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Case Study2026

XPO Fleet Intelligence

Custom-built fleet intelligence dashboard

A full-scale fleet management platform built from original research. 23 pages covering analytics, monitoring, risk management, service programs, and AI-assisted insights — all with typed mock data ready for API integration.

23

Pages Built

114

Source Files

29,000+

Lines of Code

0

TypeScript Errors

21

Entity Types

10

Research Docs

10,647

Research Lines

16

Data Exports Analyzed

Technology Stack

FrameworkNext.js 15 (App Router)
UI LibraryReact 19 + shadcn/ui
StylingTailwind CSS 4
ChartsRecharts (6 chart types)
LanguageTypeScript (strict mode)
Data LayerTyped mock data (API-swap ready)

Methodology

This platform was built through a structured research-first approach — not from a template or boilerplate.

Phase 0: Research & Knowledge Building

Analyzed 84 screenshots from the live EBAI platform, processed 16 real fleet data exports, and produced 10 research documents (10,647 lines) covering domain analysis, UX patterns, data modeling, technology selection, and competitor evaluation.

Phase 1-2: Architecture & Data Layer

Designed 15 TypeScript entity types from scratch based on real fleet operational patterns. Generated mock data for 32 vehicles, 55 drivers, 4 PM programs, and 22 LAX-area zones — all matching actual telematics data distributions.

Phase 3-10: Implementation & Polish

Built 23 pages across 7 functional areas with 24 UI components, 6 custom chart types, a compliance engine, PM alert system, and dark-first design system. Zero TypeScript errors across the entire codebase.

Original Features

These features were designed from original research and are not found in the reference platform (EBAI):

Risk Management Center

6-tier compliance expiration tracking (90/60/30/7-day alerts) across vehicle documents, driver documents, and enterprise documents. Not available in EBAI or competing platforms.

Service Programs Engine

Vehicle-type-specific PM schedules with 59 service items at mileage/time intervals derived from real maintenance data. Covers E-450 (Gas/CNG/EV) and Freightliner M2-106 Diesel.

Digital Inspections

Pre-Trip (38 items) + CHP 45-Day (48 items with 381 failure mode dropdowns) — digitized from actual paper forms and CHP 34505 CVC requirements.

Research Documents

DocumentLines
Domain Research1,102
UX Patterns806
Data Model3,194
Design Brief214
shadcn/ui Reference1,673
Animation Patterns940
Mapping Reference985
Next.js Patterns1,259
EBAI Screenshot Analysis343

Data Sources Analyzed

16 real fleet telematics exports were processed to generate realistic mock data:

Baseline fleet data (trips, hours, fuel, miles)
Violation events (356 events, 4 categories)
Vehicle faults (1,366 faults across 19 vehicles)
Utilization heatmap (24x7 hourly grid)
Fuel transactions and cost breakdowns
Zone visits and dwell time analysis
Mileage reports by vehicle
Idling sessions and duration
Anomaly detection results
Right-size operations recommendations
Route performance data
Passenger counter data
Camera event logs
All recommended assets inventory
EBAI AI assistant response samples
Dwell time analysis by zone