which is harder than it sounds.","Guides","7 min","2026-07-08T00:00:00.000Z",{"slug":24,"title":25,"excerpt":26,"category":27,"author":8,"readingTime":21,"coverImage":10,"date":28},"how-to-choose-a-language-model-and-control-costs","Choosing a language model and keeping costs under control","Benchmarks tell you almost nothing useful. What actually decides a model choice, and where the costs nobody predicted come from.","Engineering","2026-06-30T00:00:00.000Z",{"slug":30,"title":31,"excerpt":32,"category":27,"author":8,"readingTime":33,"coverImage":10,"date":34},"programming-in-the-age-of-ai-what-actually-changed","Programming in the age of AI: what actually changed","No, it did not replace developers. But it changed where they spend their time — and not all of those changes are comfortable.","9 min","2026-06-17T00:00:00.000Z",{"slug":36,"title":37,"excerpt":38,"category":39,"author":8,"readingTime":21,"coverImage":10,"date":40},"ai-modelka-pre-vas-brand-sprievodca","An AI model for your brand: the complete guide","From brief through avatar creation to the first campaign — step by step.","AI UGC","2026-06-16T00:00:00.000Z",{"slug":42,"title":43,"excerpt":44,"category":45,"author":8,"readingTime":46,"coverImage":10,"date":47},"pripadova-studia-cleago","Case study: Cleago — a platform built on context","How we designed and built a solution for Cleago (www.cleago.sk) by first understanding the context and only then coding.","Case studies","5 min","2026-06-02T00:00:00.000Z",{"slug":49,"title":50,"excerpt":51,"category":7,"author":8,"readingTime":52,"coverImage":10,"date":53},"where-not-to-use-ai-and-why-nobody-tells-you","Where not to use AI (and why nobody tells you)","A company selling AI has little incentive to talk about its limits. Let us fix that — these are the places AI simply does not belong.","6 min","2026-05-27T00:00:00.000Z",{"slug":55,"title":56,"excerpt":57,"category":45,"author":8,"readingTime":46,"coverImage":10,"date":58},"pripadova-studia-produktove-fotky","Case study: 80% less time spent creating product photos","A real example of deploying AI photos in an online store — from brief to results.","2026-05-19T00:00:00.000Z",{"slug":60,"title":61,"excerpt":62,"category":39,"author":8,"readingTime":46,"coverImage":10,"date":58},"ai-model-consistent-brand-across-campaigns","A consistent brand with an AI model across campaigns","An AI model can be a brand's steady face — if you handle consistency and transparency the right way. Here is how.",{"slug":4,"title":5,"excerpt":6,"category":7,"author":8,"readingTime":9,"coverImage":10,"date":14},{"slug":65,"title":66,"excerpt":67,"category":68,"author":8,"readingTime":52,"coverImage":10,"date":69},"rest-vs-graphql-pre-eshopy","REST vs. GraphQL API for modern online stores","When to choose which approach and what the impact on performance and development is.","Development","2026-04-21T00:00:00.000Z",{"slug":71,"title":72,"excerpt":73,"category":7,"author":8,"readingTime":33,"coverImage":10,"date":74},"how-to-bring-ai-into-your-company-first-steps","How to bring AI into your company without wasting the money","Most AI projects do not fail on technology. They fail because nobody said what was supposed to get better. Where to start instead.","2026-04-15T00:00:00.000Z",{"slug":76,"title":77,"excerpt":78,"category":45,"author":8,"readingTime":52,"coverImage":10,"date":79},"case-study-monolith-to-modular-migration","Case study: from a monolith to a modular architecture with no downtime","An illustrative example of gradually modernising an older application — where every change was risky and maintenance expensive.","2026-04-14T00:00:00.000Z",{"slug":81,"title":82,"excerpt":83,"category":20,"author":8,"readingTime":9,"coverImage":10,"date":84},"ai-agents-and-tool-calling-when-it-makes-sense","AI agents and tool calling: when it makes sense and when it does not","An agent is a model allowed to act. That is interesting and dangerous in equal measure. How it works, and where to be