[{"data":1,"prerenderedAt":577},["ShallowReactive",2],{"footer-vacatures-en":3,"use-case-interne-ai-assistent-met-eigen-kennis-en":4,"content-by-keys-offers-en-ai-reality-check,ai-prototype-sprint":94,"content-by-keys-useCases-en-ai-betrouwbaar-genoeg,ai-toevoegen-aan-bestaand-product":367,"content-by-keys-cases-en-":522,"content-by-keys-buyerTriggers-en-starten-met-ai":523},[],{"id":5,"title":6,"body":7,"description":11,"desiredOutcome":14,"extension":26,"hero":27,"key":29,"meta":30,"navigation":31,"path":32,"problem":33,"proof":45,"recognition":57,"recommendedApproach":72,"relations":74,"seo":84,"stem":87,"summary":88,"visuals":89,"__hash__":93},"useCases_en\u002Fuse-cases\u002Finterne-ai-assistent-met-eigen-kennis.md","Can we build an internal AI assistant with our own knowledge?",{"type":8,"value":9,"toc":10},"minimark",[],{"title":11,"searchDepth":12,"depth":12,"links":13},"",2,[],{"intro":15,"items":16},"You first want to know whether an internal assistant can really become usable before having a complete system built.\n",[17,20,23],{"title":18,"text":19},"Know what is technically feasible","You get insight into which sources are usable and how AI could get access to them.\n",{"title":21,"text":22},"Understand what reliable enough means","We determine where source references, validation or human review are needed.\n",{"title":24,"text":25},"A realistic solution direction","You know how data, retrieval, permissions and user experience can come together technically.\n","md",{"intro":28},"An internal assistant sounds logical: ask a question and get an immediate answer from your own knowledge. But whether that works well depends heavily on your sources, permissions, required reliability and the kind of questions employees ask.\n","interne-ai-assistent-met-eigen-kennis",{},true,"\u002Fuse-cases\u002Finterne-ai-assistent-met-eigen-kennis",{"intro":34,"causes":35},"An internal AI assistant is not simply ChatGPT with a folder of documents. Its quality is largely determined by sources, retrieval, access rights and the question of what a reliable answer is.\n",[36,39,42],{"title":37,"text":38},"Internal knowledge is rarely neatly structured","Documents differ in quality, freshness and format.\n",{"title":40,"text":41},"More context is not automatically better","Too much irrelevant information can actually make answers worse.\n",{"title":43,"text":44},"Access remains a security matter","The assistant must not become a new route to information a user normally may not see.\n",{"intro":46,"points":47},"A good knowledge assistant calls for both AI expertise and regular software engineering. Sources, data, permissions and integrations are at least as important as the model.\n",[48,51,54],{"title":49,"text":50},"We start with the information","First understand what knowledge is available and what users want to do with it.\n",{"title":52,"text":53},"Permissions belong in the design","Security is not treated as a later add-on.\n",{"title":55,"text":56},"We can go from research to implementation","If the idea proves feasible, the technical context doesn't have to be rebuilt from scratch at another party.\n",{"intro":58,"signals":59},"This usually comes up when knowledge does exist, but is hard to find or widely spread across the organisation.\n",[60,63,66,69],{"title":61,"text":62},"Employees keep asking the same questions","Answers sit in documents, manuals, SharePoint, databases or with experienced colleagues.\n",{"title":64,"text":65},"Search returns too much noise","People know the information exists somewhere, but can't quickly find the right passage or version.\n",{"title":67,"text":68},"A general chatbot is not enough","Good answers require knowledge that is not public or generally available.\n",{"title":70,"text":71},"Permissions make it complicated","Not every employee may see the same documents or data.\n",{"intro":73},"With an AI Reality Check we examine the desired questions, sources, access rules and required reliability. If the biggest uncertainty then needs practical testing, an AI Prototype Sprint can prove the most important end-to-end flow.