[{"data":1,"prerenderedAt":582},["ShallowReactive",2],{"footer-vacatures-en":3,"use-case-ai-toevoegen-aan-bestaande-software-en":4,"content-by-keys-offers-en-build-integrate,ai-prototype-sprint":94,"content-by-keys-useCases-en-agentic-workflows-bestaande-software,eigen-data-en-kennis-gebruiken-met-ai":370,"content-by-keys-cases-en-":525,"content-by-keys-buyerTriggers-en-ai-laten-bouwen":526},[],{"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\u002Fai-toevoegen-aan-bestaande-software.md","Add AI to existing software, an app or SaaS",{"type":8,"value":9,"toc":10},"minimark",[],{"title":11,"searchDepth":12,"depth":12,"links":13},"",2,[],{"intro":15,"items":16},"The new AI functionality should feel like part of the product, not like a chatbot glued on later.\n",[17,20,23],{"title":18,"text":19},"AI fits the existing user flow","The feature supports what users are already trying to do.\n",{"title":21,"text":22},"The existing architecture stays manageable","AI is integrated without needlessly adding a second technical world.\n",{"title":24,"text":25},"Production risks are covered","Costs, latency, errors and uncertain model behaviour get a place in the design.\n","md",{"intro":28},"You don't need to build a completely new AI product. Often most of the value lies in one smart feature inside software users already know. We make sure that feature fits the existing product technically.\n","ai-toevoegen-aan-bestaande-software",{},true,"\u002Fuse-cases\u002Fai-toevoegen-aan-bestaande-software",{"intro":34,"causes":35},"Integrating a standalone AI API is easy. Designing AI as a reliable part of existing product software is another story.\n",[36,39,42],{"title":37,"text":38},"Existing software already has rules","Data, permissions, business logic and user expectations are largely fixed.\n",{"title":40,"text":41},"AI introduces other failure modes","Latency, incorrect output and variable costs have to be handled in existing flows.\n",{"title":43,"text":44},"The new feature can end up too detached","A separate AI system quickly leads to duplicated data and logic.\n",{"intro":46,"points":47},"Understanding and extending existing software is a big part of our work. So AI doesn't need a greenfield to be built well.\n",[48,51,54],{"title":49,"text":50},"Understand the existing architecture first","We design the AI feature around what already works.\n",{"title":52,"text":53},"Integration over isolation","Data, auth and business logic are not duplicated unnecessarily.\n",{"title":55,"text":56},"Production from the design onwards","Reliability and operational consequences count before the feature goes live.\n",{"intro":58,"signals":59},"The problem is usually not how to call a model, but how the new functionality should relate to the existing product.\n",[60,63,66,69],{"title":61,"text":62},"The feature needs existing product data","AI has to work with users, documents or other information already in the product.\n",{"title":64,"text":65},"Permissions must simply keep working","AI must not suddenly expose information or actions that are protected elsewhere.\n",{"title":67,"text":68},"The existing UX should stay in charge","Users want a better workflow, not necessarily a chat interface.\n",{"title":70,"text":71},"You don't want to rebuild the current product","The AI layer has to connect to what already works well technically.\n",{"intro":73},"With Build & Integrate we add the AI functionality to the existing codebase and systems in a targeted way. When the new component is technically risky or still uncertain in substance, an AI Prototype Sprint can first prove the core.\n",{"buyerTriggerKeys":75,"primaryOfferKey":77,"offerKeys":78,"caseKeys":80,"relatedUseCaseKeys":81},[76],"ai-laten-bouwen","build-integrate",[77,79],"ai-prototype-sprint",[],[82,83],"agentic-workflows-bestaande-software","eigen-data-en-kennis-gebruiken-met-ai",{"title":85,"description":86},"Add AI to existing software or SaaS | 9to5 software","Add AI features (ChatGPT, Claude, OpenAI API) to existing software? 9to5 designs, builds and integrates AI into existing apps and SaaS products.\n","use-cases\u002Fai-toevoegen-aan-bestaande-software","The product already exists. Now you want to add AI functionality without losing sight of the existing architecture, user experience and reliability.