661 lines
37 KiB
HTML
661 lines
37 KiB
HTML
<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Use Cases — Nexus One AI Portal</title>
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<link rel="stylesheet" href="style.css?v=4">
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</head>
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<body>
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<header class="topnav">
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<a href="index.html" class="brand">Nexus One <span>AI</span></a>
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<nav>
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<a href="index.html">Home</a>
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<a href="quickstart.html">Quick Start</a>
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<a href="prompts.html">Prompt Library</a>
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<a href="usecases.html" class="active">Use Cases</a>
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<span class="nav-sep"></span>
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<div class="nav-dropdown">
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<button class="nav-drop-btn">Help ▾</button>
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<div class="nav-drop-menu">
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<span class="nav-drop-cat">LEARN /</span>
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<a href="quickstart.html">Quick Start</a>
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<a href="models.html">Models</a>
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<span class="nav-drop-cat">SUPPORT /</span>
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<a href="troubleshooting.html">Troubleshoot</a>
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<a href="faq.html">FAQ</a>
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<span class="nav-drop-cat">MORE /</span>
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<a href="glossary.html">Glossary</a>
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<a href="whats-new.html">What's New</a>
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</div>
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</div>
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<div class="nav-dropdown">
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<button class="nav-drop-btn">Admin ▾</button>
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<div class="nav-drop-menu nav-drop-menu-wide">
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<span class="nav-drop-cat">DOCS /</span>
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<a href="security.html">Security & Privacy</a>
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<a href="admin.html">Admin Guide</a>
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<span class="nav-drop-cat">MONITOR /</span>
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<a href="dashboard.html">Dashboard</a>
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<a href="analytics.html">Usage Analytics</a>
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<a href="audit.html">Audit Log</a>
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<a href="feedback.html">Feedback & Ratings</a>
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<span class="nav-drop-cat">MANAGE /</span>
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<a href="users.html">Users</a>
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<a href="teams.html">Teams</a>
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<a href="models-admin.html">Model Manager</a>
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<a href="training.html">Training</a>
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<a href="knowledge.html">Knowledge Base</a>
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<span class="nav-drop-cat">TOOLS /</span>
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<a href="apikeys.html">API Keys</a>
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<a href="benchmark.html">Benchmarking</a>
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<a href="model-compare.html">Model Compare</a>
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<a href="api-playground.html">API Playground</a>
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<a href="guardrails.html">Guardrails</a>
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<a href="rag-quality.html">RAG Quality</a>
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<a href="router.html">Model Router</a>
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<a href="connectors.html">Connectors</a>
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<span class="nav-drop-cat">SYSTEM /</span>
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<a href="console.html">Console</a>
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<a href="settings.html">Settings</a>
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</div>
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</div>
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<div class="nav-dropdown">
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<button class="nav-drop-btn">AI Tools ▾</button>
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<div class="nav-drop-menu">
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<span class="nav-drop-cat">INTELLIGENCE /</span>
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<a href="documents.html">Document Intelligence</a>
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<a href="chat-multi.html">Multimodal Chat</a>
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<a href="prompt-studio.html">Prompt Studio</a>
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<a href="meeting.html">Meeting Assistant</a>
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<span class="nav-drop-cat">AUTOMATION /</span>
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<a href="agents.html">Agent Builder</a>
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<a href="schedules.html">Scheduled Jobs</a>
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<a href="workflows.html">Workflow Automation</a>
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<span class="nav-drop-cat">QUALITY /</span>
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<a href="evals.html">AI Eval Suite</a>
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<a href="chatrooms.html">Chat Rooms</a>
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</div>
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</div>
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</nav>
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<a href="notifications.html" style="position:relative">🔔</a>
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<span class="badge" data-brand="tier">Basic Tier</span>
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<div id="nav-org-logo" class="nav-org-logo"></div>
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</header>
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<div class="page-hero">
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<div class="label">Use Cases</div>
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<h1>What Teams Are Doing With Nexus One AI</h1>
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<p>Real tasks, real time savings. Browse by department to see what's possible — then try it yourself with the linked prompts.</p>
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</div>
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<div class="content">
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<!-- IMPACT SUMMARY -->
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<div class="uc-impact-bar">
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<div class="uc-impact-item">
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<div class="uc-impact-num">9</div>
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<div class="uc-impact-label">Departments</div>
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</div>
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<div class="uc-impact-divider"></div>
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<div class="uc-impact-item">
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<div class="uc-impact-num">30</div>
