{
  "format": "minglabs/v1",
  "pageType": "Marketing",
  "url": "https://www.minglabs.com/work",
  "htmlUrl": "https://www.minglabs.com/work",
  "title": "Work — MING Labs",
  "description": "Selected case studies. AI Product Finders, Companions, Sales Enablement, Agent Experience, and Hybrid Organisation engagements.",
  "inLanguage": "en",
  "dateModified": "2026-09-18",
  "markdownUrl": "https://www.minglabs.com/work.md",
  "contentMarkdown": "---\ntitle: \"Work — MING Labs\"\ndescription: \"Selected case studies. AI Product Finders, Companions, Sales Enablement, Agent Experience, and Hybrid Organisation engagements.\"\ncanonical: https://www.minglabs.com/work\nlang: en\nlast-updated: 2026-09-18\n---\n\n# Work.\n\nWhat we've built for enterprises.  \nAnd for ourselves.\n\n## Selected case studies\n\n     \n\n![MANN+HUMMEL](/_astro/mannhummel-logo.B1l0KoG1.svg)\n\nAI Product Finder\n\n### 3,000 SKUs narrowed to one recommendation.\n\nAgents need this logic — it's currently locked behind a JavaScript UI. We built an AI-powered product finder that guides users through complex filtration requirements to the exact right product.\n\n3,000 → 1 recommendation \n\nMANN+HUMMEL — Detail\n\n![MANN+HUMMEL case study](/_astro/mannhummel-case.Cs84TVp_.webp)\n\nProblem\n\n3,000 SKUs with complex compatibility rules. Customers can't self-serve, sales reps need deep product knowledge.\n\nSolution\n\nConversational AI product finder that understands filtration requirements, vehicle specs, and application context.\n\nResult\n\nSelf-service product selection. Reduced support load. Foundation for agent-accessible product logic.\n\nService\n\nAI Product Finder\n\n![MANN+HUMMEL case study](/_astro/mannhummel-case.Cs84TVp_.webp)\n\n![Stöckli](/_astro/stoeckli-logo.BLMh7uo_.svg)\n\nAI Product Finder\n\n### Ski selection based on rider profile.\n\nComplex compatibility logic made conversational. From body measurements, skill level, and terrain preference to the perfect ski — without a 40-page catalogue.\n\nStöckli — Detail\n\n![Stöckli case study](/_astro/stoeckli-case.CVJXk3BH.jpg)\n\nProblem\n\nSki selection requires matching rider weight, height, skill level, terrain, and snow conditions against product lines.\n\nSolution\n\nConversational product finder that translates rider profile into precise ski recommendations with reasoning.\n\nResult\n\nHigher conversion in online shop. Reduced returns. Better match quality than manual selection.\n\nService\n\nAI Product Finder\n\n![Stöckli case study](/_astro/stoeckli-case.CVJXk3BH.jpg)\n\n![Oikos Group](/_astro/oikos-logo.SARKT9Pp.svg)\n\nAI Product Finder\n\n### Plan a whole house from a conversation with CASAI.\n\nFor Oikos Group, one of Europe's largest prefab-home builders, we built CASAI: an AI advisor that turns a buyer's wishes, style and budget into concrete, priced house concepts in real time, then compiles a downloadable Exposé. No expertise needed, no pressure, available around the clock.\n\n10,000+ realised builds behind every concept \n\nOikos Group — Detail\n\n![Oikos Group case study](/_astro/oikos-case.CU5N7wJP.webp)\n\nProblem\n\nPlanning a prefab home is a months-long, high-stakes decision. Buyers need design and pricing clarity early, but every question ties up a rep, and most stall before they ever make contact. Oikos needed one digital advisor that scales across its house brands.\n\nSolution\n\nCASAI, a conversational AI advisor. It matches a buyer's wishes against 10,000+ realised builds, turns them into concrete house concepts with live pricing, and compiles a downloadable Exposé that becomes the basis for talking to a human expert. Now the group's AI-supported digital standard, live on Hanse Haus.\n\nResult\n\nBuyers reach a clear, costed concept on their own, around the clock and with no obligation. Every completed Exposé is a qualified, sales-ready lead, and CASAI is now Oikos Group's digital standard for house planning.\n\nService\n\nAI Product Finder\n\n![Oikos Group case study](/_astro/oikos-case.CU5N7wJP.webp)\n\n![Bosch](/_astro/bosch.Dm3WUh-J.svg)\n\nAI Companion\n\n### Context that persists across sessions.\n\nAn AI companion that remembers where the user left off. Not a chatbot — a persistent working partner that builds understanding over time.\n\nBosch — Detail\n\n![Bosch case study](/_astro/bosch-case.Da50gtFX.webp)\n\nProblem\n\nEvery AI session starts from zero. Users re-explain context, lose thread, waste time re-establishing where they were.\n\nSolution\n\nSession-persistent AI companion with memory architecture. Picks up where the user left off, builds cumulative understanding.\n\nResult\n\nDramatic reduction in context-setting time. Users treat the AI as a working partner, not a tool.\n\nService\n\nAI Companion\n\n![Bosch case study](/_astro/bosch-case.Da50gtFX.webp)\n\n![Nui](/_astro/nui.BCDX7PBs.svg)\n\nAI Companion\n\n### AI companion for complex consumer decisions.\n\nGuiding users through high-consideration purchases with contextual intelligence and personalised recommendations.