\n",{"hero":90,"problem":91,"proof":92},"sq-connect-nodes","sq-foundation-build","ls-system-connect","uIjq2lxyg3PeXyGhq82YeTX4j_ALcHznMSqDHe95r9k",[95,238],{"id":96,"title":97,"body":98,"catalogue":102,"cta":112,"description":11,"extension":26,"fit":117,"hero":124,"key":77,"meta":127,"navigation":31,"next":128,"outcome":140,"path":152,"price":153,"process":156,"proof":171,"relations":185,"scope":196,"seo":226,"stem":229,"summary":230,"summaryPanel":231,"visuals":236,"__hash__":237},"offers_en\u002Foffers\u002Fbuild-integrate.md","Build & Integrate",{"type":8,"value":99,"toc":100},[],{"title":11,"searchDepth":12,"depth":12,"links":101},[],{"audience":103,"promise":104,"deliverables":105,"note":111,"visual":92},"For organisations that know what needs to be built and want 9to5 to build and integrate it professionally.\n","Not an AI demo, but something that works with your real software, data, processes and people.\n",[106,107,108,109,110],"Architecture and production-ready implementation","Integrations with existing software and data","Permissions, error handling and human oversight","Tests, monitoring and deployment where relevant","Documentation and handover to your team","We define the scope together first, then agree a fixed price for the result.\n",{"title":113,"body":114,"primaryCta":115,"local":116},"Do you know what needs to be built?","Share the desired outcome and your existing technical context with us. If the scope is clear, we turn it into a fixed engagement.\n","Discuss what you need built","AI engineering and software implementation from Delft, for organisations in Delft, The Hague, Rotterdam and the surrounding area.\n",{"title":118,"intro":119,"useCaseKeys":120},"When is this offer a fit?","When the desired outcome is clear enough to scope a professional implementation.\n",[121,122,123,29],"ai-agent-laten-bouwen","interne-ai-assistent-laten-ontwikkelen","bedrijfsproces-automatiseren-met-ai",{"headline":125,"intro":126,"primaryCta":115},"Not an AI demo, but something that works with your real software, data and people.","The problem and the desired direction are clear. Now it has to be built properly. We take on architecture, AI, integrations and production-grade engineering as one whole and hand it back over afterwards.\n",{},{"title":129,"items":130},"What happens next?",[131,134,137],{"title":132,"text":133},"You take it over","That is what we work towards. We don't want you to keep needing us.\n",{"title":135,"text":136},"A new bounded question comes up later","Then we scope it again instead of automatically keeping capacity running.\n",{"title":138,"text":139,"offerKey":79},"Still too much technical uncertainty?","Then an AI Prototype Sprint or Reality Check before Build & Integrate may be wiser.\n",{"title":141,"items":142},"Afterwards you have...",[143,146,149],{"title":144,"text":145},"Working software instead of a demo","The agreed functionality works end to end within the real technical environment.\n",{"title":147,"text":148},"Reliable integration with what is already there","Data, systems, permissions and human processes are part of the solution.\n",{"title":150,"text":151},"A product you can hand over","Architecture, code and key choices are documented well enough to continue independently.\n","\u002Foffers\u002Fbuild-integrate",{"label":154,"type":155},"Fixed price after scoping","scoped",{"title":157,"steps":158},"How it works.",[159,162,165,168],{"title":160,"text":161},"Scope + success criteria","We make exact enough what needs to work to agree on responsibility and price.\n",{"title":163,"text":164},"Architecture + integrations","We determine how AI, data, existing software, permissions and users come together.\n",{"title":166,"text":167},"Build + test","We implement the hard part and also test errors, edge cases and reliability.\n",{"title":169,"text":170},"Go live + hand over","We bring the solution to production in a controlled way, document it and hand it over.\n",{"title":172,"intro":173,"points":174,"caseKeys":184},"Why should you believe this?","AI only becomes truly valuable when it works together with existing software, data and business processes. That is simply serious software engineering.\n",[175,178,181],{"title":176,"text":177},"The same people advise and build","Advice and execution sit with the same technical people.\n",{"title":179,"text":180},"Integrations are core work","We don't treat APIs, databases, auth and existing systems as an afterthought around the AI.\n",{"title":182,"text":183},"Production and handover are part of it","The work isn't done when the demo works, but when the solution is manageable.\n",[],{"buyerTriggerKeys":186,"useCaseKeys":188,"relatedOfferKeys":191,"caseKeys":195},[76,187],"vastgelopen-met-ai",[121,122,123,29,83,189,82,190],"documenten-en-mails-automatisch-verwerken","tools-vervangen-door-maatwerkflow",[192,79,193,194],"ai-reality-check","vibe-code-rescue-audit","rescue-sprint",[],{"title":197,"intro":198,"activities":199,"deliverables":210,"exclusions":219},"What is included in this offer?","The scope is defined up front. Then you get a fixed price for the agreed result.