[{"data":1,"prerenderedAt":465},["ShallowReactive",2],{"offer-ai-reality-check-en":3,"footer-vacatures-en":155,"content-by-keys-useCases-en-processen-automatiseren-met-ai,interne-ai-assistent-met-eigen-kennis,ai-idee-technisch-haalbaar,is-ai-nodig":156,"content-by-keys-cases-en-":464},{"id":4,"title":5,"body":6,"catalogue":13,"cta":24,"description":10,"extension":29,"fit":30,"hero":38,"key":41,"meta":42,"navigation":43,"next":44,"outcome":59,"path":71,"price":72,"process":75,"proof":90,"relations":104,"scope":113,"seo":141,"stem":144,"summary":145,"summaryPanel":146,"visuals":151,"__hash__":154},"offers_en\u002Foffers\u002Fai-reality-check.md","AI Reality Check",{"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 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",[17,18,19,20,21],"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":25,"body":26,"primaryCta":27,"local":28},"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","md",{"title":31,"intro":32,"useCaseKeys":33},"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",[34,35,36,37],"processen-automatiseren-met-ai","interne-ai-assistent-met-eigen-kennis","ai-idee-technisch-haalbaar","is-ai-nodig",{"headline":39,"intro":40,"primaryCta":27},"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","ai-reality-check",{},true,{"title":45,"items":46},"What happens next?",[47,51,55],{"title":48,"text":49,"offerKey":50},"Prove that it works first?","Then an AI Prototype Sprint is often the logical next step.\n","ai-prototype-sprint",{"title":52,"text":53,"offerKey":54},"Keep building yourself?","Then a Vibe Code Kickstart lets us set up the technical foundation properly.\n","vibe-code-kickstart",{"title":56,"text":57,"offerKey":58},"Scope already clear enough?","Then we can go straight to Build & Integrate.\n","build-integrate",{"title":60,"items":61},"Afterwards you have...",[62,65,68],{"title":63,"text":64},"A clear technical direction","You know which approach is promising and which options you are better off leaving alone.\n",{"title":66,"text":67},"The main uncertainties on the table","Integrations, data, reliability, risks and human oversight are made concrete enough to make a decision.\n",{"title":69,"text":70},"A logical next step","You know whether to build, buy, integrate, validate something first or stop.\n","\u002Foffers\u002Fai-reality-check",{"label":73,"type":74},"€1,950 excl. VAT","fixed",{"title":76,"steps":77},"How it works.",[78,81,84,87],{"title":79,"text":80},"Sharpen the question","We determine which decision you need to make and what a good result would be.\n",{"title":82,"text":83},"Understand the technical context","We look at processes, data, existing software, integrations and preconditions.\n",{"title":85,"text":86},"Test the options","We compare realistic routes on feasibility, risk and technical complexity.\n",{"title":88,"text":89},"Deliver decision + route","You get our conclusion, technical direction and a concrete next step.\n",{"title":91,"intro":92,"points":93,"caseKeys":103},"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",[94,97,100],{"title":95,"text":96},"Engineers look at the real system","We look at the AI idea and also at data, integrations, software and operations.\n",{"title":98,"text":99},"AI isn't always the answer","Plain automation, existing software or building nothing are real alternatives.\n",{"title":101,"text":102},"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":105,"useCaseKeys":107,"relatedOfferKeys":111,"caseKeys":112},[106],"starten-met-ai",[34,35,36,37,108,109,110],"ai-betrouwbaar-genoeg","zelf-bouwen-of-software-kopen","ai-toevoegen-aan-bestaand-product",[50,54,58],[],{"title":114,"intro":115,"activities":116,"deliverables":125,"exclusions":134},"What is included in this offer?","A bounded technical investigation with a concrete decision as the result.\n",{"intro":117,"items":118},"A senior engineer examines the actual question and the context around it.\n",[119,120,121,122,123,124],"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":126,"items":127},"Not a generic AI report, but a compact decision document that lets you move forward.