[{"data":1,"prerenderedAt":277},["ShallowReactive",2],{"work-all":3,"ill-generic\u002Fwork":276},[4,24,39,52,65,77,91,105,117,128,140,158,175,190,209,225,242,259],{"path":5,"title":6,"description":7,"industry":8,"services":9,"results":13,"cover":23,"coverAlt":23},"\u002Fwork\u002Fecommerce-support-assistant","ChatGPT-powered support assistant for an online retailer","A GPT-based assistant grounded in order data and help-centre content that now handles the majority of first-line customer inquiries.","Retail & E-commerce",[10,11,12],"ai-chatbots","rag-systems","generative-ai-llm",[14,17,20],{"value":15,"label":16},"80%","of inquiries resolved without an agent",{"value":18,"label":19},"60%","faster first response",{"value":21,"label":22},"+40%","customer satisfaction (CSAT)",null,{"path":25,"title":26,"description":27,"industry":28,"services":29,"results":32,"cover":23,"coverAlt":23},"\u002Fwork\u002Fstock-market-deep-learning","Deep-learning market analysis for a fintech","Time-series and sentiment models feeding a portfolio-monitoring platform for an investment-focused fintech.","Finance & Fintech",[30,31],"data-analytics-predictive-ai","nlp",[33,36],{"value":34,"label":35},"22%","improvement in model-guided returns vs. baseline strategy",{"value":37,"label":38},"Daily","automated retraining and reporting",{"path":40,"title":41,"description":42,"industry":43,"services":44,"results":45,"cover":23,"coverAlt":23},"\u002Fwork\u002Fhealthcare-data-analysis","Clinical data analysis for a hospital network","Machine-learning analysis of patient records that surfaces patterns and risk indicators for clinicians.","Healthcare",[30],[46,49],{"value":47,"label":48},"30%","improvement in early risk-flag accuracy vs. manual triage",{"value":50,"label":51},"Minutes","not days, for cohort analysis",{"path":53,"title":54,"description":55,"industry":56,"services":57,"results":58,"cover":23,"coverAlt":23},"\u002Fwork\u002Fsaas-onboarding-assistant","Onboarding assistant for a B2B SaaS 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internal documents, with citations and role-based access.","Professional Services",[11,83,12],"ai-integration",[85,88],{"value":86,"label":87},"~70%","faster answers to policy and process questions",{"value":89,"label":90},"100%","answers with source citations",{"path":92,"title":93,"description":94,"industry":81,"services":95,"results":98,"cover":23,"coverAlt":23},"\u002Fwork\u002Fcrm-automation-plugin","AI plugin for CRM automation","An agent inside the client's CRM that enriches leads, drafts follow-ups and updates records from email and call notes.",[96,97,83],"ai-agents","ai-automation",[99,102],{"value":100,"label":101},"10+ hrs","saved per rep per week",{"value":103,"label":104},"-90%","missing CRM fields on new leads",{"path":106,"title":107,"description":108,"industry":8,"services":109,"results":110,"cover":23,"coverAlt":23},"\u002Fwork\u002Fmarketing-eda","Exploratory data analysis for a marketing team","A rigorous analysis of campaign, CRM and web data that reallocated 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humans.",[10,11],[134,137],{"value":135,"label":136},"~55%","of conversations resolved by the assistant",{"value":138,"label":139},"24\u002F7","coverage across time zones",{"path":141,"title":142,"description":143,"industry":43,"services":144,"results":146,"cover":156,"coverAlt":157},"\u002Fwork\u002Fclinic-ai-phone-receptionist","AI phone receptionist for a multi-site clinic group","A voice agent that answers calls, books and reschedules appointments and hands over to staff, cutting missed calls without adding headcount.",[145,97,83],"voice-ai",[147,150,153],{"value":148,"label":149},"~40%","fewer missed calls in opening hours",{"value":151,"label":152},"60–65%","of calls completed without a receptionist",{"value":154,"label":155},"\u003C1.2 s","median response latency","\u002Fcovers\u002Fwork\u002Fclinic-ai-phone-receptionist.svg","Abstract