\n",{"intro":200,"items":201},"The exact work differs per engagement, but we take responsibility for the complete technical result.\n",[202,203,204,205,206,207,208,209],"Making scope and success criteria concrete","Designing the architecture","Implementing AI functionality or an agent flow","Building integrations with existing software and data","Setting up authentication, authorisation and data flows properly","Tests, evals, monitoring and error handling where relevant","Deployment and production setup","Documentation and technical handover",{"intro":211,"items":212},"The deliverables are defined per scope and are part of the fixed price.\n",[213,214,215,216,217,218],"Working agreed functionality","Production-grade implementation","Required integrations","Relevant tests and quality checks","Technical documentation","Deployment and handover as scoped",{"intro":220,"items":221},"No unbounded consultancy and no feature list that keeps growing afterwards.\n",[222,223,224,225],"Functionality outside the agreed scope","Ongoing retainer as a standard component","External model, API and hosting costs","Additional work without a new explicit scope and price",{"title":227,"description":228},"Have AI built and integrated | Build & Integrate | 9to5 software","Have an AI agent, internal tool, MCP integration or AI feature built with Claude or ChatGPT\u002FOpenAI and professionally integrated with your software, data and processes.\n","offers\u002Fbuild-integrate","You know what needs to be built. We build it professionally, connect it to your existing software and make sure it can be handed over production-ready.\n",{"qualifier":232,"result":233,"duration":234,"nextStep":235},"Fixed price after joint scoping","Working, integrated software your team can take over","Set in the scope","Handover to your team, or further development together",{"hero":90,"proof":91,"process":92},"jlXsSL_a-hghGaOxvEHbZ2ARDUkoaAlfXaEkKLzQpSQ",{"id":239,"title":240,"body":241,"catalogue":245,"cta":256,"description":11,"extension":26,"fit":261,"hero":267,"key":79,"meta":270,"navigation":31,"next":271,"outcome":282,"path":293,"price":294,"process":297,"proof":311,"relations":324,"scope":330,"seo":357,"stem":360,"summary":361,"summaryPanel":362,"visuals":367,"__hash__":369},"offers_en\u002Foffers\u002Fai-prototype-sprint.md","AI Prototype Sprint",{"type":8,"value":242,"toc":243},[],{"title":11,"searchDepth":12,"depth":12,"links":244},[],{"audience":246,"promise":247,"deliverables":248,"note":254,"visual":255},"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",[249,250,251,252,253],"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":257,"body":258,"primaryCta":259,"local":260},"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":118,"intro":262,"useCaseKeys":263},"When the idea is clear enough to build, but still needs to be proven before you go to production.\n",[264,265,266],"ai-prototype-laten-bouwen","ai-betrouwbaar-genoeg","ai-toevoegen-aan-bestaand-product",{"headline":268,"intro":269,"primaryCta":259},"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":129,"items":272},[273,276,279],{"title":274,"text":275,"offerKey":77},"The prototype proves the direction","Then Build & Integrate can deliver the production version.\n",{"title":277,"text":278},"The outcome calls for adjustment","Then you have learned concretely which technical route doesn't work, without building a complete product first.\n",{"title":280,"text":281,"offerKey":193},"The prototype has become production itself","If an experiment already has real users and needs professionalising, the Rescue route is a better fit.\n",{"title":141,"items":283},[284,287,290],{"title":285,"text":286},"A working end-to-end prototype","The key user flow actually works with the chosen AI technology and integrations.\n",{"title":288,"text":289},"Technical assumptions are tested","You know better where quality, reliability and integrations are or aren't feasible.\n",{"title":291,"text":292},"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":295,"type":296},"€6,950 excl. VAT","fixed",{"title":157,"steps":298},[299,302,305,308],{"title":300,"text":301},"Define use case + success criterion","We pin down which technical assumption this Sprint needs to prove.\n",{"title":303,"text":304},"Set up architecture + flow","We choose the shortest technical route that validates the real problem.