\n",[19,128,129,130,131,132,133],"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":135,"items":136},"The Reality Check is meant to make a decision, not to build the whole system already.\n",[137,138,139,140],"Production-ready implementation","Fully worked-out UX or product design","Extensive organisation-wide AI strategy","Unbounded research into multiple processes or business units",{"title":142,"description":143},"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":147,"result":148,"duration":149,"nextStep":150},"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":23,"proof":152,"process":153},"sq-guardrails-check","ls-route-converge","yAAdWR1HeTz_g1hVnQ1w2rPY4-tRkksM8Ro6yjOmeok",[],[157,234,312,388],{"id":158,"title":159,"body":160,"description":10,"desiredOutcome":164,"extension":29,"hero":175,"key":34,"meta":177,"navigation":43,"path":178,"problem":179,"proof":191,"recognition":203,"recommendedApproach":218,"relations":220,"seo":225,"stem":228,"summary":229,"visuals":230,"__hash__":233},"useCases_en\u002Fuse-cases\u002Fprocessen-automatiseren-met-ai.md","Which processes are suitable for AI or automation?",{"type":7,"value":161,"toc":162},[],{"title":10,"searchDepth":11,"depth":11,"links":163},[],{"intro":165,"items":166},"You don't want to end up with a list of twenty AI ideas. You want to know where a technical investment actually makes sense.\n",[167,170,173],{"title":168,"text":169},"The best opportunities first","You know which processes combine enough value, volume and technical feasibility to explore further.\n",{"title":171,"text":172},"AI where it's needed","We distinguish between AI, regular automation, existing software and simple integrations.\n",{"title":21,"text":174},"You know which question to validate or build first, or which not to pursue any further.\n",{"intro":176},"Maybe you see opportunities for AI everywhere. Or so many that you don't know where to start. We help determine which processes are interesting, which technology fits them and which ideas you're better off leaving alone.\n",{},"\u002Fuse-cases\u002Fprocessen-automatiseren-met-ai",{"intro":180,"causes":181},"Not every annoying process is automatically a good AI problem. The value lies in the combination of process, data, technical feasibility and impact.\n",[182,185,188],{"title":183,"text":184},"AI is taken as the starting point too quickly","A fixed rule, integration or existing tool can sometimes solve the same problem more simply and reliably.\n",{"title":186,"text":187},"The real bottleneck is still unclear","What looks like manual work at first sight may be caused by a poor process or a missing integration.\n",{"title":189,"text":190},"Feasibility differs per task","Some tasks tolerate uncertainty, others require virtually error-free behaviour.\n",{"intro":192,"points":193},"We start with the problem you're trying to solve, and only then ask which AI model fits. Sometimes the best technical conclusion is that part of it doesn't need AI at all.\n",[194,197,200],{"title":195,"text":196},"Software engineering before AI hype","We also compare AI with automation, integrations and existing software.\n",{"title":198,"text":199},"Technical feasibility counts from the start","Data, systems and operational constraints are part of the assessment.\n",{"title":201,"text":202},"We can build the follow-up too","The advice doesn't have to be translated again by another technical party afterwards.\n",{"intro":204,"signals":205},"This question often comes up when there is clear inefficiency, but it's not yet clear which technical solution fits it best.\n",[206,209,212,215],{"title":207,"text":208},"Lots of repetitive knowledge work","Employees keep reading, checking, summarising or classifying similar information.\n",{"title":210,"text":211},"Information is copied over by hand","Data moves via people from email, documents or spreadsheets into other systems.\n",{"title":213,"text":214},"There are many AI ideas at once","Different teams see opportunities, but nobody knows which are the most interesting technically and commercially.\n",{"title":216,"text":217},"You don't want to automate for the sake of it","The solution has to demonstrably reduce time, errors or friction.