\n",{"buyerTriggerKeys":75,"primaryOfferKey":77,"offerKeys":78,"caseKeys":80,"relatedUseCaseKeys":81},[76],"starten-met-ai","ai-reality-check",[77,79],"ai-prototype-sprint",[],[82,83],"ai-betrouwbaar-genoeg","ai-toevoegen-aan-bestaand-product",{"title":85,"description":86},"Internal AI assistant with your own knowledge | 9to5 software","An internal ChatGPT using your own documents and data? 9to5 examines sources, RAG and retrieval, permissions and technical feasibility.\n","use-cases\u002Finterne-ai-assistent-met-eigen-kennis","You want to know whether employees can reliably use AI to work with internal documents, data and knowledge that is now spread across different sources.\n",{"hero":90,"problem":91,"proof":92},"sq-knowledge-filter","sq-guardrails-check","ls-system-connect","rPps0CfPhGKhgluvbpO1IUJG7v8sTi68GVuZUyI1cG0",[95,235],{"id":96,"title":97,"body":98,"catalogue":102,"cta":113,"description":11,"extension":26,"fit":118,"hero":125,"key":77,"meta":128,"navigation":31,"next":129,"outcome":143,"path":155,"price":156,"process":159,"proof":174,"relations":188,"scope":194,"seo":222,"stem":225,"summary":226,"summaryPanel":227,"visuals":232,"__hash__":234},"offers_en\u002Foffers\u002Fai-reality-check.md","AI Reality Check",{"type":8,"value":99,"toc":100},[],{"title":11,"searchDepth":12,"depth":12,"links":101},[],{"audience":103,"promise":104,"deliverables":105,"note":111,"visual":112},"For organisations that first want to know what's technically sound and feasible.\n","In one compact engagement, you'll learn what to do, what to avoid and what's needed technically.\n",[106,107,108,109,110],"Technical feasibility and key risks","Build \u002F buy \u002F don't build","Recommended technical approach","What AI should and shouldn't do","A concrete next step","Decision document + discussion with a senior engineer.","sq-orient-options",{"title":114,"body":115,"primaryCta":116,"local":117},"Want to know what is wise first?","Share your AI question with us. If a Reality Check isn't needed, we'll tell you.\n","Book an AI Reality Check","Senior AI and software engineering from Delft, for organisations in Delft, The Hague, Rotterdam and the surrounding area.\n",{"title":119,"intro":120,"useCaseKeys":121},"When is this offer a fit?","When the main question is not yet how to build something, but what you should be building in the first place.\n",[122,29,123,124],"processen-automatiseren-met-ai","ai-idee-technisch-haalbaar","is-ai-nodig",{"headline":126,"intro":127,"primaryCta":116},"Know what to do with AI before you start building.","You see opportunities for AI, but first want to know what is wise. We examine one concrete process or up to three ideas and give you a well-founded technical direction. Even if the conclusion is that you are better off without AI.\n",{},{"title":130,"items":131},"What happens next?",[132,135,139],{"title":133,"text":134,"offerKey":79},"Prove that it works first?","Then an AI Prototype Sprint is often the logical next step.\n",{"title":136,"text":137,"offerKey":138},"Keep building yourself?","Then a Vibe Code Kickstart lets us set up the technical foundation properly.\n","vibe-code-kickstart",{"title":140,"text":141,"offerKey":142},"Scope already clear enough?","Then we can go straight to Build & Integrate.\n","build-integrate",{"title":144,"items":145},"Afterwards you have...",[146,149,152],{"title":147,"text":148},"A clear technical direction","You know which approach is promising and which options you are better off leaving alone.\n",{"title":150,"text":151},"The main uncertainties on the table","Integrations, data, reliability, risks and human oversight are made concrete enough to make a decision.\n",{"title":153,"text":154},"A logical next step","You know whether to build, buy, integrate, validate something first or stop.\n","\u002Foffers\u002Fai-reality-check",{"label":157,"type":158},"€1,950 excl. VAT","fixed",{"title":160,"steps":161},"How it works.",[162,165,168,171],{"title":163,"text":164},"Sharpen the question","We determine which decision you need to make and what a good result would be.\n",{"title":166,"text":167},"Understand the technical context","We look at processes, data, existing software, integrations and preconditions.