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<div class="uc-impact-label">Use Cases</div>
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</div>
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<div class="uc-impact-divider"></div>
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<div class="uc-impact-item">
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<div class="uc-impact-num">100%</div>
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<div class="uc-impact-label">On-Premises</div>
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</div>
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<div class="uc-impact-divider"></div>
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<div class="uc-impact-item">
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<div class="uc-impact-num">0</div>
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<div class="uc-impact-label">Data Sent to Cloud</div>
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</div>
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</div>
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<!-- FILTERS -->
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<div class="pl-filters" style="margin-bottom:28px">
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<button class="pl-filter active" onclick="setUCFilter('all', this)">All</button>
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<button class="pl-filter" onclick="setUCFilter('procurement', this)">📋 Procurement</button>
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<button class="pl-filter" onclick="setUCFilter('hr', this)">👥 HR & Admin</button>
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<button class="pl-filter" onclick="setUCFilter('legal', this)">⚖️ Legal</button>
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<button class="pl-filter" onclick="setUCFilter('operations', this)">⚙️ Operations</button>
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<button class="pl-filter" onclick="setUCFilter('citizen', this)">🏛️ Citizen Services</button>
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<button class="pl-filter" onclick="setUCFilter('finance', this)">💰 Finance</button>
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<button class="pl-filter" onclick="setUCFilter('comms', this)">✉️ Communications</button>
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<button class="pl-filter" onclick="setUCFilter('management', this)">📊 Management</button>
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<button class="pl-filter" onclick="setUCFilter('platform', this)">🚀 Platform Features</button>
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</div>
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<!-- USE CASE CARDS -->
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<div class="uc-grid" id="uc-grid">
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<!-- PROCUREMENT -->
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<div class="uc-card" data-dept="procurement">
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<div class="uc-card-top">
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<span class="uc-dept-tag procurement">📋 Procurement</span>
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<span class="uc-time-saved">⏱ 6 hrs → 25 min</span>
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</div>
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<h3 class="uc-title">Reviewing 40 vendor contracts for penalty clauses</h3>
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<p class="uc-scenario">Before a major infrastructure project award, the procurement team needed to check 40 vendor contracts for liability-heavy penalty clauses. Previously this took a senior officer two full days. With Nexus One AI, each contract was uploaded individually and the AI extracted all penalty clauses in a structured table — 40 contracts reviewed in under 25 minutes.</p>
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<div class="uc-outcome">
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<span class="uc-outcome-label">Outcome</span>
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3 high-risk contracts flagged for legal review before signing — one containing an uncapped liability clause that was successfully renegotiated.
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</div>
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<div class="uc-footer">
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<div class="uc-tools">
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<span class="tag">Open WebUI</span>
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<span class="tag">Ollama</span>
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</div>
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<a href="prompts.html" class="uc-prompt-link">Try a prompt →</a>
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</div>
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</div>
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<div class="uc-card" data-dept="procurement">
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<div class="uc-card-top">
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<span class="uc-dept-tag procurement">📋 Procurement</span>
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<span class="uc-time-saved">⏱ 4 hrs → 30 min</span>
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</div>
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<h3 class="uc-title">Summarising a 200-page tender document for bid evaluation</h3>
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<p class="uc-scenario">The evaluation committee received a 200-page technical tender from a shortlisted vendor. Members needed a concise brief before the evaluation meeting. The AI was given the document and asked to extract the top eligibility criteria, key technical specifications, commercial terms, and submission deadlines — producing a 2-page summary in minutes.</p>
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<div class="uc-outcome">
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<span class="uc-outcome-label">Outcome</span>
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Evaluation committee walked into the meeting fully briefed. Three eligibility gaps in the vendor's submission were identified before the meeting, enabling targeted questioning.
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</div>
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<div class="uc-footer">
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<div class="uc-tools">
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<span class="tag">Open WebUI</span>
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<span class="tag">ChromaDB</span>
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</div>
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<a href="prompts.html" class="uc-prompt-link">Try a prompt →</a>
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</div>
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</div>
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<div class="uc-card" data-dept="procurement">
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<div class="uc-card-top">
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<span class="uc-dept-tag procurement">📋 Procurement</span>
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<span class="uc-time-saved">⏱ 3 hrs → 20 min</span>
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</div>
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<h3 class="uc-title">Comparing three vendor proposals side by side</h3>
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<p class="uc-scenario">Three vendors submitted competing proposals for a software project. The team uploaded all three proposals and asked the AI to compare them across technical approach, timeline, team qualifications, past experience, and total cost — producing a structured comparison table that previously required a manual spreadsheet exercise.</p>
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<div class="uc-outcome">
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<span class="uc-outcome-label">Outcome</span>
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Clear comparison table produced in 20 minutes. The preferred vendor's proposal had a weaker implementation timeline — a factor that would have taken hours to surface manually.
