[{"data":1,"prerenderedAt":265},["ShallowReactive",2],{"case-\u002Fcase-studies\u002Fai-chatbot-for-customer-support":3,"case-related-\u002Fcase-studies\u002Fai-chatbot-for-customer-support":151},{"id":4,"title":5,"body":6,"client":121,"description":122,"extension":123,"industry":124,"meta":125,"metrics":126,"navigation":136,"ogDescription":137,"ogTitle":137,"order":138,"path":139,"relatedService":140,"seo":141,"stack":142,"stem":148,"summary":149,"__hash__":150},"caseStudies\u002Fcase-studies\u002Fai-chatbot-for-customer-support.md","AI Chatbot for Customer Support — Case Study",{"type":7,"value":8,"toc":111},"minimark",[9,13,18,22,28,32,40,60,63,67,100,104,107],[10,11],"case-study-hero",{":breadcrumbs":12},"[{\"label\":\"Home\",\"to\":\"\u002F\"},{\"label\":\"Case Studies\",\"to\":\"\u002Fcase-studies\"},{\"label\":\"AI Support Assistant\"}]",[14,15,17],"h2",{"id":16},"the-challenge","The challenge",[19,20,21],"p",{},"A growing B2B SaaS support team was answering the same product questions over and\nover. Knowledge was spread across a help center, internal policy docs, and the\nheads of a few senior agents. New hires took weeks to ramp, and response times\nclimbed during busy periods.",[23,24,25],"blockquote",{},[19,26,27],{},"The numbers below are representative of this type of engagement. Real figures\nare shared with prospective clients under NDA.",[14,29,31],{"id":30},"what-we-built","What we built",[19,33,34,35,39],{},"We built a ",[36,37,38],"strong",{},"retrieval-augmented knowledge assistant"," that:",[41,42,43,47,54,57],"ul",{},[44,45,46],"li",{},"Ingested the public help center, internal SOPs, and past resolved tickets",[44,48,49,50,53],{},"Answered questions ",[36,51,52],{},"with citations"," back to the source document",[44,55,56],{},"Enforced which content was customer-facing vs. internal-only",[44,58,59],{},"Surfaced \"answer gaps\" — questions the knowledge base couldn't answer well",[19,61,62],{},"The assistant was embedded both in the customer help widget and in the agents'\ninternal console.",[14,64,66],{"id":65},"the-approach","The approach",[68,69,70,76,82,88,94],"ol",{},[44,71,72,75],{},[36,73,74],{},"Content audit"," to find authoritative, up-to-date sources",[44,77,78,81],{},[36,79,80],{},"Ingestion pipeline"," with scheduled re-syncing",[44,83,84,87],{},[36,85,86],{},"Retrieval + re-ranking"," tuned against a labeled evaluation set",[44,89,90,93],{},[36,91,92],{},"Guardrails"," so the assistant defers instead of guessing",[44,95,96,99],{},[36,97,98],{},"Analytics"," on deflection, satisfaction, and content gaps",[14,101,103],{"id":102},"results","Results",[19,105,106],{},"By grounding answers in real content and measuring quality before launch, the\nassistant deflected a meaningful share of repetitive tickets and cut first\nresponse times — while giving agents faster, cited answers.",[14,108,110],{"id":109},"stack","Stack",{"title":112,"searchDepth":113,"depth":113,"links":114},"",3,[115,117,118,119,120],{"id":16,"depth":116,"text":17},2,{"id":30,"depth":116,"text":31},{"id":65,"depth":116,"text":66},{"id":102,"depth":116,"text":103},{"id":109,"depth":116,"text":110},"B2B SaaS company","How we built a RAG knowledge assistant that deflected repetitive support tickets and cut average response time for a SaaS support team.","md","Software \u002F SaaS",{},[127,130,133],{"label":128,"value":129},"Repetitive tickets deflected","~40%",{"label":131,"value":132},"Avg. first response time","−65%",{"label":134,"value":135},"Launch timeline","6 weeks",true,null,1,"\u002Fcase-studies\u002Fai-chatbot-for-customer-support","\u002Fservices\u002Frag-chatbot-development",{"title":5,"description":122},[143,144,145,146,147],"OpenAI","pgvector","Node.js","Nuxt","AWS (Lambda, S3, CloudFront)","case-studies\u002Fai-chatbot-for-customer-support","A RAG knowledge assistant grounded in the company's help center and policies, deflecting repetitive tickets and giving agents instant, cited answers.","zDlco-Jp0kd3zakd8lGPP7uPGjfIzZYHHt4_KYurcrs",{"id":152,"title":153,"body":154,"deliverables":220,"description":227,"extension":123,"faqs":228,"forWho":241,"icon":246,"meta":247,"name":248,"navigation":136,"ogDescription":137,"ogTitle":137,"order":116,"path":140,"seo":249,"stem":250,"tagline":251,"tech":252,"useCases":258,"__hash__":264},"services\u002Fservices\u002Frag-chatbot-development.md","RAG Chatbot & Knowledge Assistant Development",{"type":7,"value":155,"toc":212},[156,161,165,177,180,184,187,191,194,198,201,205,208],[157,158],"page-hero",{":breadcrumbs":159,"eyebrow":160},"[{\"label\":\"Home\",\"to\":\"\u002F\"},{\"label\":\"Services\",\"to\":\"\u002Fservices\"},{\"label\":\"RAG