careful.","2026-03-25T00:00:00.000Z",{"slug":86,"title":87,"excerpt":88,"category":20,"author":8,"readingTime":52,"coverImage":10,"date":89},"context-driven-development-context-gathering-in-practice","Context Driven Development in practice: how gathering context changes the outcome","The most expensive mistakes come from a misunderstood brief. Here is what the context gathering that prevents them looks like.","2026-03-17T00:00:00.000Z",{"slug":91,"title":92,"excerpt":93,"category":94,"author":8,"readingTime":52,"coverImage":10,"date":89},"uctovnictvo-novej-generacie","Next-generation accounting: a platform built on context","A vision of an intelligent layer on top of existing accounting tools.","Accounting",{"slug":96,"title":97,"excerpt":98,"category":20,"author":8,"readingTime":33,"coverImage":10,"date":99},"how-to-orchestrate-language-models-in-practice","Orchestrating language models: from one prompt to a system","One prompt is a demo. An application is something else. On splitting work, routing between models, and where to leave ordinary code alone.","2026-03-04T00:00:00.000Z",{"slug":101,"title":102,"excerpt":103,"category":94,"author":8,"readingTime":46,"coverImage":10,"date":104},"digitalising-accounting-e-invoicing","Digitalising accounting: e-invoicing and what it brings","Electronic invoicing and reporting are becoming the standard. What it means for businesses and how to prepare without panic.","2026-02-17T00:00:00.000Z",{"slug":106,"title":107,"excerpt":108,"category":109,"author":8,"readingTime":9,"coverImage":10,"date":110},"what-is-an-llm-large-language-model-explained","What an LLM is: large language models without the mystique","How does a program that predicts the next word end up writing working code? A look at what actually happens inside a language model.","Fundamentals","2026-02-11T00:00:00.000Z",{"slug":112,"title":113,"excerpt":114,"category":115,"author":8,"readingTime":46,"coverImage":10,"date":116},"ako-ai-setri-naklady-na-video","How AI cuts the cost of video content production","Concrete numbers and a workflow for creating AI videos for online stores.","Marketing","2026-02-10T00:00:00.000Z",{"slug":118,"title":119,"excerpt":120,"category":109,"author":8,"readingTime":21,"coverImage":10,"date":121},"what-is-artificial-intelligence-explained-without-the-marketing","What artificial intelligence is (and what it is not)","The word AI now means everything, and therefore nothing. Here is what is actually behind it, and where technology ends and marketing begins.","2026-01-21T00:00:00.000Z",{"slug":123,"title":124,"excerpt":125,"category":68,"author":8,"readingTime":52,"coverImage":10,"date":126},"vector-databases-and-embeddings","Vector databases and embeddings: how machines grasp meaning","Semantic search sits behind many AI features. Here is what embeddings are and why modern data work rests on them.","2026-01-20T00:00:00.000Z",{"slug":128,"title":129,"excerpt":130,"category":68,"author":8,"readingTime":52,"coverImage":10,"date":131},"trendy-v-ai-vyvoji-2026","Trends in AI development for 2026","What awaits companies in the area of AI agents, automation and infrastructure.","2026-01-14T00:00:00.000Z",{"slug":133,"title":134,"excerpt":135,"category":45,"author":8,"readingTime":46,"coverImage":10,"date":136},"case-study-ai-product-photography-cosmetics","Case study: AI product photography for a cosmetics e-shop","An illustrative example of how AI content replaced repeated photoshoots and brought a consistent visual identity across seasons.","2025-12-09T00:00:00.000Z",{"slug":138,"title":139,"excerpt":140,"category":68,"author":8,"readingTime":52,"coverImage":10,"date":141},"integrating-ai-into-existing-systems","How to integrate AI into existing systems without a rewrite","You do not have to throw away working software to use AI. Here is an approach that adds value step by step, with low risk.","2025-11-18T00:00:00.000Z",{"slug":143,"title":144,"excerpt":145,"category":20,"author":8,"readingTime":21,"coverImage":10,"date":146},"gdpr-a-ai-obsah","GDPR and AI content: what to watch out for","The legal minimum for companies working with AI content and personal data.","2025-11-11T00:00:00.000Z",{"slug":148,"title":149,"excerpt":150,"category":39,"author":8,"readingTime":52,"coverImage":10,"date":151},"virtualne-ai-modelky","Virtual AI models: the future of advertising or a passing trend?","The possibilities, limits and ethics of virtual influencers for brands.","2025-10-20T00:00:00.000Z",{"slug":153,"title":154,"excerpt":155,"category":39,"author":8,"readingTime":46,"coverImage":10,"date":156},"ai-ugc-in-performance-marketing","AI UGC in performance marketing: what works and what applies","How to use AI content in Meta and TikTok campaigns, why creative testing matters, and what AI-labelling rules apply.","2025-10-14T00:00:00.000Z",{"slug":158,"title":159,"excerpt":160,"category":68,"author":8,"readingTime":52,"coverImage":10,"date":161},"llm-hallucinations-how-to-limit-them","LLM hallucinations and how to limit them in practice","Why AI sometimes states nonsense with confidence, and the techniques we use to keep output trustworthy.","2025-09-16T00:00:00.000Z",{"slug":163,"title":164,"excerpt":165,"category":39,"author":8,"readingTime":46,"coverImage":10,"date":166},"co-je-ai-ugc","What AI UGC is and why the whole world is talking about it","An introduction to AI-generated UGC and its impact on advertising and customer trust.","2025-09-15T00:00:00.000Z",{"slug":168,"title":169,"excerpt":170,"category":45,"author":8,"readingTime":52,"coverImage":10,"date":171},"case-study-b2b-eshop-faster-delivery","Case study: a B2B e-shop ready in weeks, not months","An illustrative example of how context gathering and AI execution shortened a wholesale e-shop build — without cutting quality.","2025-08-19T00:00:00.000Z",{"slug":173,"title":174,"excerpt":175,"category":115,"author":8,"readingTime":46,"coverImage":10,"date":176},"ai-v-marketingu-od-experimentu-k-vysledkom","AI in marketing: from experiment to real results","How to move from \"playing with AI\" to a measurable return on investment.","2025-08-06T00:00:00.000Z",{"slug":178,"title":179,"excerpt":180,"category":94,"author":8,"readingTime":46,"coverImage":10,"date":181},"ai-invoice-processing-in-accounting","AI invoice processing: from scan to posting","Intelligent document processing cuts the routine of retyping invoices. How it works and where AI has its limits.","2025-07-15T00:00:00.000Z",{"slug":183,"title":184,"excerpt":185,"category":20,"author":8,"readingTime":52,"coverImage":10,"date":186},"rag-why-context-decides-ai-quality","RAG: why context decides the quality of AI output","Retrieval-Augmented Generation connects a language model to your own data. Here is how it works and when to use it.","2025-06-18T00:00:00.000Z",{"slug":188,"title":189,"excerpt":190,"category":68,"author":8,"readingTime":52,"coverImage":10,"date":186},"shopsys-vs-vlastne-riesenie","ShopSys vs. a custom build: when a framework pays off","A decision framework for e-shop owners facing the choice of platform.",{"slug":192,"title":193,"excerpt":194,"category":115,"author":8,"readingTime":46,"coverImage":10,"date":195},"ako-ai-meni-ecommerce-na-slovensku","How AI is changing e-commerce","Practical examples of AI in product content, search and personalisation for online stores.","2025-05-21T00:00:00.000Z",{"slug":197,"title":198,"excerpt":199,"category":68,"author":8,"readingTime":21,"coverImage":10,"date":200},"context-driven-development-novy-pristup","Context Driven Development: a new approach to building software","An explanation of the CDD methodology from gathering context to deployment — step by step.","2025-04-09T00:00:00.000Z",{"slug":202,"title":203,"excerpt":204,"category":68,"author":8,"readingTime":52,"coverImage":10,"date":205},"koniec-ery-predrazeneho-vyvoja","Why the era of overpriced software development is over","How AI and a context-driven approach are changing the economics of building software — and why paying for inflated hours no longer makes sense.","2025-03-12T00:00:00.000Z"]