An Enterprise-Grade, Autonomous Fleet Intelligence Operating System.
🎬 5-Min YC Demo Playbook • 📖 Feature Showcase • ⚡ Quick Start • 🧠 Intelligence Layer • 🌟 Wow Factor
TransitOps is a production-grade, AI-powered Fleet Operations Intelligence Platform designed for modern commercial transport enterprises.
It digitizes the complete lifecycle of commercial fleet operations — from vehicle dispatch and driver compliance enforcement to real-road GPS simulation, predictive AI maintenance, digital twin health scoring, and executive carbon emissions reporting.
Most fleet tools are basic tracking apps. TransitOps is a full-stack enterprise intelligence platform that combines real-road telemetry, AI copilot automation, and predictive digital twins.
| What Others Build | What TransitOps Builds |
|---|---|
| Vehicle list + driver table | Fleet Digital Twin — live health score per asset (A+ to D grade) |
| Basic trip form | AI Smart Dispatch Engine — multi-criteria vehicle scoring, top 3 recommendations |
| Static map with pins | Live OSRM Road Simulation — 25 vehicles following actual National Highway geometries |
| Manual reports | Executive PDF Briefing — auto-generated board-ready report, one click |
| No AI | Groq AI Incident Investigator — structured root cause analysis from telemetry |
| No predictive features | Predictive Maintenance Queue — AI-ranked failure forecast per vehicle |
| No sustainability | Carbon Emissions Dashboard — CO₂ per corridor, ESG-rated asset ranking |
| Cinematic Landing Hero | Executive Command Dashboard |
|---|---|
|
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| SaaS Landing Page with Full-Motion Haulage Video | Financial KPIs, Smart Alerts & Instant PDF Board Export |
| Live OSRM Road Map & AI Copilot | Haulage Highway Corridors |
|---|---|
 |
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| 25 assets on Eastern India roads with Llama 3.3 Copilot | Live corridor telemetry, Geofence hubs & ETAs |
| BI Revenue & Financial Analytics Suite |
|---|
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| Recharts dynamic revenue, fuel cost & ROI analytics across all 25 commercial haulage assets |
TransitOps is engineered as a modern, high-concurrency enterprise logistics operating system combining client-side real-road telemetry simulation with sub-second AI cloud inference.
graph TD
subgraph Client ["Enterprise Frontend App Router"]
UI["React 19 Executive UI Suite"]
RBAC["6-Tier RBAC Auth Gateway"]
Map["Leaflet & OpenStreetMap Live GPS Engine"]
CopilotUI["AI Fleet Copilot Drawer"]
end
subgraph Intelligence ["7-Layer Autonomous Intelligence Engine"]
DigitalTwin["Fleet Digital Twin Health Grader"]
SmartDispatch["AI Smart Dispatch & Vehicle Matcher"]
PredictiveMaint["Predictive Maintenance Risk Evaluator"]
DriverRisk["Driver Compliance & Risk Engine"]
RoadRouting["OSRM Real-Road Highway Routing"]
CarbonCalc["Statutory CO2 & ESG Calculation Engine"]
FinancialROI["Haulage Corridor ROI Yield Model"]
end
subgraph AICloud ["Groq Sub-Second AI Cloud"]
Llama["Llama 3.3 70B Versatile LLM"]
IncidentAI["Groq AI Incident Investigation Pipeline"]
end
subgraph DataLayer ["Enterprise Unified Store"]
Telemetry["25 Live GPS Telemetry Profiles"]
Corridors["Eastern India NH Corridors & Geofence Hubs"]
AuditLog["Immutable Enterprise Audit Ledger"]
end
UI --> RBAC
RBAC --> Map
Map --> Telemetry
Telemetry --> DigitalTwin
Telemetry --> RoadRouting
DigitalTwin --> SmartDispatch
DigitalTwin --> PredictiveMaint
CopilotUI --> Llama
IncidentAI --> Llama
Telemetry --> CarbonCalc
Telemetry --> FinancialROI
sequenceDiagram
autonumber
actor Dispatcher as Fleet Manager / Dispatcher
participant Map as Live Operations Console
participant Copilot as AI Fleet Copilot
participant Telemetry as OSRM Telemetry Pipeline
participant Asset as WB-04-G-7712 (Field Asset)