\n",{"intro":219},"In an AI Reality Check we examine up to three ideas or one concrete process. We assess value, technical feasibility, data, integrations, risks and build versus buy. If one part then needs to be proven in practice, an AI Prototype Sprint can be the logical next step.\n",{"buyerTriggerKeys":221,"primaryOfferKey":41,"offerKeys":222,"caseKeys":223,"relatedUseCaseKeys":224},[106],[41,50],[],[37,109],{"title":226,"description":227},"Which processes are suitable for AI or automation? | 9to5 software","Find out which business processes are suitable for AI or automation. 9to5 examines processes, systems, data and technical feasibility.\n","use-cases\u002Fprocessen-automatiseren-met-ai","You see opportunities to make manual work smarter, but first want to know where AI actually adds value and where regular automation fits better.\n",{"hero":231,"problem":23,"proof":232},"sq-process-steps","ls-workflow-flow","Z9mZqpG5Qyu0wkGGaYeR4PecFC7nvQXcfsPeGf6UX80",{"id":235,"title":236,"body":237,"description":10,"desiredOutcome":241,"extension":29,"hero":253,"key":35,"meta":255,"navigation":43,"path":256,"problem":257,"proof":269,"recognition":281,"recommendedApproach":296,"relations":298,"seo":303,"stem":306,"summary":307,"visuals":308,"__hash__":311},"useCases_en\u002Fuse-cases\u002Finterne-ai-assistent-met-eigen-kennis.md","Can we build an internal AI assistant with our own knowledge?",{"type":7,"value":238,"toc":239},[],{"title":10,"searchDepth":11,"depth":11,"links":240},[],{"intro":242,"items":243},"You first want to know whether an internal assistant can really become usable before having a complete system built.\n",[244,247,250],{"title":245,"text":246},"Know what is technically feasible","You get insight into which sources are usable and how AI could get access to them.\n",{"title":248,"text":249},"Understand what reliable enough means","We determine where source references, validation or human review are needed.\n",{"title":251,"text":252},"A realistic solution direction","You know how data, retrieval, permissions and user experience can come together technically.\n",{"intro":254},"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",{},"\u002Fuse-cases\u002Finterne-ai-assistent-met-eigen-kennis",{"intro":258,"causes":259},"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",[260,263,266],{"title":261,"text":262},"Internal knowledge is rarely neatly structured","Documents differ in quality, freshness and format.\n",{"title":264,"text":265},"More context is not automatically better","Too much irrelevant information can actually make answers worse.\n",{"title":267,"text":268},"Access remains a security matter","The assistant must not become a new route to information a user normally may not see.\n",{"intro":270,"points":271},"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",[272,275,278],{"title":273,"text":274},"We start with the information","First understand what knowledge is available and what users want to do with it.\n",{"title":276,"text":277},"Permissions belong in the design","Security is not treated as a later add-on.\n",{"title":279,"text":280},"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":282,"signals":283},"This usually comes up when knowledge does exist, but is hard to find or widely spread across the organisation.\n",[284,287,290,293],{"title":285,"text":286},"Employees keep asking the same questions","Answers sit in documents, manuals, SharePoint, databases or with experienced colleagues.\n",{"title":288,"text":289},"Search returns too much noise","People know the information exists somewhere, but can't quickly find the right passage or version.\n",{"title":291,"text":292},"A general chatbot is not enough","Good answers require knowledge that is not public or generally available.\n",{"title":294,"text":295},"Permissions make it complicated","Not every employee may see the same documents or data.