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</div>
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<div class="uc-footer">
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<div class="uc-tools">
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<span class="tag">Open WebUI</span>
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<span class="tag">Ollama</span>
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</div>
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<a href="prompts.html" class="uc-prompt-link">Try a prompt →</a>
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</div>
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</div>
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<!-- HR & ADMIN -->
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<div class="uc-card" data-dept="hr">
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<div class="uc-card-top">
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<span class="uc-dept-tag hr">👥 HR & Admin</span>
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<span class="uc-time-saved">⏱ 2 hrs → 15 min</span>
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</div>
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<h3 class="uc-title">Drafting 20 offer letters for a bulk recruitment drive</h3>
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<p class="uc-scenario">Following a recruitment drive that selected 20 candidates across five departments, the HR team needed to issue formal offer letters quickly. Using the AI with a structured prompt template, they generated all 20 letters in 15 minutes — customised per candidate with correct job titles, departments, reporting lines, and salaries.</p>
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<div class="uc-outcome">
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<span class="uc-outcome-label">Outcome</span>
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Letters issued same day. Zero formatting errors. HR officer spent remaining time on onboarding coordination rather than letter drafting.
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</div>
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<div class="uc-footer">
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<div class="uc-tools">
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<span class="tag">Open WebUI</span>
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<span class="tag">Ollama</span>
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</div>
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<a href="prompts.html" class="uc-prompt-link">Try a prompt →</a>
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</div>
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</div>
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<div class="uc-card" data-dept="hr">
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<div class="uc-card-top">
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<span class="uc-dept-tag hr">👥 HR & Admin</span>
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<span class="uc-time-saved">⏱ 1 day → 2 hrs</span>
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</div>
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<h3 class="uc-title">Creating an employee onboarding guide from scattered policy documents</h3>
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<p class="uc-scenario">New joiners were receiving a 12-document policy pack with no summary or guide. HR uploaded all 12 documents and asked the AI to synthesise a single, friendly "What you need to know in your first week" guide covering working hours, leave, IT policy, code of conduct, and who to contact. The AI produced a clear 4-page guide in plain English.</p>
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<div class="uc-outcome">
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<span class="uc-outcome-label">Outcome</span>
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New joiner confusion calls to HR dropped significantly. The guide is now given to every new employee on day one.
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</div>
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<div class="uc-footer">
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<div class="uc-tools">
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<span class="tag">Open WebUI</span>
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<span class="tag">ChromaDB</span>
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</div>
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<a href="prompts.html" class="uc-prompt-link">Try a prompt →</a>
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</div>
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</div>
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<div class="uc-card" data-dept="hr">
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<div class="uc-card-top">
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<span class="uc-dept-tag hr">👥 HR & Admin</span>
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<span class="uc-time-saved">⏱ 3 hrs → 30 min</span>
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</div>
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<h3 class="uc-title">Building a policy knowledge quiz for compliance training</h3>
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<p class="uc-scenario">The compliance team needed to test all 150 staff on a new procurement policy. Creating a 10-question MCQ quiz manually would have taken half a day. The AI read the 28-page policy document and generated a 10-question quiz with four answer options each, one correct answer per question, and an answer key — ready for distribution.</p>
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<div class="uc-outcome">
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<span class="uc-outcome-label">Outcome</span>
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Training quiz deployed to all staff within the same afternoon the policy was issued. 100% completion rate achieved within 3 days.
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</div>
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<div class="uc-footer">
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<div class="uc-tools">
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<span class="tag">Open WebUI</span>
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<span class="tag">Ollama</span>
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</div>
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<a href="prompts.html" class="uc-prompt-link">Try a prompt →</a>
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</div>
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</div>
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<!-- LEGAL -->
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<div class="uc-card" data-dept="legal">
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<div class="uc-card-top">
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<span class="uc-dept-tag legal">⚖️ Legal</span>
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<span class="uc-time-saved">⏱ Half day → 45 min</span>
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</div>
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<h3 class="uc-title">Translating a new regulation into plain-English guidance for departments</h3>
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<p class="uc-scenario">A 60-page regulatory circular was issued by the central government with a 2-week compliance deadline. Department heads needed to understand their obligations quickly without reading the full document. The legal team used the AI to produce a plain-English summary covering what each department must do, what is prohibited, and key deadlines — within 45 minutes of receiving the circular.</p>
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<div class="uc-outcome">
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<span class="uc-outcome-label">Outcome</span>
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Plain-English brief circulated to all 8 department heads within the hour. Compliance actions started the same day — two weeks ahead of what would have been possible with a manual drafting process.