\n",{"title":169,"text":170},"Test the options","We compare realistic routes on feasibility, risk and technical complexity.\n",{"title":172,"text":173},"Deliver decision + route","You get our conclusion, technical direction and a concrete next step.\n",{"title":175,"intro":176,"points":177,"caseKeys":187},"Why should you believe this?","We don't advise from a fixed preference for AI or custom software. Because we build software ourselves, we can judge when building is wise and when it is not.\n",[178,181,184],{"title":179,"text":180},"Engineers look at the real system","We look at the AI idea and also at data, integrations, software and operations.\n",{"title":182,"text":183},"AI isn't always the answer","Plain automation, existing software or building nothing are real alternatives.\n",{"title":185,"text":186},"We can carry out the follow-up too","If building turns out to make sense, the advice doesn't have to go to another technical party first.\n",[],{"buyerTriggerKeys":189,"useCaseKeys":190,"relatedOfferKeys":192,"caseKeys":193},[76],[122,29,123,124,82,191,83],"zelf-bouwen-of-software-kopen",[79,138,142],[],{"title":195,"intro":196,"activities":197,"deliverables":206,"exclusions":215},"What is included in this offer?","A bounded technical investigation with a concrete decision as the result.\n",{"intro":198,"items":199},"A senior engineer examines the actual question and the context around it.\n",[200,201,202,203,204,205],"Intake and sharpening the problem","Examining one concrete process or up to three AI ideas","Assessing feasibility, dependencies, integrations and risks","Comparing build, buy, integrate or not building","Determining where AI does and does not make sense","Taking human oversight and key preconditions into account",{"intro":207,"items":208},"Not a generic AI report, but a compact decision document that lets you move forward.\n",[108,209,210,211,212,213,214],"What AI should and should not do","Rough solution direction and relevant integrations","Main risks and assumptions","Build \u002F buy \u002F don't build advice","Indication of size and the next step","Review session with a senior engineer",{"intro":216,"items":217},"The Reality Check is meant to make a decision, not to build the whole system already.\n",[218,219,220,221],"Production-ready implementation","Fully worked-out UX or product design","Extensive organisation-wide AI strategy","Unbounded research into multiple processes or business units",{"title":223,"description":224},"AI Reality Check | Technical feasibility of AI | 9to5 software","A senior AI and software engineer assesses what is feasible and worthwhile: an AI agent, Claude, ChatGPT or conventional software? With an approach, risks and next step.\n","offers\u002Fai-reality-check","In a compact engagement we determine what is technically sensible and feasible, where the risks are and what a wise next step is.\n",{"qualifier":228,"result":229,"duration":230,"nextStep":231},"Fixed price · one process or up to three AI ideas","A reasoned technical direction and build \u002F buy \u002F don't build advice","About two weeks","Build, validate with a prototype first, or deliberately stop",{"hero":112,"proof":91,"process":233},"ls-route-converge","yAAdWR1HeTz_g1hVnQ1w2rPY4-tRkksM8Ro6yjOmeok",{"id":236,"title":237,"body":238,"catalogue":242,"cta":253,"description":11,"extension":26,"fit":258,"hero":262,"key":79,"meta":265,"navigation":31,"next":266,"outcome":278,"path":289,"price":290,"process":292,"proof":306,"relations":319,"scope":325,"seo":353,"stem":356,"summary":357,"summaryPanel":358,"visuals":363,"__hash__":366},"offers_en\u002Foffers\u002Fai-prototype-sprint.md","AI Prototype Sprint",{"type":8,"value":239,"toc":240},[],{"title":11,"searchDepth":12,"depth":12,"links":241},[],{"audience":243,"promise":244,"deliverables":245,"note":251,"visual":252},"For a specific AI idea whose key technical assumptions need to be validated first.\n","From one clear use case to a working end-to-end prototype that lets you really test the technology.