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Integration","\u002Fen\u002Fservices\u002Ferp-integration","en\u002Fservices\u002Ferp-integration","erp-integration",{"title":120,"path":121,"stem":122,"translationKey":123,"icon":123},"Custom Development","\u002Fen\u002Fservices\u002Findividual-development","en\u002Fservices\u002Findividual-development","individual-development",{"title":125,"path":126,"stem":127,"translationKey":128,"icon":128},"System Integration","\u002Fen\u002Fservices\u002Fsystem-integration","en\u002Fservices\u002Fsystem-integration","system-integration",{"title":130,"path":131,"stem":132,"translationKey":133,"icon":133},"Migration & System Modernization","\u002Fen\u002Fservices\u002Fsystem-modernization-migration","en\u002Fservices\u002Fsystem-modernization-migration","system-modernization-migration",{"title":135,"path":136,"stem":137,"translationKey":138,"icon":138},"UX\u002FUI Design","\u002Fen\u002Fservices\u002Fux-ui-design","en\u002Fservices\u002Fux-ui-design","ux-ui-design",{},{"id":141,"title":142,"body":143,"description":722,"draft":83,"extension":723,"icon":98,"meta":724,"navigation":725,"path":96,"robots":72,"schemaOrg":726,"seo":739,"sitemap":744,"stem":97,"tags":748,"translationKey":98,"__hash__":749},"content_en\u002Fen\u002Fservices\u002Fai-integration.md","AI Integration, RAG and AI Agents for Companies",{"type":144,"value":145,"toc":700},"minimark",[146,149,154,158,161,164,167,170,174,177,180,199,202,204,208,221,224,227,230,238,241,255,275,277,281,284,287,290,295,298,302,305,309,312,316,319,322,324,328,339,342,345,347,351,354,357,363,377,383,397,404,406,410,413,416,419,442,445,447,451,454,510,513,515,519,522,525,528,548,550,554,557,560,577,580,582,586,589,592,615,627,629,633,636,653,656,658,662,665,668,671,676,678],[147,148,95],"h1",{"id":98},[150,151,153],"h2",{"id":152},"llms-create-real-value-when-they-are-embedded-into-real-processes","🤖 LLMs create real value when they are embedded into real processes",[155,156,157],"p",{},"A single language model can answer questions.",[155,159,160],{},"A meaningful AI integration does much more: it accesses company data,\nmakes decisions based on clear rules, calls APIs and returns results\ninto existing systems: the shop, the ERP, the support platform or an\ninternal tool.",[155,162,163],{},"That is where measurable value is created.",[155,165,166],{},"We integrate large language models, RAG systems and AI agents into\nexisting software landscapes. Cloud-based via providers like Anthropic\n(Claude), OpenAI or OpenRouter, or fully self-hosted on dedicated\nhardware. With a clear focus on data privacy, cost control and\nlong-term maintainability.",[168,169],"hr",{},[150,171,173],{"id":172},"️-when-ai-integration-is-economically-reasonable","⚙️ When AI integration is economically reasonable",[155,175,176],{},"Not every task needs an LLM. Classical software is often faster, cheaper\nand more predictable.",[155,178,179],{},"AI integration plays out its strengths especially when:",[181,182,183,187,190,193,196],"ul",{},[184,185,186],"li",{},"large amounts of unstructured data must be evaluated",[184,188,189],{},"natural language is required as input or output",[184,191,192],{},"knowledge from many scattered sources must be combined",[184,194,195],{},"decisions must follow flexible rules",[184,197,198],{},"recurring routine tasks can be automated",[155,200,201],{},"In these scenarios, the investment pays off. In other cases, classical\ninterfaces, workflows or scripts are often the better choice, and that\nis exactly what we will tell you.",[168,203],{},[150,205,207],{"id":206},"rag-systems-making-your-own-data-usable","🧩 RAG systems: making your own data usable",[209,210],"auto-image",{"src":211,"alt":212,"className":213,"preset":216,"width":217,"height":218,"fetchPriority":219,"loading":220},"\u002Fpages\u002Fservices\u002Fabstract-digital-network-with-glowing-lines.webp","AI integration and RAG",[214,215],"mx-auto","table","content",624,416,"high","eager",[155,222,223],{},"Retrieval Augmented Generation (RAG) combines language models with\ncontrolled, internal data sources.",[155,225,226],{},"Instead of letting the model answer alone, relevant knowledge is\nretrieved from a vector database and passed as targeted