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</div>
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<div class="uc-footer">
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<div class="uc-tools">
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<span class="tag">Open WebUI</span>
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<span class="tag">ChromaDB</span>
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</div>
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<a href="prompts.html" class="uc-prompt-link">Try a prompt →</a>
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</div>
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</div>
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<div class="uc-card" data-dept="legal">
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<div class="uc-card-top">
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<span class="uc-dept-tag legal">⚖️ Legal</span>
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<span class="uc-time-saved">⏱ 2 hrs → 20 min</span>
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</div>
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<h3 class="uc-title">Extracting all deadlines from a 45-page regulatory notice</h3>
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<p class="uc-scenario">A multi-part regulatory notice contained submission deadlines, reporting dates, and filing requirements scattered across 45 pages. Rather than reading the entire document, the legal officer uploaded it and asked the AI to extract every deadline in chronological order with the responsible department noted against each — producing a compliance calendar in minutes.</p>
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<div class="uc-outcome">
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<span class="uc-outcome-label">Outcome</span>
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Full compliance calendar produced in 20 minutes. Two deadlines that had been overlooked in previous manual reviews were surfaced and actioned before they were missed.
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</div>
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<div class="uc-footer">
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<div class="uc-tools">
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<span class="tag">Open WebUI</span>
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||
<span class="tag">Ollama</span>
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</div>
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<a href="prompts.html" class="uc-prompt-link">Try a prompt →</a>
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</div>
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</div>
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<!-- OPERATIONS -->
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<div class="uc-card" data-dept="operations">
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<div class="uc-card-top">
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<span class="uc-dept-tag operations">⚙️ Operations</span>
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<span class="uc-time-saved">⏱ 1 day → 1 hr</span>
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</div>
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<h3 class="uc-title">Converting a 300-page equipment manual into field SOPs</h3>
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<p class="uc-scenario">Technicians maintaining critical infrastructure were working from a 300-page OEM manual that was impractical to use in the field. The operations team uploaded sections of the manual and asked the AI to generate step-by-step SOP checklists per equipment type, with tools required, safety warnings, and estimated time per task — producing laminated field cards for each procedure.</p>
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<div class="uc-outcome">
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<span class="uc-outcome-label">Outcome</span>
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12 SOPs produced in one day. Field teams reported significantly faster task execution and fewer errors, with technicians no longer needing to flip through a 300-page manual on site.
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</div>
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<div class="uc-footer">
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||
<div class="uc-tools">
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||
<span class="tag">Open WebUI</span>
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||
<span class="tag">ChromaDB</span>
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||
</div>
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||
<a href="prompts.html" class="uc-prompt-link">Try a prompt →</a>
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||
</div>
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</div>
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<div class="uc-card" data-dept="operations">
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<div class="uc-card-top">
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<span class="uc-dept-tag operations">⚙️ Operations</span>
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<span class="uc-time-saved">⏱ 3 hrs → 30 min</span>
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</div>
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<h3 class="uc-title">Identifying recurring faults from 6 months of maintenance logs</h3>
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<p class="uc-scenario">The facilities team had 6 months of maintenance logs in spreadsheet format with 800+ entries. They needed to identify patterns — which equipment failed most often, which faults were recurring, and whether any systemic issue existed. The AI analysed the uploaded log and produced a structured breakdown of top faults, affected assets, and a hypothesis on root causes.</p>
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<div class="uc-outcome">
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<span class="uc-outcome-label">Outcome</span>
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A recurring cooling issue in Generator Block 3 was identified — traced back to a filter replacement interval that was too long. Interval adjusted; no further cooling-related faults in the following 3 months.
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</div>
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<div class="uc-footer">
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||
<div class="uc-tools">
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||
<span class="tag">Open WebUI</span>
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||
<span class="tag">Ollama</span>
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||
</div>
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<a href="prompts.html" class="uc-prompt-link">Try a prompt →</a>
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</div>
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</div>
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<!-- CITIZEN SERVICES -->
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<div class="uc-card" data-dept="citizen">
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<div class="uc-card-top">
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<span class="uc-dept-tag citizen">🏛️ Citizen Services</span>
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<span class="uc-time-saved">⏱ 30 min → 3 min</span>
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</div>
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<h3 class="uc-title">Drafting professional responses to citizen complaints</h3>
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<p class="uc-scenario">The citizen services desk receives 60–80 complaints per week. Each written response previously required a senior officer to draft from scratch — consuming 20–30 minutes per letter. Using a structured prompt, officers now describe the complaint and desired resolution in a few sentences and the AI drafts a formal, empathetic response that only needs a quick review before sending.</p>
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<div class="uc-outcome">
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<span class="uc-outcome-label">Outcome</span>
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Average response time per complaint reduced from 30 minutes to under 5 minutes. Backlog of 120 pending responses cleared in a single afternoon.