\n",[246,247,248,249,250],"Technical architecture for one use case","Working end-to-end prototype","Up to two key integrations","Validation of key assumptions and reliability","Demo, documentation and advice on moving to production","A working technical flow, not a clickable mock-up.","ls-system-assemble",{"title":254,"body":255,"primaryCta":256,"local":257},"Prove it works technically first?","Share the use case with us. We'll help you choose the smallest prototype that actually proves something important.\n","Start an AI Prototype Sprint","AI prototyping by senior engineers from Delft, for organisations in the region.\n",{"title":119,"intro":259,"useCaseKeys":260},"When the idea is clear enough to build, but still needs to be proven before you go to production.\n",[261,82,83],"ai-prototype-laten-bouwen",{"headline":263,"intro":264,"primaryCta":256},"Prove that it works before you turn it into a complete product.","You roughly know what you want, but not yet whether the technology behaves in practice as you hope. In one bounded Sprint we build the key flow and test the assumptions that determine whether investing further is wise.\n",{},{"title":130,"items":267},[268,271,274],{"title":269,"text":270,"offerKey":142},"The prototype proves the direction","Then Build & Integrate can deliver the production version.\n",{"title":272,"text":273},"The outcome calls for adjustment","Then you have learned concretely which technical route doesn't work, without building a complete product first.\n",{"title":275,"text":276,"offerKey":277},"The prototype has become production itself","If an experiment already has real users and needs professionalising, the Rescue route is a better fit.\n","vibe-code-rescue-audit",{"title":144,"items":279},[280,283,286],{"title":281,"text":282},"A working end-to-end prototype","The key user flow actually works with the chosen AI technology and integrations.\n",{"title":284,"text":285},"Technical assumptions are tested","You know better where quality, reliability and integrations are or aren't feasible.\n",{"title":287,"text":288},"A decision about production","You know what it takes to turn the prototype into a serious product or production component.\n","\u002Foffers\u002Fai-prototype-sprint",{"label":291,"type":158},"€6,950 excl. VAT",{"title":160,"steps":293},[294,297,300,303],{"title":295,"text":296},"Define use case + success criterion","We pin down which technical assumption this Sprint needs to prove.\n",{"title":298,"text":299},"Set up architecture + flow","We choose the shortest technical route that validates the real problem.\n",{"title":301,"text":302},"Build + evaluate","We deliver the end-to-end flow and test where the solution is fragile.\n",{"title":304,"text":305},"Demo + production advice","We show what works, what is still uncertain and what a next phase needs.\n",{"title":175,"intro":307,"points":308,"caseKeys":318},"A good prototype is more than a pretty click-through demo. It answers the technical question that decides whether the concept can work.\n",[309,312,315],{"title":310,"text":311},"We test the real thing","We test real integrations, data flows and model behaviour where relevant.\n",{"title":313,"text":314},"Small enough to learn","We keep you from building half a product before the key assumption is proven.\n",{"title":316,"text":317},"Ready for a real follow-up decision","Continuing, adjusting or stopping can all be a good outcome.\n",[],{"buyerTriggerKeys":320,"useCaseKeys":322,"relatedOfferKeys":323,"caseKeys":324},[76,321],"ai-laten-bouwen",[261],[77,142,277],[],{"title":195,"intro":326,"activities":327,"deliverables":338,"exclusions":346},"One clear use case, one primary flow and a controlled amount of technology around it.\n",{"intro":328,"items":329},"We build enough to test the real technical risk, without building the complete end product right away.\n",[330,331,332,333,334,335,336,337],"Sharpening scope and success criteria","Defining the technical architecture","Building a working prototype","Up to two key external integrations","Delivering one primary end-to-end flow","Configuring the model, agent or instructions","Validating the key technical assumptions","Evaluating reliability at a high level",{"intro":339,"items":340},"A tangible prototype plus enough technical clarity for a follow-up decision.