Dispatcher->>Map: Open Live Operations Center (/live-operations)
Map->>Telemetry: Stream 25 Commercial Vehicle GPS Coordinates
Telemetry-->>Map: Render Real Highway Geometries (NH-12, NH-19)
Dispatcher->>Copilot: Ask "Show vehicles near Durgapur"
Copilot->>Telemetry: Query Geofence Hubs & Idling Assets
Telemetry-->>Copilot: Identify Idling Refrigerated Truck WB-04-G-7712
Copilot->>Map: Trigger Interactive Auto-Navigation FlyTo
Map->>Asset: Pan Map & Open Active Asset Telemetry Console
Copilot-->>Dispatcher: Display Asset Health, Speed & Click-to-Inspect Pills
flowchart LR
A["Freight Trip Request"] --> B{"Evaluate 25 Fleet Assets"}
B --> C["Engine Health Grade (45%)"]
B --> D["Fuel Reserves (35%)"]
B --> E["Driver Safety Score (20%)"]
C --> F["Digital Twin Match Score"]
D --> F
E --> F
F --> G["Rank Top 3 Autonomous Recommendations"]
G --> H["One-Click Dispatch & RTO Verification"]
# Clone the repository
git clone https://github.com/ayushkumar2601/odoo-TransitOps.git
cd odoo-TransitOps
# Install dependencies
npm install
# Start the backend AI service
node backend/server.js &
# Start the development server
npm run devOpen: http://localhost:3000
No database setup required. TransitOps runs fully on a production-grade centralized mock data layer. Every feature works out of the box.
| Role | What You'll See |
|---|---|
| 🔴 Fleet Manager | Full command center — all modules, digital twins, war room access |
| 🔵 Dispatcher | Trip dispatching with AI recommendations, vehicle pool |
| 🟡 Safety Officer | Driver compliance dashboard, license audit board |
| 🟢 Financial Analyst | ROI scorecard, fuel expenses, executive briefing |
| ⚫ Driver | Personal portal — own trips, safety score, vehicle assignment |
| 🔮 Admin | Full master oversight across all operational towers |
7 proprietary algorithmic engines — this is what separates TransitOps from every other fleet app.
Every commercial vehicle gets a live digital health profile — not just status flags.
Health Score = (Engine Health × 0.45) + (Safety Score × 0.35) + (ROI Score × 0.20)
− (Alert Penalties) − (Odometer Wear Penalty) − (Status Penalty)
Output: Health Score (0–100) · Letter Grade (A+ to D) · Lifecycle Stage · Breakdown Risk % · Next Service Days
Lifecycle Stages: New Asset → Peak Efficiency → Mature Asset → High Maintenance → Retirement Candidate
Before a vehicle breaks down on NH-12, TransitOps already knows it's going to.
The AI ranks all fleet assets by failure probability using 5 degradation signals (odometer wear, fuel efficiency decay, engine health, open alerts, lifecycle stage) with 82–96% confidence.
Output: Sorted Critical → Moderate → Low queue, predicted service date, root reason array
When creating a trip, the AI immediately recommends the 3 best vehicles — before the dispatcher even starts searching.
Match Score = (Health Score × 0.45) + (Fuel Level × 0.35) + (Safety Score × 0.20)
+ Grade Bonus + Availability Bonus
Output: Top 3 vehicles with Match Score %, click-to-autofill integration
Every driver gets an AI risk profile — updated live from telemetry.
Risk Level: Low · Medium · High
Visible as: Color-coded AI Risk Badges on every driver card
Vehicles follow actual Indian National Highways — not straight lines between coordinates.
Routes are derived from live OSRM queries against the OpenStreetMap road network, with fallback to 10 prebuilt NH geometries for zero-latency demo performance.
Covered corridors: NH-12 (Kolkata→Siliguri) · NH-19 (Howrah→Ranchi) · NH-16 (Kharagpur→Bhubaneswar) · NH-49 (Durgapur→Jamshedpur) · NH-27 (Asansol→Patna)
CO₂ (kg) = Diesel Consumed (Liters) × 2.68
Using the statutory diesel emission factor (IPCC/MoEFCC standard), TransitOps calculates per-vehicle carbon footprint and fleet-wide monthly CO₂ totals — with carbon savings tracked from route optimization.