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</div>
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<div class="uc-footer">
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||
<div class="uc-tools">
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||
<span class="tag">Open WebUI</span>
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||
<span class="tag">Ollama</span>
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||
</div>
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||
<a href="prompts.html" class="uc-prompt-link">Try a prompt →</a>
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||
</div>
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</div>
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<div class="uc-card" data-dept="citizen">
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<div class="uc-card-top">
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<span class="uc-dept-tag citizen">🏛️ Citizen Services</span>
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<span class="uc-time-saved">⏱ 2 days → 2 hrs</span>
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</div>
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<h3 class="uc-title">Building a citizen FAQ from 3 months of call centre logs</h3>
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<p class="uc-scenario">The contact centre manager needed to create an FAQ document to reduce repeat calls about a new service. Three months of call log summaries were uploaded to the AI, which identified the 15 most common questions, grouped them by theme, and drafted plain-English answers suitable for publishing on the department's website and sending via SMS to citizens.</p>
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<div class="uc-outcome">
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<span class="uc-outcome-label">Outcome</span>
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||
FAQ published within the same week. Call volume for the top 5 repeat queries dropped by an estimated 30% in the following month.
|
||
</div>
|
||
<div class="uc-footer">
|
||
<div class="uc-tools">
|
||
<span class="tag">Open WebUI</span>
|
||
<span class="tag">ChromaDB</span>
|
||
</div>
|
||
<a href="prompts.html" class="uc-prompt-link">Try a prompt →</a>
|
||
</div>
|
||
</div>
|
||
|
||
<!-- FINANCE -->
|
||
<div class="uc-card" data-dept="finance">
|
||
<div class="uc-card-top">
|
||
<span class="uc-dept-tag finance">💰 Finance</span>
|
||
<span class="uc-time-saved">⏱ Half day → 45 min</span>
|
||
</div>
|
||
<h3 class="uc-title">Identifying budget overruns across 12 departments</h3>
|
||
<p class="uc-scenario">The finance team received budget utilisation reports from 12 departments at mid-year. Manually reviewing each to find overspends against allocation would have taken a full day. Each report was uploaded to the AI which identified line items exceeding allocation by more than 10%, flagged the highest-risk overruns, and produced a consolidated exception report for the Finance Director.</p>
|
||
<div class="uc-outcome">
|
||
<span class="uc-outcome-label">Outcome</span>
|
||
Exception report produced in 45 minutes rather than a full day. Four departments flagged for immediate action; one department had a data entry error corrected that would otherwise have been missed until year-end audit.
|
||
</div>
|
||
<div class="uc-footer">
|
||
<div class="uc-tools">
|
||
<span class="tag">Open WebUI</span>
|
||
<span class="tag">Ollama</span>
|
||
</div>
|
||
<a href="prompts.html" class="uc-prompt-link">Try a prompt →</a>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="uc-card" data-dept="finance">
|
||
<div class="uc-card-top">
|
||
<span class="uc-dept-tag finance">💰 Finance</span>
|
||
<span class="uc-time-saved">⏱ 3 hrs → 30 min</span>
|
||
</div>
|
||
<h3 class="uc-title">Summarising an internal audit report for the board</h3>
|
||
<p class="uc-scenario">A 90-page internal audit report needed to be presented to the board in a 10-minute slot. The finance officer uploaded the full report and asked the AI to produce a 1-page executive brief covering total findings by severity, the three most significant issues, already-closed items, and the overall audit conclusion — in language suitable for board-level consumption.</p>
|
||
<div class="uc-outcome">
|
||
<span class="uc-outcome-label">Outcome</span>
|
||
Board brief prepared in 30 minutes. The board could engage substantively with the three critical findings rather than spending the session asking what the report contained.
|
||
</div>
|
||
<div class="uc-footer">
|
||
<div class="uc-tools">
|
||
<span class="tag">Open WebUI</span>
|
||
<span class="tag">ChromaDB</span>
|
||
</div>
|
||
<a href="prompts.html" class="uc-prompt-link">Try a prompt →</a>
|
||
</div>
|
||
</div>
|
||
|
||
<!-- COMMUNICATIONS -->
|
||
<div class="uc-card" data-dept="comms">
|
||
<div class="uc-card-top">
|
||
<span class="uc-dept-tag comms">✉️ Communications</span>
|
||
<span class="uc-time-saved">⏱ 4 hrs → 40 min</span>
|
||
</div>
|
||
<h3 class="uc-title">Rewriting a technical infrastructure report for the public</h3>
|
||
<p class="uc-scenario">The engineering division produced a 35-page technical report on a new water treatment facility. The communications team needed a public-facing version — clear, jargon-free, and under 2 pages — for release to local media. The AI rewrote the key findings in plain English, preserved all the important facts, and formatted it as a media brief ready for review.</p>
|
||
<div class="uc-outcome">
|
||
<span class="uc-outcome-label">Outcome</span>
|
||
Public brief ready in 40 minutes. Media release issued the same day as the technical report — the first time the department achieved simultaneous technical and public releases.