context.",[155,228,229],{},"This solves two key problems:",[181,231,232,235],{},[184,233,234],{},"hallucinations are significantly reduced",[184,236,237],{},"current and company-specific data becomes usable",[155,239,240],{},"Typical use cases include:",[181,242,243,246,249,252],{},[184,244,245],{},"internal knowledge bases and employee assistants (e.g. based on\nNotion)",[184,247,248],{},"technical support systems on top of product documentation",[184,250,251],{},"research tools across contracts, tickets and emails",[184,253,254],{},"product advisors in eCommerce based on internal data",[155,256,257,258,262,263,266,267,270,271,274],{},"On the technical side, we typically work with ",[259,260,261],"strong",{},"Qdrant"," as a vector\ndatabase, ",[259,264,265],{},"LangChain",", ",[259,268,269],{},"LlamaIndex"," or ",[259,272,273],{},"Paperclip"," for pipeline\nlogic and an LLM chosen per requirement, cloud or self-hosted.",[168,276],{},[150,278,280],{"id":279},"️-ai-agents-tasks-not-just-answers","🛠️ AI agents: tasks, not just answers",[155,282,283],{},"An agent is more than a chatbot.",[155,285,286],{},"It receives a goal and decides on its own which tools to use: calling\nAPIs, querying data, chaining steps and returning results.",[155,288,289],{},"Typical examples from real projects:",[291,292,294],"h3",{"id":293},"support-automation","Support automation",[155,296,297],{},"Read tickets, classify them, search the knowledge base, draft a\nresponse, escalate if needed.",[291,299,301],{"id":300},"shop-and-erp-workflows","Shop and ERP workflows",[155,303,304],{},"Validate orders, enrich master data, generate product texts, answer\nsupplier inquiries automatically.",[291,306,308],{"id":307},"back-office-automation","Back office automation",[155,310,311],{},"Extract data from PDFs, emails or Excel files and feed it back into\nexisting systems in a structured way.",[291,313,315],{"id":314},"research-and-analysis-agents","Research and analysis agents",[155,317,318],{},"Multi-step research across internal and external sources with a clear\naudit trail.",[155,320,321],{},"We deploy agents where they work faster or cheaper than manual\nprocesses, not as an end in itself.",[168,323],{},[150,325,327],{"id":326},"️-typical-architecture-of-an-ai-integration","🏗️ Typical architecture of an AI integration",[329,330,336],"pre",{"className":331,"code":333,"language":334,"meta":335},[332],"language-text","Business Platform\n│\n├─ Data sources (ERP, shop, PIM, Notion \u002F wiki, tickets, emails, files)\n├─ Indexing & embeddings\n├─ Vector database (Qdrant)\n├─ RAG \u002F agent layer (LangChain, LlamaIndex, Paperclip)\n├─ LLM (Claude \u002F GPT in the cloud, self-hosted e.g. Llama, Qwen, Mistral via Ollama \u002F vLLM)\n├─ Orchestration & workflows (n8n, custom services)\n└─ Integration with existing systems (APIs, webhooks, UIs)\n","text","",[337,338,333],"code",{"__ignoreMap":335},[155,340,341],{},"This architecture is intentionally modular.",[155,343,344],{},"Individual components can be replaced: switching models, scaling the\nvector database or replacing a provider with a self-hosted solution\nwithout rebuilding the entire application.",[168,346],{},[150,348,350],{"id":349},"️-cloud-llms-or-self-hosted","☁️ Cloud LLMs or self-hosted?",[155,352,353],{},"This is the most important decision in any AI integration.",[155,355,356],{},"We work with both and provide honest advice on what fits each case.",[155,358,359,362],{},[259,360,361],{},"Cloud models"," (Claude by Anthropic, GPT by OpenAI, additional models via OpenRouter as a gateway)",[181,364,365,368,371,374],{},[184,366,367],{},"currently leading for complex reasoning tasks",[184,369,370],{},"no infrastructure to operate, fast to start",[184,372,373],{},"cost scales per token with usage",[184,375,376],{},"data leaves your environment",[155,378,379,382],{},[259,380,381],{},"Self-hosted models"," (Llama, Qwen, Mistral and other open-source models, run e.g. via Ollama or vLLM)",[181,384,385,388,391,394],{},[184,386,387],{},"full data sovereignty",[184,389,390],{},"predictable cost based on hardware instead of tokens",[184,392,393],{},"lower latency within your network",[184,395,396],{},"higher requirements for hardware and operations",[155,398,399,400,403],{},"In many