\n",{"intro":297},"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":299,"primaryOfferKey":41,"offerKeys":300,"caseKeys":301,"relatedUseCaseKeys":302},[106],[41,50],[],[108,110],{"title":304,"description":305},"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":309,"problem":152,"proof":310},"sq-knowledge-filter","ls-system-connect","rPps0CfPhGKhgluvbpO1IUJG7v8sTi68GVuZUyI1cG0",{"id":313,"title":314,"body":315,"description":10,"desiredOutcome":319,"extension":29,"hero":331,"key":36,"meta":333,"navigation":43,"path":334,"problem":335,"proof":347,"recognition":359,"recommendedApproach":374,"relations":376,"seo":381,"stem":384,"summary":385,"visuals":386,"__hash__":387},"useCases_en\u002Fuse-cases\u002Fai-idee-technisch-haalbaar.md","Is our AI idea technically feasible?",{"type":7,"value":316,"toc":317},[],{"title":10,"searchDepth":11,"depth":11,"links":318},[],{"intro":320,"items":321},"You want enough technical evidence to decide whether it is wise to invest time and budget in the idea.\n",[322,325,328],{"title":323,"text":324},"A clear feasibility verdict","You know which parts are realistic and where fundamental uncertainty remains.\n",{"title":326,"text":327},"The biggest technical risks","Data, integrations, reliability and operational limitations are made explicit.\n",{"title":329,"text":330},"A logical validation step","You know what still needs testing before you responsibly build in full.\n",{"intro":332},"An idea can sound logical on paper and still turn out to be technically hard. Conversely, something can sound complicated while a small experiment quickly brings clarity. We help you see that difference early.\n",{},"\u002Fuse-cases\u002Fai-idee-technisch-haalbaar",{"intro":336,"causes":337},"Technical feasibility is more than the question whether a model can perform the task successfully once.\n",[338,341,344],{"title":339,"text":340},"Demo and production are different requirements","An impressive example says little about consistent behaviour at scale.\n",{"title":342,"text":343},"Your own data matters a lot","The quality and availability of information can make or break the solution.\n",{"title":345,"text":346},"Integrations can be decisive","Sometimes the AI part is simple and the real complexity sits in existing systems.\n",{"intro":348,"points":349},"We look at this as engineers. Whether a concept sounds interesting matters less than what it technically takes to make it work reliably enough.\n",[350,353,356],{"title":351,"text":352},"Risks visible early","You discover technical showstoppers before the full project has started.\n",{"title":354,"text":355},"A no is also an answer","A Reality Check may also conclude that the idea is not wise in this form.\n",{"title":357,"text":358},"A concrete technical route","If it is promising, you know which parts then need to be proven or built.\n",{"intro":360,"signals":361},"This question usually comes up when the problem is clear, but the technical possibilities have not yet been sufficiently proven.\n",[362,365,368,371],{"title":363,"text":364},"A vendor says it can be done","But you want to know independently under which conditions that really holds.\n",{"title":366,"text":367},"The demos online look convincing","It's just unclear whether the same technology works with your own data and requirements.\n",{"title":369,"text":370},"One technical part determines the project","For example accuracy, latency, integration or processing of specific documents.\n",{"title":372,"text":373},"The investment after this step is large","You want to reduce uncertainty before having a full product built.\n",{"intro":375},"The AI Reality Check makes the technical assumptions explicit and determines which are plausible and which are not. If one crucial assumption can't be answered on paper yet, an AI Prototype Sprint can validate it in practice.\n",{"buyerTriggerKeys":377,"primaryOfferKey":41,"offerKeys":378,"caseKeys":379,"relatedUseCaseKeys":380},[106],[41,50],[],[108,37],{"title":382,"description":383},"Is our AI idea technically feasible? | 9to5 software","Have the technical feasibility of your AI idea assessed. 9to5 examines models, data, integrations, risks and a realistic solution direction.\n","use-cases\u002Fai-idee-technisch-haalbaar","You have a concrete AI idea and want to know whether it can really be built with the available models, data, systems and constraints.