|
||
</div>
|
||
<div class="uc-footer">
|
||
<div class="uc-tools">
|
||
<span class="tag">Open WebUI</span>
|
||
<span class="tag">Ollama</span>
|
||
</div>
|
||
<a href="prompts.html" class="uc-prompt-link">Try a prompt →</a>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="uc-card" data-dept="comms">
|
||
<div class="uc-card-top">
|
||
<span class="uc-dept-tag comms">✉️ Communications</span>
|
||
<span class="uc-time-saved">⏱ 1 hr → 10 min</span>
|
||
</div>
|
||
<h3 class="uc-title">Extracting action items from weekly leadership meeting minutes</h3>
|
||
<p class="uc-scenario">The department secretary was spending an hour after every leadership meeting manually extracting decisions and action items from raw meeting notes, then formatting and distributing them. By uploading the notes to the AI, a structured action table — owner, deadline, priority — was produced in under 10 minutes, ready to send directly to the team.</p>
|
||
<div class="uc-outcome">
|
||
<span class="uc-outcome-label">Outcome</span>
|
||
Action items distributed within 15 minutes of every meeting ending. Follow-through on assigned tasks improved as owners received reminders with precise accountability language.
|
||
</div>
|
||
<div class="uc-footer">
|
||
<div class="uc-tools">
|
||
<span class="tag">Open WebUI</span>
|
||
<span class="tag">Ollama</span>
|
||
</div>
|
||
<a href="prompts.html" class="uc-prompt-link">Try a prompt →</a>
|
||
</div>
|
||
</div>
|
||
|
||
<!-- MANAGEMENT -->
|
||
<div class="uc-card" data-dept="management">
|
||
<div class="uc-card-top">
|
||
<span class="uc-dept-tag management">📊 Management</span>
|
||
<span class="uc-time-saved">⏱ Half day → 1 hr</span>
|
||
</div>
|
||
<h3 class="uc-title">Producing executive summaries of lengthy project reports</h3>
|
||
<p class="uc-scenario">Senior management were receiving 40–80 page project status reports from multiple divisions monthly, with no time to read each in full before the monthly review meeting. Each report was uploaded to the AI which produced a consistent 1-page executive summary — purpose, key findings, risks, recommendations, and overall status — giving management meaningful oversight at scale.</p>
|
||
<div class="uc-outcome">
|
||
<span class="uc-outcome-label">Outcome</span>
|
||
Monthly review preparation time reduced from half a day to under an hour. Management engaged with substantially more projects per meeting — and with greater depth on the critical ones.
|
||
</div>
|
||
<div class="uc-footer">
|
||
<div class="uc-tools">
|
||
<span class="tag">Open WebUI</span>
|
||
<span class="tag">ChromaDB</span>
|
||
</div>
|
||
<a href="prompts.html" class="uc-prompt-link">Try a prompt →</a>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="uc-card" data-dept="management">
|
||
<div class="uc-card-top">
|
||
<span class="uc-dept-tag management">📊 Management</span>
|
||
<span class="uc-time-saved">⏱ 2 hrs → 20 min</span>
|
||
</div>
|
||
<h3 class="uc-title">Preparing a SWOT analysis from the annual report for strategy planning</h3>
|
||
<p class="uc-scenario">The strategy planning team needed a SWOT analysis for a division's annual planning workshop. Rather than reading the 55-page annual report and constructing the SWOT manually, the AI was given the report and asked to identify four to five specific, evidence-based points per quadrant — producing a structured analysis grounded directly in the report's own data.</p>
|
||
<div class="uc-outcome">
|
||
<span class="uc-outcome-label">Outcome</span>
|
||
SWOT produced in 20 minutes with direct page references for each point. Workshop facilitator used it as the opening frame, with participants able to interrogate specific findings rather than debating what the data said.
