[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"post-where-not-to-use-ai-and-why-nobody-tells-you":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-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.","For business","MightCore","6 min",null,"\u003Cp>We are a company that builds AI systems. This article therefore runs against our short-term interest. But a client who concludes a year from now that somebody sold them nonsense is worse than a deal we never took.\u003C\u002Fp>\n\u003Cp>So: where AI does not belong.\u003C\u002Fp>\n\n\u003Ch2>Anywhere ordinary code will do\u003C\u002Fh2>\n\u003Cp>This is by far the most common case, and the most embarrassing.\u003C\u002Fp>\n\u003Cp>We once found a language model deciding whether an order qualified for free shipping. The rule was: over a hundred euros, yes. One condition in code. Instead there was an API call that cost money, took two seconds, occasionally returned the wrong answer, and could not be properly tested.\u003C\u002Fp>\n\u003Cp>If the rule can be written, write the rule. It is cheaper, faster, and it does not get things wrong. Reach for a model only where you cannot write the rule.\u003C\u002Fp>\n\n\u003Ch2>Exact arithmetic\u003C\u002Fh2>\n\u003Cp>A language model does not calculate. It predicts what the result should look like, which is a very different thing.\u003C\u002Fp>\n\u003Cp>With small numbers it usually lands, because it saw similar examples in training. With your numbers — fourteen line items, three VAT rates, a discount and rounding — it usually lands too. That \"usually\" is precisely the problem.\u003C\u002Fp>\n\u003Cp>Prices, tax, payroll, stock, accounting. All plain arithmetic in code. A model may at most read the numbers off a document — something else has to add them up.\u003C\u002Fp>\n\n\u003Ch2>Decisions with legal effect\u003C\u002Fh2>\n\u003Cp>Rejecting a warranty claim. Judging whether a customer breached the terms. Assessing an application. Anything ending in a decision that harms somebody who can then contest it.\u003C\u002Fp>\n\u003Cp>The problem is not only that the model might be wrong. The problem is that you cannot explain why. When a regulator or a customer asks on what basis you decided, \"the model rated it that way\" is not an answer. And for certain kinds of decisions GDPR requires you to provide human intervention — an automated decision with legal effect is not merely a technical question.\u003C\u002Fp>\n\u003Cp>A model can prepare the material here. A person who puts their name to it has to decide.\u003C\u002Fp>\n\n\u003Ch2>When you have no data\u003C\u002Fh2>\n\u003Cp>\"We'd like to predict which customers will churn.\" Good. How many historical churn cases do you have? \"Well… about thirty.\"\u003C\u002Fp>\n\u003Cp>Thirty examples is not a dataset. It is an anecdote. Whatever you build will learn noise and look convincing right up until you deploy it.\u003C\u002Fp>\n\u003Cp>This does not apply to language models, which arrive pre-trained — there you need no data of your own. It applies to anything meant to predict something specific to your business.\u003C\u002Fp>\n\n\u003Ch2>Sensitive data with no thought-out framework\u003C\u002Fh2>\n\u003Cp>Not a prohibition, a warning. When you send health records, payroll or client personal data to an external API, you need to know where it goes, how long it is kept, whether anyone trains on it, and whether you have a lawful basis.\u003C\u002Fp>\n\u003Cp>Those answers exist and are usually fine — providers offer modes where your data is not trained on. But somebody has to verify it \u003Cem>beforehand\u003C\u002Fem>, not after being asked.\u003C\u002Fp>\n\n\u003Ch2>When nobody checks the output\u003C\u002Fh2>\n\u003Cp>This is a summary of everything above.\u003C\u002Fp>\n\u003Cp>An AI feature nobody looks at is a time bomb. Not because the model is bad, but because you have no way to learn that something broke. Language model failures are quiet — the output looks equally good whether it is right or wrong. That is what separates them from ordinary software, which fails loudly.\u003C\u002Fp>\n\u003Cp>If \"who looks at this?\" does not have a name as an answer, the project is not finished.\u003C\u002Fp>\n\n\u003Ch2>This is not scepticism\u003C\u002Fh2>\n\u003Cp>None of this means AI does not work. It means it works on certain tasks, and forcing it onto the others gives you an expensive, unreliable way to do something that could have been done better.\u003C\u002Fp>\n\u003Cp>The best AI projects we have delivered had one thing in common: we threw a large part of the original brief out, because it could be solved the ordinary way.\u003C\u002Fp>","Where not to use AI — the limits nobody mentions","Concrete cases where a language model is the wrong tool: exact arithmetic, decisions with legal effect, small data, and anywhere ordinary code will do.","2026-05-27T00:00:00.000Z",[16,23,29,35,41,48,49,54,58,64,70,75,80,85,90,95,100,105,111,117,122,127,132,137,142,147,152,157,162,167,172,177,182,187,191,196,201],{"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":4,"title":5,"excerpt":6,"category":7,"author":8,"readingTime":9,"coverImage":10,"date":14},{"slug":50,"title":51,"excerpt":52,"category":45,"author":8,"readingTime":46,"coverImage":10,"date":53},"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":55,"title":56,"excerpt":57,"category":39,"author":8,"readingTime":46,"coverImage":10,"date":53},"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":59,"title":60,"excerpt":61,"category":7,"author":8,"readingTime":62,"coverImage":10,"date":63},"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.","8 min","2026-05-06T00:00:00.000Z",{"slug":65,"title":66,"excerpt":67,"category":68,"author":8,"readingTime":9,"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":9,"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":62,"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":9,"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":9,"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":62,"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":9,"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":9,"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":9,"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":9,"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":9,"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":9,"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":9,"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":9,"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":9,"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"]