@extends('layouts.app') @section('title', 'Use Cases | Data Intellec') @section('description', 'Anonymized BI and AI use cases across manufacturing, FMCG, financial services and enterprise operations — real delivery patterns, organized by solution type and industry.') @push('styles') @endpush @php // Real content sourced from the client's own pitch deck (DataIntellec - // 2026.pdf, 2026-08-27) — nine real delivery patterns, condensed from // each case's Pain Points / Solution / Business Outcome bullets. No // client names, industries or metrics were invented: the deck names an // industry for the three Real-time BI cases only, so the other six // (which the deck describes without a named vertical) are tagged // "Enterprise" rather than forced into a fabricated industry. There are // no real White-label case studies yet, so that solution filter was // dropped rather than left pointing at an always-empty result. $solutionFilters = [ ['key' => 'all', 'label' => 'All solutions'], ['key' => 'real-time-bi', 'label' => 'Real-time BI'], ['key' => 'ai-over-bi', 'label' => 'AI over BI'], ['key' => 'ai-agents', 'label' => 'AI Agents'], ]; $industryFilters = [ ['key' => 'all', 'label' => 'All industries'], ['key' => 'manufacturing', 'label' => 'Manufacturing'], ['key' => 'fmcg', 'label' => 'FMCG'], ['key' => 'financial-services', 'label' => 'Financial Services'], ['key' => 'enterprise', 'label' => 'Enterprise'], ]; $useCases = [ [ 'solution' => 'real-time-bi', 'solutionLabel' => 'Real-time BI', 'industry' => 'manufacturing', 'industryLabel' => 'Manufacturing', 'title' => 'Manufacturing Command Center', 'pain' => 'Production data was spread across ERP, MES, IoT devices and Excel reports, leaving plant managers without real-time visibility into machine performance and downtime.', 'outcome' => 'Single operational view across all manufacturing plants', ], [ 'solution' => 'real-time-bi', 'solutionLabel' => 'Real-time BI', 'industry' => 'fmcg', 'industryLabel' => 'FMCG', 'title' => 'FMCG Sales Intelligence Hub', 'pain' => 'Sales data was distributed across distributors, ERP, CRM and retailer systems, with no visibility into primary vs. secondary sales until stock-outs were already late.', 'outcome' => 'Improved sales visibility across all regions', ], [ 'solution' => 'real-time-bi', 'solutionLabel' => 'Real-time BI', 'industry' => 'financial-services', 'industryLabel' => 'Financial Services', 'title' => 'NBFC Executive Risk Dashboard', 'pain' => 'Lending, collections and portfolio reports were generated manually, with credit risk monitored using delayed spreadsheets.', 'outcome' => 'Faster executive reporting and improved portfolio risk monitoring', ], [ 'solution' => 'ai-over-bi', 'solutionLabel' => 'AI over BI', 'industry' => 'enterprise', 'industryLabel' => 'Enterprise', 'title' => 'AI-Powered Executive Summaries', 'pain' => 'Leadership teams spent hours interpreting dashboards, and weekly management summaries required significant manual effort.', 'outcome' => 'Significant reduction in reporting effort', ], [ 'solution' => 'ai-over-bi', 'solutionLabel' => 'AI over BI', 'industry' => 'enterprise', 'industryLabel' => 'Enterprise', 'title' => 'Demand Forecasting Engine', 'pain' => 'Forecasts relied heavily on historical averages and manual planning, causing overstocking and stock shortages.', 'outcome' => 'Improved forecast accuracy and optimized inventory planning', ], [ 'solution' => 'ai-over-bi', 'solutionLabel' => 'AI over BI', 'industry' => 'enterprise', 'industryLabel' => 'Enterprise', 'title' => 'AI Risk & Opportunity Detection', 'pain' => 'Business risks were discovered only after affecting operations, with no automated way to identify anomalies or revenue leakage.', 'outcome' => 'Early risk detection and increased revenue opportunities', ], [ 'solution' => 'ai-agents', 'solutionLabel' => 'AI Agents', 'industry' => 'enterprise', 'industryLabel' => 'Enterprise', 'title' => 'Executive AI Copilot', 'pain' => 'Executives depended on analysts for reports, and business questions required manual dashboard exploration.', 'outcome' => 'Instant business insights and reduced dependency on analysts', ], [ 'solution' => 'ai-agents', 'solutionLabel' => 'AI Agents', 'industry' => 'enterprise', 'industryLabel' => 'Enterprise', 'title' => 'Business Intake & BCD Agent', 'pain' => 'Business requirement gathering was manual and inconsistent, and project approvals were delayed by documentation effort.', 'outcome' => 'Faster project initiation and standardized documentation', ], [ 'solution' => 'ai-agents', 'solutionLabel' => 'AI Agents', 'industry' => 'enterprise', 'industryLabel' => 'Enterprise', 'title' => 'Enterprise Knowledge Agent', 'pain' => 'Employees spent excessive time searching for policies, SOPs and project documents scattered across SharePoint and file servers.', 'outcome' => 'Faster knowledge discovery and reduced support workload', ], ]; $industryIcons = [ 'manufacturing' => '', 'fmcg' => '', 'financial-services' => '', 'enterprise' => '', ]; $industryImages = [ 'manufacturing' => '/images/factory-floor.webp', 'fmcg' => '/images/products/1.webp', 'financial-services' => '/images/why-2.webp', 'enterprise' => '/images/we-turn.webp', ]; $solutionIcons = [ 'all' => '', 'real-time-bi' => '', 'ai-over-bi' => '', 'ai-agents' => '', ]; @endphp @section('content')

Use cases

Our track record of success.

Nine delivery patterns across real-time BI, AI over BI and AI agents — anonymized because the real ones are under NDA, specific because that's the point.

Solution type
@foreach ($solutionFilters as $index => $filter) @endforeach
Industry
@foreach ($industryFilters as $index => $filter) @endforeach
@foreach ($useCases as $case)
{{ $case['industryLabel'] }} {{ $case['solutionLabel'] }}

{{ $case['title'] }}

{{ $case['pain'] }}

Outcome {{ $case['outcome'] }}
@endforeach

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