\n",{"title":306,"text":307},"Build + evaluate","We deliver the end-to-end flow and test where the solution is fragile.\n",{"title":309,"text":310},"Demo + production advice","We show what works, what is still uncertain and what a next phase needs.\n",{"title":172,"intro":312,"points":313,"caseKeys":323},"A good prototype is more than a pretty click-through demo. It answers the technical question that decides whether the concept can work.\n",[314,317,320],{"title":315,"text":316},"We test the real thing","We test real integrations, data flows and model behaviour where relevant.\n",{"title":318,"text":319},"Small enough to learn","We keep you from building half a product before the key assumption is proven.\n",{"title":321,"text":322},"Ready for a real follow-up decision","Continuing, adjusting or stopping can all be a good outcome.\n",[],{"buyerTriggerKeys":325,"useCaseKeys":327,"relatedOfferKeys":328,"caseKeys":329},[326,76],"starten-met-ai",[264],[192,77,193],[],{"title":197,"intro":331,"activities":332,"deliverables":343,"exclusions":350},"One clear use case, one primary flow and a controlled amount of technology around it.\n",{"intro":333,"items":334},"We build enough to test the real technical risk, without building the complete end product right away.\n",[335,336,337,338,339,340,341,342],"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":344,"items":345},"A tangible prototype plus enough technical clarity for a follow-up decision.\n",[250,346,347,348,217,349],"Technical architecture direction","Validated core flow","Demo of the result","Advice for production and next steps",{"intro":351,"items":352},"A Prototype Sprint proves the core. Production hardening is a separate step.\n",[353,354,355,356],"Full production guarantees or SLAs","Extensive scalability and operationalisation","Unbounded integrations or multiple main flows","Full product development beyond the agreed use case",{"title":358,"description":359},"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":363,"result":364,"duration":365,"nextStep":366},"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":91,"proof":368,"process":255},"sq-process-steps","WOLcKM4lFt5312Z6kkvWsCyd0jk_equXhLoXFeu1UMg",[371,448],{"id":372,"title":373,"body":374,"description":11,"desiredOutcome":378,"extension":26,"hero":390,"key":82,"meta":392,"navigation":31,"path":393,"problem":394,"proof":406,"recognition":418,"recommendedApproach":433,"relations":435,"seo":440,"stem":443,"summary":444,"visuals":445,"__hash__":447},"useCases_en\u002Fuse-cases\u002Fagentic-workflows-bestaande-software.md","Extend existing software with agentic workflows",{"type":8,"value":375,"toc":376},[],{"title":11,"searchDepth":12,"depth":12,"links":377},[],{"intro":379,"items":380},"You want to make an existing workflow smarter without redesigning the rest of the product.\n",[381,384,387],{"title":382,"text":383},"Handle more work automatically","Within clear boundaries, the workflow can gather information and take actions itself.\n",{"title":385,"text":386},"Existing software stays in charge","The agent uses current data, APIs, auth and business logic.\n",{"title":388,"text":389},"Autonomy stays controllable","Critical steps can be validated or put in front of a user.\n",{"intro":391},"Not every product needs to become an AI-first product. Sometimes a single agentic workflow is exactly enough: an existing process in which software independently performs a few smart steps before a user takes over again.\n",{},"\u002Fuse-cases\u002Fagentic-workflows-bestaande-software",{"intro":395,"causes":396},"Adding an agent to existing software is mainly an architecture question: how much freedom does it get and which existing rules stay in charge?\n",[397,400,403],{"title":398,"text":399},"The agent has to work within existing domain rules","AI should not re-implement existing business logic in a different way.\n",{"title":401,"text":402},"Tool use has to be safe","A wrong answer is different from a wrong write or delete action.\n",{"title":404,"text":405},"State has to stay explicit","Long workflows need to know what has already happened and what still has to happen.\n",{"intro":407,"points":408},"Agentic workflows sit where AI meets existing software. That is where our experience with software architecture, integrations and AI engineering comes together.\n",[409,412,415],{"title":410,"text":411},"Building on what exists","The agent builds on existing systems and rules.\n",{"title":413,"text":414},"Autonomy within limits","Tools, permissions and human-in-the-loop are designed deliberately.