[{"data":1,"prerenderedAt":392},["ShallowReactive",2],{"offer-ai-prototype-sprint-en":3,"footer-vacatures-en":153,"content-by-keys-useCases-en-ai-prototype-laten-bouwen,ai-betrouwbaar-genoeg,ai-toevoegen-aan-bestaand-product":154,"content-by-keys-cases-en-":391},{"id":4,"title":5,"body":6,"catalogue":13,"cta":24,"description":10,"extension":29,"fit":30,"hero":37,"key":40,"meta":41,"navigation":42,"next":43,"outcome":57,"path":69,"price":70,"process":73,"proof":88,"relations":102,"scope":110,"seo":139,"stem":142,"summary":143,"summaryPanel":144,"visuals":149,"__hash__":152},"offers_en\u002Foffers\u002Fai-prototype-sprint.md","AI Prototype Sprint",{"type":7,"value":8,"toc":9},"minimark",[],{"title":10,"searchDepth":11,"depth":11,"links":12},"",2,[],{"audience":14,"promise":15,"deliverables":16,"note":22,"visual":23},"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",[17,18,19,20,21],"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":25,"body":26,"primaryCta":27,"local":28},"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","md",{"title":31,"intro":32,"useCaseKeys":33},"When is this offer a fit?","When the idea is clear enough to build, but still needs to be proven before you go to production.\n",[34,35,36],"ai-prototype-laten-bouwen","ai-betrouwbaar-genoeg","ai-toevoegen-aan-bestaand-product",{"headline":38,"intro":39,"primaryCta":27},"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","ai-prototype-sprint",{},true,{"title":44,"items":45},"What happens next?",[46,50,53],{"title":47,"text":48,"offerKey":49},"The prototype proves the direction","Then Build & Integrate can deliver the production version.\n","build-integrate",{"title":51,"text":52},"The outcome calls for adjustment","Then you have learned concretely which technical route doesn't work, without building a complete product first.\n",{"title":54,"text":55,"offerKey":56},"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":58,"items":59},"Afterwards you have...",[60,63,66],{"title":61,"text":62},"A working end-to-end prototype","The key user flow actually works with the chosen AI technology and integrations.\n",{"title":64,"text":65},"Technical assumptions are tested","You know better where quality, reliability and integrations are or aren't feasible.\n",{"title":67,"text":68},"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":71,"type":72},"€6,950 excl. VAT","fixed",{"title":74,"steps":75},"How it works.",[76,79,82,85],{"title":77,"text":78},"Define use case + success criterion","We pin down which technical assumption this Sprint needs to prove.\n",{"title":80,"text":81},"Set up architecture + flow","We choose the shortest technical route that validates the real problem.\n",{"title":83,"text":84},"Build + evaluate","We deliver the end-to-end flow and test where the solution is fragile.\n",{"title":86,"text":87},"Demo + production advice","We show what works, what is still uncertain and what a next phase needs.\n",{"title":89,"intro":90,"points":91,"caseKeys":101},"Why should you believe this?","A good prototype is more than a pretty click-through demo. It answers the technical question that decides whether the concept can work.\n",[92,95,98],{"title":93,"text":94},"We test the real thing","We test real integrations, data flows and model behaviour where relevant.\n",{"title":96,"text":97},"Small enough to learn","We keep you from building half a product before the key assumption is proven.\n",{"title":99,"text":100},"Ready for a real follow-up decision","Continuing, adjusting or stopping can all be a good outcome.\n",[],{"buyerTriggerKeys":103,"useCaseKeys":106,"relatedOfferKeys":107,"caseKeys":109},[104,105],"starten-met-ai","ai-laten-bouwen",[34],[108,49,56],"ai-reality-check",[],{"title":111,"intro":112,"activities":113,"deliverables":124,"exclusions":132},"What is included in this offer?","One clear use case, one primary flow and a controlled amount of technology around it.\n",{"intro":114,"items":115},"We build enough to test the real technical risk, without building the complete end product right away.\n",[116,117,118,119,120,121,122,123],"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":125,"items":126},"A tangible prototype plus enough technical clarity for a follow-up decision.\n",[18,127,128,129,130,131],"Technical architecture direction","Validated core flow","Demo of the result","Technical documentation","Advice for production and next steps",{"intro":133,"items":134},"A Prototype Sprint proves the core. Production hardening is a separate step.\n",[135,136,137,138],"Full production guarantees or SLAs","Extensive scalability and operationalisation","Unbounded integrations or multiple main flows","Full product development beyond the agreed use case",{"title":140,"description":141},"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":145,"result":146,"duration":147,"nextStep":148},"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":150,"proof":151,"process":23},"sq-foundation-build","sq-process-steps","WOLcKM4lFt5312Z6kkvWsCyd0jk_equXhLoXFeu1UMg",[],[155,233,313],{"id":156,"title":157,"body":158,"description":10,"desiredOutcome":162,"extension":29,"hero":173,"key":34,"meta":175,"navigation":42,"path":176,"problem":177,"proof":189,"recognition":201,"recommendedApproach":216,"relations":218,"seo":225,"stem":228,"summary":229,"visuals":230,"__hash__":232},"useCases_en\u002Fuse-cases\u002Fai-prototype-laten-bouwen.md","Build an AI concept or prototype quickly",{"type":7,"value":159,"toc":160},[],{"title":10,"searchDepth":11,"depth":11,"links":161},[],{"intro":163,"items":164},"You don't want slides about what AI could do. You want something that works, so you can make a real technical decision.