[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"post-ako-sa-dostat-do-odpovedi-chatgpt-perplexity":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},"ako-sa-dostat-do-odpovedi-chatgpt-perplexity","How to get into ChatGPT and Perplexity answers (AEO\u002FGEO)","AI assistants increasingly replace classic search. How to prepare content so ChatGPT, Perplexity and Google AI Overviews cite you.","Guides","MightCore","7 min",null,"\u003Cp>More and more people do not ask a search engine but an AI assistant directly — ChatGPT, Perplexity or Google AI Overviews. Instead of a list of links they get a single answer, often with a few cited sources. If you are not among them, the customer never hears about you. Optimising for these answers is called AEO (Answer Engine Optimization) or GEO (Generative Engine Optimization).\u003C\u002Fp>\n\u003Ch2>How AI chooses who to cite\u003C\u002Fh2>\n\u003Cp>The model looks for passages that answer the question directly and unambiguously, can be verified, and have a clear structure. Marketing filler does not help, nor does content where the answer hides in the third paragraph. The opposite helps: the answer up front, concrete facts, and headings that mirror real questions.\u003C\u002Fp>\n\u003Ch2>What to do with the content\u003C\u002Fh2>\n\u003Cp>Write answer-first: let the first sentence of a section be the direct answer, the rest expands it. Use headings phrased as the questions people actually type. Give specific numbers, steps and examples instead of generic phrases — those are citable. And link related pages so the model sees the whole site as context.\u003C\u002Fp>\n\u003Ch2>The technical part people forget\u003C\u002Fh2>\n\u003Cp>AI crawlers must be able to reach and understand the content. That is the job of \u003Ca href=\"\u002Fblog\u002Fseo-v-ere-ai-vyhladavania\">structured data, llms.txt and clean technical SEO\u003C\u002Fa>, which we covered in a separate article. Without them even great content may stay invisible.\u003C\u002Fp>\n\u003Ch2>Where to start\u003C\u002Fh2>\n\u003Cp>This is exactly what we handle under \u003Ca href=\"\u002Fservices#ai-optimalizacie\">AI optimisation\u003C\u002Fa> — from rewriting content to the technical setup. If you want to see how we use AI in production while keeping quality, read \u003Ca href=\"\u002Fhow-it-works\">our Context Driven Development approach\u003C\u002Fa>.\u003C\u002Fp>","How to get into ChatGPT and Perplexity answers | MightCore","AEO and GEO in practice: how to write content so AI assistants (ChatGPT, Perplexity, Google AI Overviews) find, understand and cite you — beyond classic SEO.","2026-06-30T00:00:00.000Z",[16,21,26,27,32,39,44,50,57,63,68,74,79,83,87,92,97,103,108,112,117,122,127,131,136,141,146,152,158,163,168,173,178,183,188,193,198,203,208,213,218,223,228,233,237,242,247],{"slug":17,"title":18,"excerpt":19,"category":7,"author":8,"readingTime":9,"coverImage":10,"date":20},"seo-v-ere-ai-vyhladavania","SEO in the age of AI search: data, llms.txt and answer-first","Classic SEO did not vanish, it expanded. How structured data, llms.txt and answer-first content decide visibility in search engines and in AI.","2026-07-18T00:00:00.000Z",{"slug":22,"title":23,"excerpt":24,"category":7,"author":8,"readingTime":9,"coverImage":10,"date":25},"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.","2026-07-08T00:00:00.000Z",{"slug":4,"title":5,"excerpt":6,"category":7,"author":8,"readingTime":9,"coverImage":10,"date":14},{"slug":28,"title":29,"excerpt":30,"category":31,"author":8,"readingTime":9,"coverImage":10,"date":14},"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",{"slug":33,"title":34,"excerpt":35,"category":36,"author":8,"readingTime":37,"coverImage":10,"date":38},"why-we-charge-for-value-not-hours","Why we charge for value, not hours","Billing by the hour rewards slowness and punishes experience. Here is why we look at what a solution brings the business, not what it cost us.","How we work","6 min","2026-06-17T00:00:00.000Z",{"slug":40,"title":41,"excerpt":42,"category":31,"author":8,"readingTime":43,"coverImage":10,"date":38},"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",{"slug":45,"title":46,"excerpt":47,"category":48,"author":8,"readingTime":9,"coverImage":10,"date":49},"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":51,"title":52,"excerpt":53,"category":54,"author":8,"readingTime":55,"coverImage":10,"date":56},"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":58,"title":59,"excerpt":60,"category":61,"author":8,"readingTime":37,"coverImage":10,"date":62},"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","2026-05-27T00:00:00.000Z",{"slug":64,"title":65,"excerpt":66,"category":7,"author":8,"readingTime":37,"coverImage":10,"date":67},"review-a-audit-aplikacie","Application review and audit: an independent look at your software","What works, what is a risk and what will be expensive. What an independent application audit looks like and what you actually get from it.","2026-05-26T00:00:00.000Z",{"slug":69,"title":70,"excerpt":71,"category":36,"author":8,"readingTime":72,"coverImage":10,"date":73},"how-to-spot-a-good-software-vendor-and-the-red-flags","How to spot a good software vendor (and the red flags)","You spot a good vendor by what they do before the contract is signed, not by their portfolio. Here are the signs of trust and the warning signs.","8 min","2026-05-20T00:00:00.000Z",{"slug":75,"title":76,"excerpt":77,"category":7,"author":8,"readingTime":37,"coverImage":10,"date":78},"hosting-ai-aplikacii","Hosting AI applications: running models, APIs and data safely","Model, API and data in one secure place. What reliable hosting of AI applications involves and how not to get a shocking model-call bill.","2026-05-19T00:00:00.000Z",{"slug":80,"title":81,"excerpt":82,"category":48,"author":8,"readingTime":55,"coverImage":10,"date":78},"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":84,"title":85,"excerpt":86,"category":54,"author":8,"readingTime":55,"coverImage":10,"date":78},"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.",{"slug":88,"title":89,"excerpt":90,"category":7,"author":8,"readingTime":37,"coverImage":10,"date":91},"ai-mentoring-pre-firmy","AI