\n",{"title":416,"text":417},"Built for production too","Retries, logging, state and error handling are part of the build.\n",{"intro":419,"signals":420},"Agentic workflows pay off most when an existing feature currently takes several manual or half-manual steps.\n",[421,424,427,430],{"title":422,"text":423},"A user has to take many intermediate steps","Fetching, comparing and passing on information takes unnecessary interaction.\n",{"title":425,"text":426},"The next step depends on context","A fixed workflow is too rigid, but full human handling is not always necessary.\n",{"title":428,"text":429},"The software already has usable APIs","Many of the required actions already exist technically, but are now triggered by hand.\n",{"title":431,"text":432},"Full autonomy is not required","The workflow may run on its own until a logical checkpoint or decision moment.\n",{"intro":434},"With Build & Integrate we design the agentic workflow as an extension of the existing software. We reuse current APIs and domain logic where possible and only build the new orchestration layer that is needed.\n",{"buyerTriggerKeys":436,"primaryOfferKey":77,"offerKeys":437,"caseKeys":438,"relatedUseCaseKeys":439},[76],[77,79],[],[121,29],{"title":441,"description":442},"Add agentic workflows to existing software | 9to5 software","Add agentic workflows or AI agents (Claude, OpenAI) to existing software? 9to5 builds controlled agent flows around your data, APIs and MCP tools.\n","use-cases\u002Fagentic-workflows-bestaande-software","Your existing software already does a lot well, but some workflows could be smarter if AI gathered information and carried out next steps on its own.\n",{"hero":368,"problem":90,"proof":446},"ls-agentic-loop","l-LSbU1lEyqddJX5i8k4FjICWCwevjAMP1tFWXQP80s",{"id":449,"title":450,"body":451,"description":11,"desiredOutcome":455,"extension":26,"hero":467,"key":83,"meta":469,"navigation":31,"path":470,"problem":471,"proof":483,"recognition":495,"recommendedApproach":510,"relations":512,"seo":517,"stem":520,"summary":521,"visuals":522,"__hash__":524},"useCases_en\u002Fuse-cases\u002Feigen-data-en-kennis-gebruiken-met-ai.md","Make your own documents, data and knowledge usable for AI",{"type":8,"value":452,"toc":453},[],{"title":11,"searchDepth":12,"depth":12,"links":454},[],{"intro":456,"items":457},"You want to give AI access to relevant information of your own without putting everything into one uncontrollable data bin.\n",[458,461,464],{"title":459,"text":460},"Relevant information at the right moment","The AI only gets the context needed for the current task.\n",{"title":462,"text":463},"Sources stay traceable","Important output can be traced back to the underlying information.\n",{"title":465,"text":466},"Permissions and freshness stay intact","Users only see what they are allowed to see, and changes in sources can carry through into the solution.\n",{"intro":468},"A language model doesn't know your organisation by itself. If AI has to work with your own documents, product data or internal knowledge, that information has to be made available at the right moment and with the right permissions.\n",{},"\u002Fuse-cases\u002Feigen-data-en-kennis-gebruiken-met-ai",{"intro":472,"causes":473},"\"Giving our own data to AI\" sounds like one technical step, but usually consists of selection, retrieval, permissions and quality control.\n",[474,477,480],{"title":475,"text":476},"More context is not automatically better","A model actually gives worse answers when too much irrelevant information is included.\n",{"title":478,"text":479},"Sources have different structures","A PDF, a relational database and a ticketing system each call for a different approach.\n",{"title":481,"text":482},"Data permissions don't disappear with AI","Retrieval has to respect the same access that applies elsewhere in the organisation.\n",{"intro":484,"points":485},"The quality of an AI solution is often determined more by the context you provide than by the newest model. That's why we treat retrieval and data integration as fully-fledged engineering.\n",[486,489,492],{"title":487,"text":488},"Source and use case belong together","We don't just index everything because we can.\n",{"title":490,"text":491},"Existing permissions stay in charge","AI must not become a way around the access model.\n",{"title":493,"text":494},"No unnecessary platform layer","We only choose technical components the real problem requires.\n",{"intro":496,"signals":497},"This question arises when general AI works well, but business-specific context is missing.