\n",[165,168,170],{"title":166,"text":167},"A working core flow","The most important user flow works from start to finish.\n",{"title":64,"text":169},"You see whether model, data and integrations work together well enough.\n",{"title":171,"text":172},"A clear follow-up decision","After the prototype you know better what production requires and whether building on makes sense.\n",{"intro":174},"Sometimes you know exactly what you want, but not yet whether it will work well enough technically. Then a full production project is too early. We first build the smallest serious prototype that lets you test the key assumptions.\n",{},"\u002Fuse-cases\u002Fai-prototype-laten-bouwen",{"intro":178,"causes":179},"A prototype should remove uncertainty. If it tries to be the full product already, it becomes unnecessarily slow and expensive.\n",[180,183,186],{"title":181,"text":182},"Scope grows easily","As soon as something works, ideas for accounts, dashboards and extra features appear.\n",{"title":184,"text":185},"Production requirements distract from the core question","The first goal is proving that the most important AI assumption works.\n",{"title":187,"text":188},"Technical feasibility is hard to predict on paper","Some model and data questions you simply have to try.\n",{"intro":190,"points":191},"We build a prototype so you can base a technical decision on it. It is as big as it needs to be to test the uncertainty honestly.\n",[192,195,198],{"title":193,"text":194},"Working instead of theoretical","The most important technical assumption is actually implemented.\n",{"title":196,"text":197},"Scope stays deliberately small","No complete product posing as a prototype.\n",{"title":199,"text":200},"The follow-up is a new choice","Even after a successful sprint you are not obliged to continue.\n",{"intro":202,"signals":203},"A prototype helps most when the biggest uncertainty is technical and won't go away with another meeting.\n",[204,207,210,213],{"title":205,"text":206},"The concept is already fairly sharp","You know which user needs to be able to solve which problem.\n",{"title":208,"text":209},"One technical assumption determines a lot","For example answer quality, document interpretation or agent behaviour.\n",{"title":211,"text":212},"Building in full right away feels too early","There is still too much uncertainty for a larger fixed scope.\n",{"title":214,"text":215},"A real demo would settle the discussion","Stakeholders can decide better when they actually see the system working.\n",{"intro":217},"The AI Prototype Sprint is the primary route here: one clear use case, one working end-to-end flow and at most the most important integrations. Afterwards you get not only the prototype but also advice on a possible production version.\n",{"buyerTriggerKeys":219,"primaryOfferKey":40,"offerKeys":220,"caseKeys":221,"relatedUseCaseKeys":222},[105],[40,49],[],[223,224],"ai-agent-laten-bouwen","ai-toevoegen-aan-bestaande-software",{"title":226,"description":227},"Have an AI prototype built | 9to5 software","Validate an AI concept quickly with a working prototype? 9to5 builds an end-to-end AI prototype that tests the most important technical assumptions.\n","use-cases\u002Fai-prototype-laten-bouwen","The idea is concrete enough to build, but you first want to show that the most important technical assumption actually works.\n",{"hero":150,"problem":231,"proof":23},"sq-orient-options","2TwZUJkbgDuCmkqkRlk3otdKXvT8Qwxjs8j3pKfpC-M",{"id":234,"title":235,"body":236,"description":10,"desiredOutcome":240,"extension":29,"hero":252,"key":35,"meta":254,"navigation":42,"path":255,"problem":256,"proof":268,"recognition":280,"recommendedApproach":295,"relations":297,"seo":304,"stem":307,"summary":308,"visuals":309,"__hash__":312},"useCases_en\u002Fuse-cases\u002Fai-betrouwbaar-genoeg.md","Can AI perform this task reliably enough?",{"type":7,"value":237,"toc":238},[],{"title":10,"searchDepth":11,"depth":11,"links":239},[],{"intro":241,"items":242},"You want to set a realistic quality bar and know whether the technology can meet it with the right technical measures.\n",[243,246,249],{"title":244,"text":245},"A concrete definition of good enough","We make explicit which errors are acceptable and which are not.\n",{"title":247,"text":248},"Insight into remaining risks","You know where AI remains uncertain and what the practical impact of that is.\n",{"title":250,"text":251},"A fitting control layer","We determine where validation, fallback or human review is needed.\n",{"intro":253},"\"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":257,"causes":258},"AI is probabilistic, so you can't rule out every failure scenario. You can understand, measure and contain errors.\n",[259,262,265],{"title":260,"text":261},"Average quality says too little","A high score can still hide unacceptable errors.