[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"post-how-to-bring-ai-into-your-company-first-steps":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},"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.","For business","MightCore","9 min",null,"\u003Cp>The question we hear most often is: \"We'd like to do something with AI. What could we do?\"\u003C\u002Fp>\n\u003Cp>It is the wrong question, though I understand entirely why it gets asked. The pressure is external — a competitor announced something, it was on every slide at a conference, someone raised it in a meeting. The trouble is that this is how you start a project nobody uses six months later.\u003C\u002Fp>\n\u003Cp>The better question is duller: \u003Cstrong>which work in this company is repetitive, tedious, and done by an expensive person?\u003C\u002Fstrong>\u003C\u002Fp>\n\n\u003Ch2>Start from the pain, not the technology\u003C\u002Fh2>\n\u003Cp>When hunting for a first project, do not look for \"where could AI go\". Look for where somebody spends hours copying data from one place to another. Where the same questions come round again and again. Where something takes three days even though the work itself is twenty minutes and the rest is waiting.\u003C\u002Fp>\n\u003Cp>Every company has these, and everybody knows where they are. They are usually described as \"that's just how we've always done it\".\u003C\u002Fp>\n\u003Cp>Once you find one, ask three questions:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>How many hours a month does it eat? Under ten, leave it alone.\u003C\u002Fli>\n\u003Cli>Is there data — examples of how it has been done so far?\u003C\u002Fli>\n\u003Cli>Does it matter if it is occasionally wrong, or is that unacceptable?\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>The third one matters most. If the answer is \"it can never be wrong\", do not start there. Not because it cannot be done, but because a first project needs to succeed.\u003C\u002Fp>\n\n\u003Ch2>The first project should be small and boring\u003C\u002Fh2>\n\u003Cp>I understand the pull toward something big and visible. Resist it.\u003C\u002Fp>\n\u003Cp>A good first project ships in weeks, touches one team, can be switched off with no consequences, and has a measurable effect. It needs no process redesign and no company-wide buy-in.\u003C\u002Fp>\n\u003Cp>Candidates that reliably work out: pulling fields out of documents (invoices, delivery notes, contracts), sorting and routing inbound email, drafting first-pass support replies, searching your own documentation, generating product copy from specifications.\u003C\u002Fp>\n\u003Cp>All boring. All effective.\u003C\u002Fp>\n\n\u003Ch2>What to measure — and measure it \u003Cem>first\u003C\u002Fem>\u003C\u002Fh2>\n\u003Cp>This is the step almost everyone skips, and then nobody can say whether it was worth it.\u003C\u002Fp>\n\u003Cp>Before anything is deployed, measure the status quo. How long one item takes. How many arrive a month. How many are wrong. How many hours someone spends on it.\u003C\u002Fp>\n\u003Cp>Without that number you will be having a feelings-based argument in six months. With it, you have an answer in ten minutes.\u003C\u002Fp>\n\u003Cp>Measure the uncomfortable one too: how many outputs a human had to correct. That is the only figure that tells you whether the system genuinely helps or has merely moved the work from writing to checking.\u003C\u002Fp>\n\n\u003Ch2>A human in the loop is not a weakness\u003C\u002Fh2>\n\u003Cp>Nearly everyone wants full automation. Nearly nobody needs it.\u003C\u002Fp>\n\u003Cp>A model that drafts and a person who confirms with one click saves most of the time and keeps control. A model that acts alone saves slightly more time and introduces a risk nobody is watching.\u003C\u002Fp>\n\u003Cp>The sensible path is incremental anyway: let AI suggest and have a human check everything. When months of data show it is not wrong within some category, let that category through automatically. Keep reviewing the rest. Never advance without the numbers to justify it.\u003C\u002Fp>\n\n\u003Ch2>The money, and what nobody mentions up front\u003C\u002Fh2>\n\u003Cp>Model calls are cheaper than people expect. At sensible volumes most business deployments land in the tens to low hundreds of euros a month — negligible against work that used to cost a person hours.\u003C\u002Fp>\n\u003Cp>The expensive parts are elsewhere: preparing data, integrating with the systems you already run, and the time of the people who have to explain how any of it currently works. That is where eighty percent of the budget goes. Anyone selling you an AI project who only talks about the model has either never done one or is not telling you everything.