Chatbots\"}]","RAG Chatbot Development",[14,162,164],{"id":163},"answers-from-your-knowledge-not-guesses","Answers from your knowledge — not guesses",[19,166,167,168,171,172,176],{},"A ",[36,169,170],{},"retrieval-augmented generation (RAG)"," assistant connects a language model to\n",[173,174,175],"em",{},"your"," documents, so it answers real questions with real sources. It's the\ndifference between a chatbot that frustrates customers and a knowledge assistant\nyour team and customers actually trust.",[19,178,179],{},"We build the full pipeline — ingestion, retrieval, citations, permissions, and\nevaluation — and deploy it where your users already are: your website, an internal\nportal, or Slack\u002FTeams.",[14,181,183],{"id":182},"who-this-is-for","Who this is for",[19,185,186],{},"If your answers live in PDFs, manuals, policies, a help center, or a pile of\nsupport tickets, a RAG assistant turns that scattered knowledge into instant,\naccurate responses.",[14,188,190],{"id":189},"what-we-build","What we build",[19,192,193],{},"A production-grade assistant with the unglamorous parts done right: clean\ningestion, grounded retrieval, citations, access control, and measurable quality.",[14,195,197],{"id":196},"example-use-cases","Example use cases",[19,199,200],{},"Customer support, internal help desks, sales enablement, and document search are\nthe highest-ROI starting points.",[14,202,204],{"id":203},"how-we-work","How we work",[19,206,207],{},"Discovery → content audit → ingestion pipeline → retrieval & citations →\nevaluation → deploy → monitor question gaps → improve.",[14,209,211],{"id":210},"frequently-asked-questions","Frequently asked questions",{"title":112,"searchDepth":113,"depth":113,"links":213},[214,215,216,217,218,219],{"id":163,"depth":116,"text":164},{"id":182,"depth":116,"text":183},{"id":189,"depth":116,"text":190},{"id":196,"depth":116,"text":197},{"id":203,"depth":116,"text":204},{"id":210,"depth":116,"text":211},[221,222,223,224,225,226],"Document ingestion pipeline (PDFs, docs, wikis, tickets, databases)","Retrieval-augmented generation with source citations","Chat interface (web widget, internal portal, or Slack\u002FTeams)","Access controls and per-document permissions","Evaluation harness to measure answer quality","Analytics on questions, gaps, and deflection","We build retrieval-augmented (RAG) chatbots and knowledge assistants that answer questions accurately from your documents, manuals, and support data — with citations.",[229,232,235,238],{"q":230,"a":231},"How is a RAG chatbot different from a regular chatbot?","A regular chatbot follows scripted flows or guesses from a general model. A RAG assistant retrieves your actual documents at query time and answers from them with citations — so it stays accurate and current as your content changes.",{"q":233,"a":234},"Will it make things up?","We ground answers in your retrieved content, add citations, and configure the system to say 'I don't know' instead of guessing. We also run evaluations to catch hallucinations before launch.",{"q":236,"a":237},"Can it respect who is allowed to see what?","Yes. We can enforce per-document and per-role permissions so the assistant only answers from content a given user is allowed to access.",{"q":239,"a":240},"How do you keep it up to date?","The ingestion pipeline re-syncs your sources on a schedule or on change, so new and updated documents are reflected without retraining a model.",[242,243,244,245],"Businesses with manuals, policies, SOPs, or large document libraries","Support teams answering the same questions repeatedly","Sales teams that need instant answers from product and pricing docs","Internal teams onboarding staff who need fast, reliable answers","rag",{},"RAG Chatbots",{"title":153,"description":227},"services\u002Frag-chatbot-development","Knowledge assistants that answer from your documents — accurately, with sources.",[253,254,255,256,257],"OpenAI \u002F Anthropic \u002F Azure OpenAI","Vector databases (pgvector, Pinecone, Qdrant)","Python \u002F Node.js","Embeddings & re-ranking","AWS \u002F Azure \u002F GCP",[259,260,261,262,263],"Customer support assistant trained on your help center and policies","Internal employee assistant for HR, IT, and operations questions","Sales enablement bot answering from product and pricing materials","Document search across thousands of files with natural language","Compliance assistant grounded in current policy documents","DGQkdE6rZe73MAEaHQXxl69q7Q-9213h9LcWb4D4AO0",1782707468861]