|
||
</div>
|
||
<div class="uc-footer">
|
||
<div class="uc-tools">
|
||
<span class="tag">Open WebUI</span>
|
||
<span class="tag">Ollama</span>
|
||
</div>
|
||
<a href="prompts.html" class="uc-prompt-link">Try a prompt →</a>
|
||
</div>
|
||
</div>
|
||
|
||
<!-- PLATFORM FEATURE USE CASES -->
|
||
|
||
<div class="uc-card" data-dept="platform">
|
||
<div class="uc-card-top">
|
||
<span class="uc-dept-tag" style="background:rgba(124,58,237,.12);color:var(--purple)">🤖 Agent Builder</span>
|
||
<span class="uc-time-saved">⏱ 3 hrs daily → automated</span>
|
||
</div>
|
||
<h3 class="uc-title">Automated daily contract expiry monitoring</h3>
|
||
<p class="uc-scenario">The procurement team had 300+ vendor contracts with varying expiry dates scattered across a shared drive. Tracking renewals was a manual, error-prone task. An AI agent was built to scan the contract folder nightly, extract expiry dates from each PDF, and generate a prioritised "expiring in 30/60/90 days" report — delivered automatically each morning.</p>
|
||
<div class="uc-outcome">
|
||
<span class="uc-outcome-label">Outcome</span>
|
||
Zero missed renewals since deployment. Two contracts that would have lapsed unnoticed were caught and renewed on time.
|
||
</div>
|
||
<div class="uc-footer">
|
||
<div class="uc-tools">
|
||
<span class="tag">Agent Builder</span>
|
||
<span class="tag">Scheduled Jobs</span>
|
||
<span class="tag">Document Intelligence</span>
|
||
</div>
|
||
<a href="agents.html" class="uc-prompt-link">Set up an agent →</a>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="uc-card" data-dept="platform">
|
||
<div class="uc-card-top">
|
||
<span class="uc-dept-tag" style="background:rgba(124,58,237,.12);color:var(--purple)">🎙️ Meeting Assistant</span>
|
||
<span class="uc-time-saved">⏱ 1.5 hrs → 10 min</span>
|
||
</div>
|
||
<h3 class="uc-title">Board meeting minutes produced instantly</h3>
|
||
<p class="uc-scenario">The executive secretariat previously spent 1–2 hours after each board meeting transcribing notes and producing minutes. With the Meeting Assistant, the audio recording is uploaded at the end of the session. The AI produces a full transcript, structured meeting summary with agenda item headings, a decision register, and a list of action items with owners — ready for review within minutes.</p>
|
||
<div class="uc-outcome">
|
||
<span class="uc-outcome-label">Outcome</span>
|
||
Draft minutes circulated to board members within 30 minutes of meeting end, compared to the previous next-day turnaround. Action items are now tracked in a structured format rather than buried in prose.
|
||
</div>
|
||
<div class="uc-footer">
|
||
<div class="uc-tools">
|
||
<span class="tag">Meeting Assistant</span>
|
||
<span class="tag">Whisper STT</span>
|
||
</div>
|
||
<a href="meeting.html" class="uc-prompt-link">Try Meeting Assistant →</a>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="uc-card" data-dept="platform">
|
||
<div class="uc-card-top">
|
||
<span class="uc-dept-tag" style="background:rgba(124,58,237,.12);color:var(--purple)">🔄 Workflow Automation</span>
|
||
<span class="uc-time-saved">⏱ Manual daily task → zero-touch</span>
|
||
</div>
|
||
<h3 class="uc-title">Nightly audit log summarisation for the CISO</h3>
|
||
<p class="uc-scenario">The IT security team needed a daily briefing of notable events from the system audit log — but parsing raw logs manually each morning was impractical. A workflow was created: every night at midnight, it pulls the day's audit log, runs it through the AI to extract anomalies and notable events, and deposits a plain-English summary in a shared folder for the CISO to review first thing in the morning.</p>
|
||
<div class="uc-outcome">
|
||
<span class="uc-outcome-label">Outcome</span>
|
||
Security oversight improved without adding staff workload. An unusual access pattern was flagged in the first week that had previously gone unnoticed.
|
||
</div>
|
||
<div class="uc-footer">
|
||
<div class="uc-tools">
|
||
<span class="tag">Workflow Automation</span>
|
||
<span class="tag">Scheduled Jobs</span>
|
||
<span class="tag">Connectors</span>
|
||
</div>
|
||
<a href="workflows.html" class="uc-prompt-link">Build a workflow →</a>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="uc-card" data-dept="platform">
|
||
<div class="uc-card-top">
|
||
<span class="uc-dept-tag" style="background:rgba(124,58,237,.12);color:var(--purple)">💬 Secure Chat Rooms</span>
|
||
<span class="uc-time-saved">⏱ Faster incident response</span>
|
||
</div>
|
||
<h3 class="uc-title">IT incident response room with AI co-pilot</h3>
|
||
<p class="uc-scenario">During a critical infrastructure outage, the IT team created a dedicated Secure Chat Room for the incident. The AI was connected to the system knowledge base (runbooks, architecture docs, past incident reports) and participated in the chat — answering technical questions in real time, suggesting diagnostic steps, and drafting the incident report as the conversation progressed. All messages stayed on-premises.</p>
|
||
<div class="uc-outcome">
|
||
<span class="uc-outcome-label">Outcome</span>
|
||
Mean time to resolution reduced. The AI surfaced a relevant past incident from 18 months ago that pointed directly to the root cause — knowledge that would have otherwise required contacting a staff member who had since left.