\n",{"hero":23,"problem":152,"proof":153},"nXhvMZpj-Ze0VcqpMSJ12RYkFCrsaViqtrzaF2WVJiI",{"id":389,"title":390,"body":391,"description":10,"desiredOutcome":395,"extension":29,"hero":407,"key":37,"meta":409,"navigation":43,"path":410,"problem":411,"proof":423,"recognition":435,"recommendedApproach":450,"relations":452,"seo":457,"stem":460,"summary":461,"visuals":462,"__hash__":463},"useCases_en\u002Fuse-cases\u002Fis-ai-nodig.md","Do we need AI for our idea?",{"type":7,"value":392,"toc":393},[],{"title":10,"searchDepth":11,"depth":11,"links":394},[],{"intro":396,"items":397},"You want the technically simplest solution that solves the real problem well enough. Even if there turns out to be no AI in it.\n",[398,401,404],{"title":399,"text":400},"Clarity about the real need","We sharpen which problem the user or organisation is actually trying to solve.\n",{"title":402,"text":403},"Alternatives side by side","AI is compared with rules, search functionality, automation, integrations and existing software.\n",{"title":405,"text":406},"No unnecessary complexity","You only choose AI when the benefits outweigh the extra uncertainty, cost and maintenance.\n",{"intro":408},"AI is a means, not a mandatory part of a modern software project. Sometimes a model delivers exactly the flexibility you need. Sometimes it mainly makes a problem more expensive, slower and less predictable.\n",{},"\u002Fuse-cases\u002Fis-ai-nodig",{"intro":412,"causes":413},"AI lowers the barrier to automating complex tasks, but at the same time introduces uncertainty that regular software often doesn't have.\n",[414,417,420],{"title":415,"text":416},"Technology is chosen before the problem","So later you try to make the process fit the chosen solution.\n",{"title":418,"text":419},"Non-determinism has a price","Validation, monitoring and error handling become more important as soon as model output is part of the workflow.\n",{"title":421,"text":422},"Existing solutions are forgotten","An API, integration or standard software can sometimes deliver the same result faster and cheaper.\n",{"intro":424,"points":425},"9to5 software would rather earn from a good technical decision than from unnecessary complexity. We build AI, but have no interest in AI as the default answer to every problem.\n",[426,429,432],{"title":427,"text":428},"Software engineers, not just AI consultants","That's why we can also seriously assess simpler technical alternatives.\n",{"title":430,"text":431},"Build, buy or don't build","Not building is a valid technical conclusion.\n",{"title":433,"text":434},"No automatic follow-up engagement","If the Reality Check gives enough of an answer, nothing needs to follow.\n",{"intro":436,"signals":437},"This is an especially good question when AI was named as the solution early on, while the problem itself hasn't been worked out technically yet.\n",[438,441,444,447],{"title":439,"text":440},"The project started with \"we need to do something with AI\"","There is enthusiasm about the technology, but the concrete problem is still vague.\n",{"title":442,"text":443},"The task seems fairly predictable","You wonder whether regular rules or existing software wouldn't be enough.\n",{"title":445,"text":446},"Reliability is extremely important","Variable model output may be undesirable for the core of the process.\n",{"title":448,"text":449},"You want to avoid vendor and model complexity","AI should only be added when that dependency really delivers something.\n",{"intro":451},"The AI Reality Check deliberately compares several technical routes. The outcome can be AI, a combination of AI and regular software, or simply: don't build with AI. Only if AI really adds something do we then determine a logical next step.\n",{"buyerTriggerKeys":453,"primaryOfferKey":41,"offerKeys":454,"caseKeys":455,"relatedUseCaseKeys":456},[106],[41],[],[36,109],{"title":458,"description":459},"Do we need AI for our idea? | 9to5 software","Not every problem needs AI. 9to5 compares AI with regular software, automation, integrations and existing solutions.\n","use-cases\u002Fis-ai-nodig","You have a problem or product idea and suspect AI could be a solution, but first want to know whether that is technically and practically the wisest route.\n",{"hero":23,"problem":231,"proof":153},"jY_wiimcbcgfU4hJZSC-MbkrLdhoPA3RYSnl1nrt4UI",[],1792049910231]