projects, a ",[259,401,402],{},"hybrid setup"," is the best choice: sensitive\nworkloads run locally, demanding reasoning tasks run in the cloud.",[168,405],{},[150,407,409],{"id":408},"️-hardware-planning-for-self-hosted-llms","🖥️ Hardware planning for self-hosted LLMs",[155,411,412],{},"Self-hosted models depend heavily on the right hardware.",[155,414,415],{},"We plan setups ranging from a small single-GPU server for internal\ntools up to multi-GPU machines for production inference under load.",[155,417,418],{},"Typical aspects of the planning:",[181,420,421,424,427,430,433,436,439],{},[184,422,423],{},"model choice (e.g. 7B, 13B, 70B, MoE architectures like Mixtral)",[184,425,426],{},"quantization (e.g. 4-bit, 8-bit) to reduce memory requirements",[184,428,429],{},"GPU selection (VRAM, bandwidth, power)",[184,431,432],{},"inference stack (Ollama, vLLM, llama.cpp)",[184,434,435],{},"scaling across multiple nodes",[184,437,438],{},"monitoring and load balancing",[184,440,441],{},"backup, update and model rollout strategy",[155,443,444],{},"We do not sugarcoat. If a use case does not fit the available hardware,\nwe say so and propose alternatives via cloud or hybrid setups.",[168,446],{},[150,448,450],{"id":449},"technology-stack","🧰 Technology stack",[155,452,453],{},"We deliberately work with a clear, controllable stack:",[181,455,456,462,468,474,480,486,492,498,504],{},[184,457,458,461],{},[259,459,460],{},"LLMs (cloud):"," Claude (Anthropic), GPT (OpenAI), additional models\nvia OpenRouter",[184,463,464,467],{},[259,465,466],{},"LLMs (self-hosted):"," Llama, Qwen, Mistral and other open-source\nmodels",[184,469,470,473],{},[259,471,472],{},"Inference runtimes:"," Ollama, vLLM, llama.cpp",[184,475,476,479],{},[259,477,478],{},"Vector database:"," Qdrant",[184,481,482,485],{},[259,483,484],{},"Agent & RAG frameworks:"," LangChain, LlamaIndex, Paperclip",[184,487,488,491],{},[259,489,490],{},"Typical data sources:"," Notion, internal wikis, ERP and shop\nsystems, ticket systems, mailboxes, file storage",[184,493,494,497],{},[259,495,496],{},"Workflow orchestration:"," n8n",[184,499,500,503],{},[259,501,502],{},"Backend:"," Symfony \u002F PHP, Spring Boot \u002F Java, Node.js, depending on\nthe existing system landscape",[184,505,506,509],{},[259,507,508],{},"Infrastructure:"," Docker, Kubernetes, Hetzner, Kubernetes ONE\n(Profihost), AWS",[155,511,512],{},"This keeps projects maintainable and evolvable, even without Kickbyte.",[168,514],{},[150,516,518],{"id":517},"data-privacy-security-and-control","🔐 Data privacy, security and control",[155,520,521],{},"AI integration almost always touches sensitive data.",[155,523,524],{},"That is why privacy and security are not an afterthought for us, but a\nstarting point.",[155,526,527],{},"Concrete building blocks:",[181,529,530,533,536,539,542,545],{},[184,531,532],{},"data classification before integration",[184,534,535],{},"clear separation between index and request data",[184,537,538],{},"GDPR-compliant hosting options, including Germany",[184,540,541],{},"logging and audit trails for all agent actions",[184,543,544],{},"configurable filters and guardrails",[184,546,547],{},"fully self-hosted setups without external APIs when needed",[168,549],{},[150,551,553],{"id":552},"️-challenges-in-ai-projects","⚠️ Challenges in AI projects",[155,555,556],{},"AI projects rarely fail because of the technology. They fail because of\nunclear goals and poor data quality.",[155,558,559],{},"Typical challenges:",[181,561,562,565,568,571,574],{},[184,563,564],{},"vague or overly broad use cases",[184,566,567],{},"fragmented or poor data",[184,569,570],{},"missing evaluation of quality and accuracy",[184,572,573],{},"runaway costs from inefficient prompts or models",[184,575,576],{},"weak integration into existing processes",[155,578,579],{},"We address these projects pragmatically: clear use case, fast prototype,\nmeasurable results, then production rollout.",[168,581],{},[150,583,585],{"id":584},"our-role-in-ai-projects","🧑‍💻 Our role in AI projects",[155,587,588],{},"We support companies along the full lifecycle of an AI integration.",[155,590,591],{},"Typical responsibilities include:",[181,593,594,597,600,603,606,609,612],{},[184,595,596],{},"use case evaluation and