|
||
</div>
|
||
<div class="uc-footer">
|
||
<div class="uc-tools">
|
||
<span class="tag">Chat Rooms</span>
|
||
<span class="tag">Knowledge Base</span>
|
||
<span class="tag">RAG</span>
|
||
</div>
|
||
<a href="chatrooms.html" class="uc-prompt-link">Create a room →</a>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="uc-card" data-dept="platform">
|
||
<div class="uc-card-top">
|
||
<span class="uc-dept-tag" style="background:rgba(124,58,237,.12);color:var(--purple)">📄 Document Intelligence</span>
|
||
<span class="uc-time-saved">⏱ 2 days → 2 hours</span>
|
||
</div>
|
||
<h3 class="uc-title">Batch extraction of data from 80 survey forms</h3>
|
||
<p class="uc-scenario">The HR team received 80 completed employee survey forms in PDF format. Manually entering responses into a spreadsheet would have taken two full working days. Using Document Intelligence's batch processing, all 80 forms were uploaded at once. The AI extracted structured data from each — names, scores, text responses, department codes — and produced a consolidated CSV ready for analysis.</p>
|
||
<div class="uc-outcome">
|
||
<span class="uc-outcome-label">Outcome</span>
|
||
Two days of data entry collapsed to two hours including review and QA. Survey results were in the analyst's hands the same day forms were received.
|
||
</div>
|
||
<div class="uc-footer">
|
||
<div class="uc-tools">
|
||
<span class="tag">Document Intelligence</span>
|
||
<span class="tag">Batch Processing</span>
|
||
</div>
|
||
<a href="documents.html" class="uc-prompt-link">Try Document Intelligence →</a>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="uc-card" data-dept="platform">
|
||
<div class="uc-card-top">
|
||
<span class="uc-dept-tag" style="background:rgba(124,58,237,.12);color:var(--purple)">🖼️ Multimodal Chat</span>
|
||
<span class="uc-time-saved">⏱ Infrastructure review simplified</span>
|
||
</div>
|
||
<h3 class="uc-title">Interpreting technical diagrams and network maps</h3>
|
||
<p class="uc-scenario">The operations team frequently receives dense technical diagrams — network topology maps, P&IDs, electrical single-line diagrams — from contractors that require interpretation. By uploading the diagram image to Multimodal Chat with LLaVA, staff can ask "what does this show?", "identify the redundancy paths", or "what's missing from this network design?" — and receive an expert-level technical summary without waiting for an engineer to be available.</p>
|
||
<div class="uc-outcome">
|
||
<span class="uc-outcome-label">Outcome</span>
|
||
Non-technical staff can independently pre-assess contractor submissions. One submission with an incomplete redundancy design was flagged before reaching the engineering team, saving a full review cycle.
|
||
</div>
|
||
<div class="uc-footer">
|
||
<div class="uc-tools">
|
||
<span class="tag">Multimodal Chat</span>
|
||
<span class="tag">LLaVA Vision</span>
|
||
</div>
|
||
<a href="chat-multi.html" class="uc-prompt-link">Try Multimodal Chat →</a>
|
||
</div>
|
||
</div>
|
||
|
||
</div><!-- end uc-grid -->
|
||
|
||
<!-- BOTTOM CTA -->
|
||
<div class="uc-cta">
|
||
<h3>Ready to try these yourself?</h3>
|
||
<p>The Prompt Library has ready-to-use prompts for every scenario above — copy, paste into Open WebUI, and upload your document.</p>
|
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|
||
<a href="prompts.html" class="btn-primary" style="text-decoration:none">Browse Prompt Library →</a>
|
||
<a href="quickstart.html" class="btn-secondary" style="text-decoration:none">Quick Start Guide</a>
|
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|
||
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|
||
|
||
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|
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|
||
<footer>
|
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<p>Nexus One AI · Powered by Cezen · Basic Tier</p>
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<p>Questions? <a href="mailto:support@cezentech.com">support@cezentech.com</a> · <a href="https://cezentech.com" target="_blank">cezentech.com</a></p>
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