\n",[498,501,504,507],{"title":499,"text":500},"Knowledge is spread across many sources","Documents, databases and other systems each hold part of the context.\n",{"title":502,"text":503},"Copying into a prompt doesn't scale","Adding information by hand works for a demo, but not for structural use.\n",{"title":505,"text":506},"Information changes regularly","A static dataset quickly becomes outdated.\n",{"title":508,"text":509},"Not all knowledge is for everyone","Access rules from existing systems have to be preserved.\n",{"intro":511},"With Build & Integrate we build the technical layer that gives AI targeted access to your data and knowledge. If feasibility or answer quality is still uncertain, an AI Prototype Sprint can first validate the most important sources and use case.\n",{"buyerTriggerKeys":513,"primaryOfferKey":77,"offerKeys":514,"caseKeys":515,"relatedUseCaseKeys":516},[76],[77,79],[],[122,29],{"title":518,"description":519},"Use your own data and knowledge with AI | 9to5 software","Put your own documents and databases to work with AI? 9to5 builds RAG, retrieval, integrations and AI workflows around your existing information.\n","use-cases\u002Feigen-data-en-kennis-gebruiken-met-ai","The valuable information is already there. Now AI has to be able to work with it reliably, securely and practically.\n",{"hero":523,"problem":90,"proof":92},"sq-knowledge-filter","rQE5CNfHzt2_sDfHXKyafJGMwiRmtAudJssA1bEegxQ",[],[527],{"id":528,"title":529,"approach":530,"body":545,"cta":549,"description":11,"extension":26,"hero":552,"key":76,"label":554,"meta":555,"navigation":31,"path":556,"proof":557,"relations":569,"seo":575,"stem":578,"summary":579,"visuals":580,"__hash__":581},"buyerTriggers_en\u002Fbuyer-triggers\u002Fai-laten-bouwen.md","We know what we want. Now it needs to be built.",{"title":531,"steps":532},"From a clear scope to something that really works.",[533,536,539,542],{"title":534,"text":535},"Define the scope and success criteria","We make it clear what needs to work, who it is for and when it is good enough to go live.\n",{"title":537,"text":538},"Set up the architecture and integrations","We determine how AI, data, existing software, permissions and integrations should work together technically.\n",{"title":540,"text":541},"Build and test","We implement the difficult part and test more than the happy path, including reliability, errors and human oversight where needed.\n",{"title":543,"text":544},"Bring it into production and hand over","We launch it properly, document the important decisions and make sure your team can carry on independently.\n",{"type":8,"value":546,"toc":547},[],{"title":11,"searchDepth":12,"depth":12,"links":548},[],{"title":550,"body":551},"Know what needs to be built?","Explain what you need and which systems it should work with. We'll quickly tell you how to build it properly and what a logical first step would be.\n",{"intro":553},"You know what needs to be built. We help you build it properly, connect it to your existing systems and get it running reliably in production, using Claude, ChatGPT\u002FOpenAI, Codex or MCP where appropriate. We step in, build, document and hand over, without an open-ended staffing commitment.\n","Build with AI",{},"\u002Fbuyer-triggers\u002Fai-laten-bouwen",{"intro":558,"points":559},"AI is only really useful when it works with existing software, data and business processes. We build that as software engineering, not as a standalone demo.\n",[560,563,566],{"title":561,"text":562},"Integrations are central to our work","We treat APIs, databases, permissions and existing systems as core parts of the solution, not extras around the AI.\n",{"title":564,"text":565},"Reliable beyond the happy path","We also test errors, edge cases and human oversight where needed.\n",{"title":567,"text":568},"Production and handover are part of the job","The work is done when the solution is live, documented and manageable for your team.\n",{"useCaseKeys":570,"offerKeys":572,"caseKeys":574},[121,122,123,29,83,189,264,571,82,190],"product-goed-opzetten-voor-ai-development",[77,79,573],"vibe-code-kickstart",[],{"title":576,"description":577},"AI agent development and integration | 9to5 software","Have senior engineers build an AI agent, MCP integration, assistant or automation with Claude, ChatGPT\u002FOpenAI or Codex. From scope to production.\n","buyer-triggers\u002Fai-laten-bouwen","The problem and direction are clear. Now it needs to be built properly, integrated and brought into production.\n",{"hero":91,"proof":255,"approach":255},"ShT4oNEyuY5KbwbXvOuC2vEuCmku084FYvyfv89UAyM",1792049911813]