\u003C\u002Fp>\n\u003Cp>The other thing worth saying out loud: costs scale with use. A twenty-euro pilot can be a two-thousand-euro production system. That is not a problem when you know in advance. It is a problem when you learn it from an invoice.\u003C\u002Fp>\n\n\u003Ch2>Where it actually gets stuck\u003C\u002Fh2>\n\u003Cp>Not on the technology. Almost never.\u003C\u002Fp>\n\u003Cp>It gets stuck on data that is a mess, scattered across five systems, owned by nobody. On people who do not want to use it because nobody asked them. On a sponsor who will never see the thing in action. And on a missing decision about what happens when the system is wrong — who catches it and who fixes it.\u003C\u002Fp>\n\u003Cp>All of that is solvable. But it has to be solved \u003Cem>before\u003C\u002Fem> anyone writes code, not after.\u003C\u002Fp>\n\n\u003Ch2>The whole thing on one screen\u003C\u002Fh2>\n\u003Cp>Pick one repetitive process costing somebody at least ten hours a month. Measure where it stands today. Deploy AI as a suggestion, not a decision. Track how many suggestions get corrected. When that number is low enough, let a slice run automatically. And if after three months it is not helping, switch it off — that is not a failure, that is information you bought cheaply.\u003C\u002Fp>","How to bring AI into your company — first steps without waste","A practical route to adopting AI in a company: picking the first process, what to measure, what it costs, and the mistakes everyone else makes.","2026-04-15T00:00:00.000Z",[16,23,29,34,40,47,53,58,62,68,74,75,80,85,90,95,100,105,111,117,122,127,132,137,142,147,152,157,162,167,172,177,182,187,191,196,201],{"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":9,"coverImage":10,"date":33},"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.","2026-06-17T00:00:00.000Z",{"slug":35,"title":36,"excerpt":37,"category":38,"author":8,"readingTime":21,"coverImage":10,"date":39},"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":41,"title":42,"excerpt":43,"category":44,"author":8,"readingTime":45,"coverImage":10,"date":46},"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":48,"title":49,"excerpt":50,"category":7,"author":8,"readingTime":51,"coverImage":10,"date":52},"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":54,"title":55,"excerpt":56,"category":44,"author":8,"readingTime":45,"coverImage":10,"date":57},"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":59,"title":60,"excerpt":61,"category":38,"author":8,"readingTime":45,"coverImage":10,"date":57},"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":63,"title":64,"excerpt":65,"category":7,"author":8,"readingTime":66,"coverImage":10,"date":67},"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":69,"title":70,"excerpt":71,"category":72,"author":8,"readingTime":51,"coverImage":10,"date":73},"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":4,"title":5,"excerpt":6,"category":7,"author":8,"readingTime":9,"coverImage":10,"date":14},{"slug":76,"title":77,"excerpt":78,"category":44,"author":8,"readingTime":51,"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":66,"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":51,"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":51,"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":9,"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":45,"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":66,"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":45,"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":72,"author":8,"readingTime":51,"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":72,"author":8,"readingTime":51,"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":44,"author":8,"readingTime":45,"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":72,"author":8,"readingTime":51,"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":38,"author":8,"readingTime":51,"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":38,"author":8,"readingTime":45,"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":72,"author":8,"readingTime":51,"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":38,"author":8,"readingTime":45,"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":44,"author":8,"readingTime":51,"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":45,"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":45,"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":51,"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":72,"author":8,"readingTime":51,"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":45,"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":72,"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":72,"author":8,"readingTime":51,"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"]