\n",[247,341,342,343,344,345],"Technical architecture direction","Validated core flow","Demo of the result","Technical documentation","Advice for production and next steps",{"intro":347,"items":348},"A Prototype Sprint proves the core. Production hardening is a separate step.\n",[349,350,351,352],"Full production guarantees or SLAs","Extensive scalability and operationalisation","Unbounded integrations or multiple main flows","Full product development beyond the agreed use case",{"title":354,"description":355},"AI Prototype Sprint | Working AI prototype | 9to5 software","Turn one AI concept into a working end-to-end prototype, for example with Claude or OpenAI. Includes architecture, integrations, validation and production advice.\n","offers\u002Fai-prototype-sprint","From one concrete AI idea to a working end-to-end prototype that lets you actually test the key technical assumptions.\n",{"qualifier":359,"result":360,"duration":361,"nextStep":362},"Fixed price · one concrete AI idea","A working end-to-end prototype with tested technical assumptions","One bounded Sprint of about three weeks","A reasoned decision about production, for example via Build & Integrate",{"hero":364,"proof":365,"process":252},"sq-foundation-build","sq-process-steps","WOLcKM4lFt5312Z6kkvWsCyd0jk_equXhLoXFeu1UMg",[368,445],{"id":369,"title":370,"body":371,"description":11,"desiredOutcome":375,"extension":26,"hero":387,"key":82,"meta":389,"navigation":31,"path":390,"problem":391,"proof":403,"recognition":415,"recommendedApproach":430,"relations":432,"seo":437,"stem":440,"summary":441,"visuals":442,"__hash__":444},"useCases_en\u002Fuse-cases\u002Fai-betrouwbaar-genoeg.md","Can AI perform this task reliably enough?",{"type":8,"value":372,"toc":373},[],{"title":11,"searchDepth":12,"depth":12,"links":374},[],{"intro":376,"items":377},"You want to set a realistic quality bar and know whether the technology can meet it with the right technical measures.\n",[378,381,384],{"title":379,"text":380},"A concrete definition of good enough","We make explicit which errors are acceptable and which are not.\n",{"title":382,"text":383},"Insight into remaining risks","You know where AI remains uncertain and what the practical impact of that is.\n",{"title":385,"text":386},"A fitting control layer","We determine where validation, fallback or human review is needed.\n",{"intro":388},"\"Can AI do this?\" is often the wrong question. A better one: how often does it go right, what mistakes does it make and what happens when such a mistake occurs? That tells you whether an AI solution is usable in practice.\n",{},"\u002Fuse-cases\u002Fai-betrouwbaar-genoeg",{"intro":392,"causes":393},"AI is probabilistic, so you can't rule out every failure scenario. You can understand, measure and contain errors.\n",[394,397,400],{"title":395,"text":396},"Average quality says too little","A high score can still hide unacceptable errors.\n",{"title":398,"text":399},"Test examples are often too friendly","Real users and data contain many more exceptions.\n",{"title":401,"text":402},"System design affects reliability","Validation, retrieval and human-in-the-loop can matter more than yet another prompt.\n",{"intro":404,"points":405},"For us reliability is an engineering question, not a feeling after a good demo.\n",[406,409,412],{"title":407,"text":408},"Errors become part of the design","We look explicitly at what can go wrong and what impact that has.\n",{"title":410,"text":411},"Human-in-the-loop where it adds value","A small human check can sometimes be much wiser than pursuing full autonomy.\n",{"title":413,"text":414},"Criteria first, then technology","Only when \"good enough\" is clear can you judge whether a solution is feasible.\n",{"intro":416,"signals":417},"This mostly comes up when an AI demo is convincing, but the task is important enough that occasional errors have consequences.\n",[418,421,424,427],{"title":419,"text":420},"Quality varies per example","Some cases go excellently and others surprisingly badly.\n",{"title":422,"text":423},"Not all errors are equally serious","A wrong phrasing is different from a wrong decision or amount.\n",{"title":425,"text":426},"Manual checking may still be needed","You want to know how much human review is still needed to run the process responsibly.