\n\nNui — Detail\n\n![Nui case study](/_astro/nui-case.CKlE7MJX.jpeg)\n\nProblem\n\nHigh-consideration consumer decisions require deep product understanding and personal context.\n\nSolution\n\nAI companion that learns user preferences and guides through complex decision trees conversationally.\n\nResult\n\nImproved conversion rates and customer satisfaction through personalised guidance.\n\nService\n\nAI Companion\n\n![Nui case study](/_astro/nui-case.CKlE7MJX.jpeg)\n\n![Voith](/_astro/voith.B7Wq-RVU.svg)\n\nSales Enablement\n\n### Full buyer context before the first call.\n\nSales team sees the complete journey. Every touchpoint, every document downloaded, every question asked — surfaced before the conversation starts.\n\nVoith — Detail\n\n![Voith case study](/_astro/voith-case.Dp4OQA-J.jpg)\n\nProblem\n\nSales reps enter calls blind. Buyer history scattered across CRM, website analytics, and email threads.\n\nSolution\n\nAI-powered sales enablement that aggregates buyer journey into a pre-call briefing with context and talking points.\n\nResult\n\nSales team enters every call prepared. Shorter sales cycles. Higher win rates on complex deals.\n\nService\n\nSales Enablement\n\n![Voith case study](/_astro/voith-case.Dp4OQA-J.jpg)\n\n![MING Labs](/_astro/ming-logo.D_wUBLvN.svg)\n\nHybrid Org\n\n### Our own hybrid organisation. Agents alongside experts, autonomous 24/7.\n\nOur own organisation as proof. We run what we sell — a hybrid organisation where AI agents handle structured work so humans focus on judgment and relationships.\n\n60%+ of structured routine handled by agents \n\nMING Labs — Detail\n\nProblem\n\nA small expert team with enterprise-grade workload. Expert time consumed by reporting, inbox, pipeline management.\n\nSolution\n\nA named AI agent fleet (Lola, Cody, SM3CB, Martin, Vera, Joerg) operating 24/7 across sales, product, strategy, and operations.\n\nResult\n\n~40% of expert time is structured routine — agents now handle 60%+ of it. Morning briefings, pipeline intelligence, overnight deliverables, all autonomous.\n\nService\n\nHybrid Organisation\n\n![Hyperize](/_astro/hyperize-logo.1fVw8eDI.svg)\n\nAgent Experience\n\n### 520 queries. 10 brands. 4 AI platforms.\n\nHyperize — our venture for agent visibility. Understanding how AI platforms recommend — or ignore — brands when users ask for products and services.\n\n0% brand citations on generic queries \n\nHyperize — Detail\n\nProblem\n\nBrands have zero visibility into how AI agents recommend products. Even brands cited 86% of the time when named get zero citations the moment the query goes generic.\n\nSolution\n\nPlatform that tracks brand visibility across ChatGPT, Gemini, Perplexity, and Claude for real purchase queries.\n\nResult\n\nBrands see exactly where they're invisible to AI agents — and get a roadmap to fix it.\n\nService\n\nAgent Experience\n\n## Your use case is next.\n\nWe start with a workshop — half a day, three roles, one concept paper. No pitch decks.\n\n[Discuss my project](/contact?type=work)\n\n## Available actions\n\n- **Copy briefing for my AI**: copies the [AI briefing](https://www.minglabs.com/briefing.md) to the clipboard in the browser.\n\n---\n\nCanonical: https://www.minglabs.com/work\nMachine-readable index: https://www.minglabs.com/llms.txt\n",
  "metadata": {
    "title": "Work — MING Labs",
    "description": "Selected case studies. AI Product Finders, Companions, Sales Enablement, Agent Experience, and Hybrid Organisation engagements.",
    "canonical": "https://www.minglabs.com/work",
    "inLanguage": "en",
    "openGraph": {
      "title": "Work — MING Labs",
      "description": "Selected case studies. AI Product Finders, Companions, Sales Enablement, Agent Experience, and Hybrid Organisation engagements.",
      "url": "https://www.minglabs.com/work",
      "image": "https://www.minglabs.com/og-image-v2.jpg"
    },
    "twitter": {
      "card": "summary_large_image",
      "title": "Work — MING Labs",
      "description": "Selected case studies. AI Product Finders, Companions, Sales Enablement, Agent Experience, and Hybrid Organisation engagements.",
      "image": "https://www.minglabs.com/og-image-v2.jpg"
    }
  },
  "actions": [
    {
      "id": "navigation",
      "label": "Get in touch",
      "accessibleName": "Get in touch",
      "kind": "navigate",
      "requiresBrowser": false,
      "url": "https://www.minglabs.com/contact"
    },
    {
      "id": "project",
      "label": "Discuss my project",
      "accessibleName": "Discuss my project with MING Labs",
      "kind": "navigate",
      "requiresBrowser": false,
      "url": "https://www.minglabs.com/contact?type=work"
    },
    {
      "id": "copy-briefing",
      "label": "Copy briefing for my AI",
      "accessibleName": "Copy briefing for my AI",
      "kind": "copy",
      "requiresBrowser": true,
      "contentUrl": "https://www.minglabs.com/briefing.md"
    }
  ]
}