business case analysis",[184,598,599],{},"prototyping and proof of concept",[184,601,602],{},"architecture and model selection",[184,604,605],{},"building RAG systems and agents",[184,607,608],{},"integration into existing systems via APIs and workflows",[184,610,611],{},"hardware planning for self-hosted LLMs",[184,613,614],{},"operation, monitoring and continuous improvement",[155,616,617,618,622,623,626],{},"We combine AI expertise with years of experience in\n",[619,620,621],"a",{"href":121},"custom development"," and\n",[619,624,625],{"href":126},"system integration",". That combination\nis what makes the difference. AI without clean integration remains a\ntoy.",[168,628],{},[150,630,632],{"id":631},"when-ai-integration-makes-the-most-sense","🎯 When AI integration makes the most sense",[155,634,635],{},"AI integration is particularly valuable for companies that:",[181,637,638,641,644,647,650],{},[184,639,640],{},"hold large amounts of data in documents, emails, tickets or PIM\u002FERP",[184,642,643],{},"want to automate repetitive tasks",[184,645,646],{},"need to make internal knowledge more accessible",[184,648,649],{},"want to extend their shops, products or services with AI features",[184,651,652],{},"deliberately focus on data sovereignty and long-term independence",[155,654,655],{},"In all these cases, a clean AI integration delivers real and lasting\nvalue.",[168,657],{},[150,659,661],{"id":660},"ai-that-fits-your-business","🧠 AI that fits your business",[155,663,664],{},"AI is no longer an end in itself.",[155,666,667],{},"It is becoming a regular part of modern business processes: in\neCommerce, in the ERP, in support, in internal knowledge management.",[155,669,670],{},"The decisive factor is not the largest model, but the right combination\nof use case, model, data and integration.",[155,672,673],{},[259,674,675],{},"We build AI solutions that fit into existing systems, deliver\nmeasurable value and stay maintainable over time.",[168,677],{},[679,680,699],"nuxt-link",{"to":681,"className":682},"\u002Fen\u002Fcontact",[683,214,215,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698],"not-prose","text-white","px-4","py-1.5","text-base","font-semibold","leading-7","shadow-sm","ring-1","duration-300","ease","rounded-lg","bg-primary","ring-primary","hover:bg-secondary","hover:ring-secondary","\n👉 Talk to us about your AI project\n",{"title":335,"searchDepth":701,"depth":701,"links":702},2,[703,704,705,706,713,714,715,716,717,718,719,720,721],{"id":152,"depth":701,"text":153},{"id":172,"depth":701,"text":173},{"id":206,"depth":701,"text":207},{"id":279,"depth":701,"text":280,"children":707},[708,710,711,712],{"id":293,"depth":709,"text":294},3,{"id":300,"depth":709,"text":301},{"id":307,"depth":709,"text":308},{"id":314,"depth":709,"text":315},{"id":326,"depth":701,"text":327},{"id":349,"depth":701,"text":350},{"id":408,"depth":701,"text":409},{"id":449,"depth":701,"text":450},{"id":517,"depth":701,"text":518},{"id":552,"depth":701,"text":553},{"id":584,"depth":701,"text":585},{"id":631,"depth":701,"text":632},{"id":660,"depth":701,"text":661},"We integrate large language models, RAG systems and AI agents into existing business processes. With cloud LLMs or self-hosted infrastructure.","md",{},{"title":95},[727],{"name":95,"serviceType":95,"provider":728,"description":733,"areaServed":734,"@type":738},{"@id":729,"name":730,"url":731,"@type":732},"#identity","Kickbyte GmbH","https:\u002F\u002Fkickbyte.de","Organization","Consulting, development and integration of LLM-based applications, RAG systems and AI agents into existing business software and eCommerce platforms.",[735],{"name":736,"@type":737},"Worldwide","Place","Service",{"title":142,"description":740,"ogTitle":741,"ogDescription":742,"robots":743},"LLM integration, RAG systems and AI agents for eCommerce, ERP and business software, with cloud models or self-hosted open-source LLMs.","AI Integration, RAG and AI Agents - Kickbyte","We integrate LLMs, RAG and AI agents into existing systems. Cloud or on-premise. With a clear focus on data privacy and maintainability.","index,follow",{"loc":96,"lastmod":745,"changefreq":746,"priority":747},"2026-05-12","monthly",0.9,[],"UOuMqIwcMcCaOEgNVG8L1PGNpWDRCx9UVZTaTAZR7K4",1779873361360]