\n",{"title":263,"text":264},"Test examples are often too friendly","Real users and data contain many more exceptions.\n",{"title":266,"text":267},"System design affects reliability","Validation, retrieval and human-in-the-loop can matter more than yet another prompt.\n",{"intro":269,"points":270},"For us reliability is an engineering question, not a feeling after a good demo.\n",[271,274,277],{"title":272,"text":273},"Errors become part of the design","We look explicitly at what can go wrong and what impact that has.\n",{"title":275,"text":276},"Human-in-the-loop where it adds value","A small human check can sometimes be much wiser than pursuing full autonomy.\n",{"title":278,"text":279},"Criteria first, then technology","Only when \"good enough\" is clear can you judge whether a solution is feasible.\n",{"intro":281,"signals":282},"This mostly comes up when an AI demo is convincing, but the task is important enough that occasional errors have consequences.\n",[283,286,289,292],{"title":284,"text":285},"Quality varies per example","Some cases go excellently and others surprisingly badly.\n",{"title":287,"text":288},"Not all errors are equally serious","A wrong phrasing is different from a wrong decision or amount.\n",{"title":290,"text":291},"Manual checking may still be needed","You want to know how much human review is still needed to run the process responsibly.\n",{"title":293,"text":294},"You can't compare objectively yet","The assessment is mostly based on a few separate tests or demos.\n",{"intro":296},"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":298,"primaryOfferKey":108,"offerKeys":299,"caseKeys":300,"relatedUseCaseKeys":301},[104],[108,40],[],[302,303],"ai-idee-technisch-haalbaar","interne-ai-assistent-met-eigen-kennis",{"title":305,"description":306},"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":310,"problem":151,"proof":311},"sq-guardrails-check","ls-workflow-flow","1eHc-QVFMSpnvdWD_ldWkQjaMkOCDK0so4qQpB0xL8o",{"id":314,"title":315,"body":316,"description":10,"desiredOutcome":320,"extension":29,"hero":332,"key":36,"meta":334,"navigation":42,"path":335,"problem":336,"proof":348,"recognition":360,"recommendedApproach":375,"relations":377,"seo":382,"stem":385,"summary":386,"visuals":387,"__hash__":390},"useCases_en\u002Fuse-cases\u002Fai-toevoegen-aan-bestaand-product.md","How do we add AI to our existing product?",{"type":7,"value":317,"toc":318},[],{"title":10,"searchDepth":11,"depth":11,"links":319},[],{"intro":321,"items":322},"You want to know which AI feature delivers enough user value and technically fits the product that already exists.\n",[323,326,329],{"title":324,"text":325},"A sharp product use case","You know which user can solve which problem better with AI.\n",{"title":327,"text":328},"A fitting technical architecture","The new functionality connects to existing data, auth and business logic.\n",{"title":330,"text":331},"A realistic implementation route","You know what needs testing first and what can then be built production-ready.\n",{"intro":333},"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":337,"causes":338},"Adding AI to existing software is both a product choice and an architecture choice.\n",[339,342,345],{"title":340,"text":341},"The technology draws too much attention","An interesting AI possibility is not yet a useful product feature.\n",{"title":343,"text":344},"Existing data and permissions count","A feature has to fit the same user and security context as the rest of the product.\n",{"title":346,"text":347},"New failure modes are added","Variable quality, latency and model costs have to become part of the product experience.\n",{"intro":349,"points":350},"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",[351,354,357],{"title":352,"text":353},"Product and technology together","We assess both user value and technical consequences.\n",{"title":355,"text":356},"Existing software remains the starting point","Auth, data and business logic are reused where possible.\n",{"title":358,"text":359},"From exploration to real implementation","If the use case holds up, the same engineers can also build the hard technical follow-up.\n",{"intro":361,"signals":362},"This often comes up when competitors launch AI features or users start asking what AI could mean in the product.\n",[363,366,369,372],{"title":364,"text":365},"There are several possible AI features","Summarising, generating, searching, agents and automation all seem interesting.\n",{"title":367,"text":368},"You don't want to build a gimmick","The feature has to demonstrably solve an existing user problem better.\n",{"title":370,"text":371},"The feature needs product data","AI has to be able to safely use information that is already in the software.\n",{"title":373,"text":374},"The existing architecture sets limits","You want to avoid one AI feature becoming a separate technical island.\n",{"intro":376},"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":378,"primaryOfferKey":108,"offerKeys":379,"caseKeys":380,"relatedUseCaseKeys":381},[104],[108,40,49],[],[302,35],{"title":383,"description":384},"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":388,"problem":150,"proof":389},"sq-connect-nodes","ls-system-connect","RSVufcPhYOQ6qeNYCFlOmKFGLqdfCrZhxfRPGRtSLjM",[],1792049910231]