\n",{"title":428,"text":429},"You can't compare objectively yet","The assessment is mostly based on a few separate tests or demos.\n",{"intro":431},"In an AI Reality Check we first define what reliable enough means for this specific task and which risks need to be managed technically. If practical measurements are needed, an AI Prototype Sprint can then actually test the most important scenarios.\n",{"buyerTriggerKeys":433,"primaryOfferKey":77,"offerKeys":434,"caseKeys":435,"relatedUseCaseKeys":436},[76],[77,79],[],[123,29],{"title":438,"description":439},"Is AI reliable enough for this task? | 9to5 software","Find out whether AI is reliable enough for your process. 9to5 looks at error margins, evals, risks, human-in-the-loop and technical feasibility.\n","use-cases\u002Fai-betrouwbaar-genoeg","You can see that AI can in principle perform the task, but want to know whether the quality is predictable enough for serious use.\n",{"hero":91,"problem":365,"proof":443},"ls-workflow-flow","1eHc-QVFMSpnvdWD_ldWkQjaMkOCDK0so4qQpB0xL8o",{"id":446,"title":447,"body":448,"description":11,"desiredOutcome":452,"extension":26,"hero":464,"key":83,"meta":466,"navigation":31,"path":467,"problem":468,"proof":480,"recognition":492,"recommendedApproach":507,"relations":509,"seo":514,"stem":517,"summary":518,"visuals":519,"__hash__":521},"useCases_en\u002Fuse-cases\u002Fai-toevoegen-aan-bestaand-product.md","How do we add AI to our existing product?",{"type":8,"value":449,"toc":450},[],{"title":11,"searchDepth":12,"depth":12,"links":451},[],{"intro":453,"items":454},"You want to know which AI feature delivers enough user value and technically fits the product that already exists.\n",[455,458,461],{"title":456,"text":457},"A sharp product use case","You know which user can solve which problem better with AI.\n",{"title":459,"text":460},"A fitting technical architecture","The new functionality connects to existing data, auth and business logic.\n",{"title":462,"text":463},"A realistic implementation route","You know what needs testing first and what can then be built production-ready.\n",{"intro":465},"You don't have to make your product \"AI-first\" to use AI well. Often most of the value lies in one targeted improvement of an existing workflow. So the first question is not how to add AI, but where.\n",{},"\u002Fuse-cases\u002Fai-toevoegen-aan-bestaand-product",{"intro":469,"causes":470},"Adding AI to existing software is both a product choice and an architecture choice.\n",[471,474,477],{"title":472,"text":473},"The technology draws too much attention","An interesting AI possibility is not yet a useful product feature.\n",{"title":475,"text":476},"Existing data and permissions count","A feature has to fit the same user and security context as the rest of the product.\n",{"title":478,"text":479},"New failure modes are added","Variable quality, latency and model costs have to become part of the product experience.\n",{"intro":481,"points":482},"9to5 software likes working in existing products and codebases. So AI doesn't have to stay a separate experiment next to the real product.\n",[483,486,489],{"title":484,"text":485},"Product and technology together","We assess both user value and technical consequences.\n",{"title":487,"text":488},"Existing software remains the starting point","Auth, data and business logic are reused where possible.\n",{"title":490,"text":491},"From exploration to real implementation","If the use case holds up, the same engineers can also build the hard technical follow-up.\n",{"intro":493,"signals":494},"This often comes up when competitors launch AI features or users start asking what AI could mean in the product.\n",[495,498,501,504],{"title":496,"text":497},"There are several possible AI features","Summarising, generating, searching, agents and automation all seem interesting.\n",{"title":499,"text":500},"You don't want to build a gimmick","The feature has to demonstrably solve an existing user problem better.\n",{"title":502,"text":503},"The feature needs product data","AI has to be able to safely use information that is already in the software.