mentoring for companies: teaching a team to actually use AI","Buying an AI tool does not change a team. What practical AI mentoring looks like, where AI helps and where it is just needless complication.","2026-05-12T00:00:00.000Z",{"slug":93,"title":94,"excerpt":95,"category":61,"author":8,"readingTime":72,"coverImage":10,"date":96},"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.","2026-05-06T00:00:00.000Z",{"slug":98,"title":99,"excerpt":100,"category":101,"author":8,"readingTime":37,"coverImage":10,"date":102},"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":104,"title":105,"excerpt":106,"category":36,"author":8,"readingTime":9,"coverImage":10,"date":107},"fixed-price-or-hourly-which-is-better-for-the-client","Fixed price or hourly: which is better for the client","A fixed price moves the risk to the vendor, hourly moves it to the client. Here is when each fits — and why neither is always the right one.","2026-04-15T00:00:00.000Z",{"slug":109,"title":110,"excerpt":111,"category":61,"author":8,"readingTime":43,"coverImage":10,"date":107},"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.",{"slug":113,"title":114,"excerpt":115,"category":54,"author":8,"readingTime":37,"coverImage":10,"date":116},"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":118,"title":119,"excerpt":120,"category":7,"author":8,"readingTime":72,"coverImage":10,"date":121},"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":123,"title":124,"excerpt":125,"category":7,"author":8,"readingTime":37,"coverImage":10,"date":126},"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":128,"title":129,"excerpt":130,"category":7,"author":8,"readingTime":37,"coverImage":10,"date":126},"uctovnictvo-novej-generacie","When it pays to have your application reviewed — and what an audit actually gives you","You have software that runs, but you are not sure of its state? When an independent audit makes sense and what its output looks like.",{"slug":132,"title":133,"excerpt":134,"category":36,"author":8,"readingTime":37,"coverImage":10,"date":135},"what-happens-in-a-first-software-consultation","What happens in a first consultation, and what you take away","A first consultation is not a sales call. It is an hour spent understanding your problem — and you often leave with a clearer view, even if we never start.","2026-03-11T00:00:00.000Z",{"slug":137,"title":138,"excerpt":139,"category":7,"author":8,"readingTime":43,"coverImage":10,"date":140},"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":142,"title":143,"excerpt":144,"category":7,"author":8,"readingTime":55,"coverImage":10,"date":145},"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":147,"title":148,"excerpt":149,"category":150,"author":8,"readingTime":72,"coverImage":10,"date":151},"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":153,"title":154,"excerpt":155,"category":156,"author":8,"readingTime":55,"coverImage":10,"date":157},"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":159,"title":160,"excerpt":161,"category":36,"author":8,"readingTime":9,"coverImage":10,"date":162},"why-context-comes-before-code","Why context comes before code","Most software does not fail on the code; it fails because the wrong thing got built. Here is why everything we do starts with understanding the context.","2026-02-04T00:00:00.000Z",{"slug":164,"title":165,"excerpt":166,"category":150,"author":8,"readingTime":9,"coverImage":10,"date":167},"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":169,"title":170,"excerpt":171,"category":101,"author":8,"readingTime":37,"coverImage":10,"date":172},"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":174,"title":175,"excerpt":176,"category":101,"author":8,"readingTime":37,"coverImage":10,"date":177},"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":179,"title":180,"excerpt":181,"category":54,"author":8,"readingTime":55,"coverImage":10,"date":182},"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":184,"title":185,"excerpt":186,"category":101,"author":8,"readingTime":37,"coverImage":10,"date":187},"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":189,"title":190,"excerpt":191,"category":7,"author":8,"readingTime":9,"coverImage":10,"date":192},"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":194,"title":195,"excerpt":196,"category":48,"author":8,"readingTime":37,"coverImage":10,"date":197},"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":199,"title":200,"excerpt":201,"category":48,"author":8,"readingTime":55,"coverImage":10,"date":202},"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":204,"title":205,"excerpt":206,"category":101,"author":8,"readingTime":37,"coverImage":10,"date":207},"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":209,"title":210,"excerpt":211,"category":48,"author":8,"readingTime":55,"coverImage":10,"date":212},"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":214,"title":215,"excerpt":216,"category":54,"author":8,"readingTime":37,"coverImage":10,"date":217},"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":219,"title":220,"excerpt":221,"category":156,"author":8,"readingTime":55,"coverImage":10,"date":222},"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":224,"title":225,"excerpt":226,"category":7,"author":8,"readingTime":55,"coverImage":10,"date":227},"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":229,"title":230,"excerpt":231,"category":7,"author":8,"readingTime":37,"coverImage":10,"date":232},"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":234,"title":235,"excerpt":236,"category":101,"author":8,"readingTime":37,"coverImage":10,"date":232},"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":238,"title":239,"excerpt":240,"category":156,"author":8,"readingTime":55,"coverImage":10,"date":241},"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":243,"title":244,"excerpt":245,"category":101,"author":8,"readingTime":9,"coverImage":10,"date":246},"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":248,"title":249,"excerpt":250,"category":101,"author":8,"readingTime":37,"coverImage":10,"date":251},"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"]