navy cover with a rising cyan course 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set",{"value":203,"label":204},"-60%","defective units reaching final inspection",{"value":206,"label":207},"\u003C40 ms","inference per frame at line speed","\u002Fcovers\u002Fwork\u002Fmanufacturing-defect-detection.svg",{"path":210,"title":211,"description":212,"industry":28,"services":213,"results":215,"cover":224,"coverAlt":157},"\u002Fwork\u002Fon-prem-llm-regulated-insurer","On-premises open-source LLM platform for a regulated insurer","A private LLM stack (open-weight models, GPU serving, guardrails and observability) deployed inside the client's data centre so no policy data leaves their network.",[214,197,11],"open-source-ai",[216,219,222],{"value":217,"label":218},"0","documents or prompts leaving the network",{"value":220,"label":221},"~1,200","internal users onboarded in the first quarter",{"value":86,"label":223},"lower cost per 1M tokens vs. the API quote","\u002Fcovers\u002Fwork\u002Fon-prem-llm-regulated-insurer.svg",{"path":226,"title":227,"description":228,"industry":56,"services":229,"results":231,"cover":241,"coverAlt":157},"\u002Fwork\u002Flegal-contract-extraction-fine-tuning","Fine-tuned LLM for clause extraction from commercial contracts","A domain-adapted model that pulls parties, dates, obligations and unusual clauses out of contracts with accuracy a general-purpose model could not reach at the same cost.",[230,31,214],"ai-fine-tuning",[232,235,238],{"value":233,"label":234},"0.91 → 0.96","field-level F1 vs. the prompted frontier model",{"value":236,"label":237},"~85%","lower inference cost per contract",{"value":239,"label":240},"40+","clause types extracted","\u002Fcovers\u002Fwork\u002Flegal-contract-extraction-fine-tuning.svg",{"path":243,"title":244,"description":245,"industry":246,"services":247,"results":249,"cover":258,"coverAlt":157},"\u002Fwork\u002Fproptech-ai-saas-mvp","AI SaaS MVP for a property-management startup","From concept to paying pilot customers in twelve weeks: an AI-powered tenant-communication and maintenance-triage product built for a seed-stage startup.","Real Estate & Construction",[248,83,10],"ai-saas-product-development",[250,253,256],{"value":251,"label":252},"12 weeks","from kick-off to first paying pilot",{"value":254,"label":255},"6","pilot agencies onboarded in the first quarter",{"value":86,"label":257},"of tenant messages triaged automatically","\u002Fcovers\u002Fwork\u002Fproptech-ai-saas-mvp.svg",{"path":260,"title":261,"description":262,"industry":8,"services":263,"results":265,"cover":275,"coverAlt":157},"\u002Fwork\u002Fmultimodal-product-catalogue","Multimodal AI catalogue system for a home-and-garden retailer","Vision-language models that turn supplier photos and spec sheets into structured, searchable product listings, with attribute extraction, image tagging and visual search.",[264,196,12],"multimodal-ai",[266,269,272],{"value":267,"label":268},"~10×","faster new-product listing",{"value":270,"label":271},"~93%","attribute accuracy on the QA sample",{"value":273,"label":274},"+9%","search-to-product-page click-through","\u002Fcovers\u002Fwork\u002Fmultimodal-product-catalogue.svg","\u003Csvg xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" viewBox=\"0 0 640 480\" role=\"img\" aria-hidden=\"true\">\u003Cg fill=\"none\" stroke=\"currentColor\" stroke-width=\"5\" stroke-linecap=\"round\" stroke-linejoin=\"round\">\u003Crect x=\"140\" y=\"100\" width=\"360\" height=\"230\" rx=\"16\"\u002F>\u003Cpath d=\"M100 340H540V352a14 14 0 0 1-14 14H114a14 14 0 0 1-14-14Z\"\u002F>\u003Cline x1=\"300\" y1=\"352\" x2=\"340\" y2=\"352\" stroke-opacity=\"0.45\"\u002F>\u003Cg stroke-width=\"3.5\">\u003Crect x=\"190\" y=\"240\" width=\"24\" height=\"60\" rx=\"5\"\u002F>\u003Crect x=\"230\" y=\"210\" width=\"24\" height=\"90\" rx=\"5\"\u002F>\u003Crect x=\"270\" 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