\n",{"title":505,"text":506},"The existing architecture sets limits","You want to avoid one AI feature becoming a separate technical island.\n",{"intro":508},"In an AI Reality Check we first determine which AI use case has enough value and technical feasibility and how it fits into the existing product. An AI Prototype Sprint can then prove the core before a larger Build & Integrate implementation follows.\n",{"buyerTriggerKeys":510,"primaryOfferKey":77,"offerKeys":511,"caseKeys":512,"relatedUseCaseKeys":513},[76],[77,79,142],[],[123,82],{"title":515,"description":516},"Add AI to an existing product | 9to5 software","Adding AI to an existing app or SaaS product? 9to5 examines promising use cases, architecture, data, integrations and technical feasibility.\n","use-cases\u002Fai-toevoegen-aan-bestaand-product","You see opportunities to make your current software smarter, but first want to determine which AI functionality really adds something, technically and commercially.\n",{"hero":520,"problem":364,"proof":92},"sq-connect-nodes","RSVufcPhYOQ6qeNYCFlOmKFGLqdfCrZhxfRPGRtSLjM",[],[524],{"id":525,"title":526,"approach":527,"body":542,"cta":546,"description":11,"extension":26,"hero":549,"key":76,"label":551,"meta":552,"navigation":31,"path":553,"proof":554,"relations":565,"seo":570,"stem":573,"summary":574,"visuals":575,"__hash__":576},"buyerTriggers_en\u002Fbuyer-triggers\u002Fstarten-met-ai.md","We want to get started with AI, but aren't sure how.",{"title":528,"steps":529},"From a challenge to a sound technical approach.",[530,533,536,539],{"title":531,"text":532},"Define the problem","We establish which decision you need to make, which process you want to improve and what a good result would look like.\n",{"title":534,"text":535},"Understand the context and systems","We look at existing software, data, integrations, users and technical constraints.\n",{"title":537,"text":538},"Assess the options","We compare AI, automation, existing software, integrations and custom development in terms of feasibility, risk and value.\n",{"title":540,"text":541},"Deliver a decision and a way forward","You get a concrete technical direction, the main risks and a logical next step.\n",{"type":8,"value":543,"toc":544},[],{"title":11,"searchDepth":12,"depth":12,"links":545},[],{"title":547,"body":548},"Not sure whether AI is the right solution here?","Briefly describe the idea or process you have in mind. We'll quickly tell you what looks technically promising and what a sensible first step would be.\n",{"intro":550},"You might have an idea, a process that could work better or simply a sense that AI could do more for you. We help you work out what looks promising, where the risks lie and whether AI is actually the right solution.\n","Start with AI",{},"\u002Fbuyer-triggers\u002Fstarten-met-ai",{"intro":555,"points":556},"We don't advise from a fixed preference for AI or custom software. Because we build software ourselves, we can honestly assess what is technically feasible and worthwhile, and what you are better off not building.\n",[557,559,562],{"title":179,"text":558},"We look at the AI idea, but also at the data, existing software, integrations and users.\n",{"title":560,"text":561},"AI is a tool, not the default answer","Automation, existing software, an integration or building nothing are all serious options too.\n",{"title":563,"text":564},"Advice and implementation from the same people","If building turns out to make sense, we can carry out the next steps ourselves. That keeps our advice realistic.\n",{"useCaseKeys":566,"offerKeys":568,"caseKeys":569},[122,29,123,124,82,191,83,567],"vibe-coding-goed-beginnen",[77,138,79],[],{"title":571,"description":572},"Getting started with AI? From idea to technical approach | 9to5 software","Help with a serious AI challenge. An AI agent, ChatGPT, Claude or conventional software? We assess what is feasible, what not to build and the next step.\n","buyer-triggers\u002Fstarten-met-ai","We see opportunities for AI, but first want to know what makes sense, what is feasible and what is technically sound.\n",{"hero":112,"proof":233,"approach":233},"oMS04pD9lTBstiI46yn57yKInU-_urtigoQYGlj0PWA",1792049912173]