Vehicle ROI (%) = (Revenue − Fuel Cost − Maintenance Cost) / Acquisition Cost × 100
All 25 vehicles ranked by actual ROI yield. Displayed in the War Room bottom bar and Executive Briefing.
A full-screen TV-ready executive control room — black background, glowing KPI bar, live OSM fleet map at 70% screen width, real-time incident feed, and predictive workshop queue. Auto-refreshes every 5 seconds. Fullscreen mode hides the sidebar entirely for wall-display deployment.
Select any historical trip. Press play. A commercial truck travels its exact OSRM road corridor — with live fuel burn accumulation (0.38L/waypoint), automatic checkpost event logging, and ETA countdown. Speed controls: 1x · 2x · 5x · 10x.
Toggle 🔥 Hub Heatmap on the Live Operations map. Five Eastern India logistics hubs bloom with multi-ring thermal density overlays — red thermal core, amber density ring, blue perimeter halo — turning a plain map into a vehicle density intelligence layer.
One click downloads a professionally formatted A4 PDF — compiled live from real fleet data — covering Fleet Utilization · Revenue vs. OPEX · Digital Twin Top Performers · Predictive Maintenance Queue · Driver Compliance Audit · Carbon Sustainability Summary.
Signed: "Confidential • TransitOps AI Platform • Approved for Board Review."
Ask in plain English: "Why is WB-38-F-9102 delayed?"
The AI returns a structured incident report — not a chatbot response:
📍 Root Cause: Engine thermal overheating on NH-19 upgrade segment
⚡ Contributing Factors:
• Engine health critical at 42%
• Cargo payload 11,000 kg on sustained gradient
• Active Alert: ENGINE_OVERHEAT_CRITICAL
📊 Operational Impact: Corridor SLA breach risk — Dhanbad Mining Hub
✅ Recommended Actions:
• Dispatch emergency roadside repair unit
• Reassign backup tractor within 90 minutes
• Flag driver for thermal monitoring coaching
Step-by-step. Each step is 60 seconds. Optimized for maximum impact.
| Step | Screen | What to Show | Talking Point |
|---|---|---|---|
| 1 | /signin |
6 role cards, click Fleet Manager | "6 roles, each sees a completely different platform" |
| 2 | /dashboard |
KPI bar, utilization formula, alert feed | "Real-time operational command center" |
| 3 | /command-center |
War Room, hit fullscreen button | "This runs on a control room display wall" |
| 4 | /live-operations |
Toggle 🔥 Heatmap, click a moving truck | "25 vehicles following actual NH road geometries" |
| 5 | Vehicle Marker → Twin Modal | Health Score gauge, Grade, Lifecycle Stage | "Same digital twin technology as Caterpillar and John Deere" |
| 6 | /maintenance |
AI Predictive Service Queue | "AI ranked failure risk before the breakdown happens" |
| 7 | /replay |
Select TRP-101, 5x speed, watch fuel counter | "Historical fleet replay on real road geometry" |
| 8 | AI Copilot | Ask: "Why is WB-38-F-9102 delayed?" | "Root cause analysis in 3 seconds vs 30 minutes manually" |
| 9 | /trips → Create Trip |
Smart Dispatch AI panel | "AI recommends the best vehicle before you search" |
| 10 | /briefing + /sustainability |
Download PDF, show CO₂ chart | "Board-ready PDF report + statutory carbon audit" |
🔵 17 Core PRD Features (click to expand)
| Feature | Status |
|---|---|
| Secure Authentication & Session Management | ✅ |
| Role-Based Access Control (6 Roles) | ✅ |
| Executive Dashboard (Role-Adaptive) | ✅ |
| Vehicle Asset Registry (25 vehicles) | ✅ |
| Driver Personnel Governance (35 drivers) | ✅ |
| Trip Dispatching & Haulage Lifecycle (50 trips) | ✅ |
| Workshop & Maintenance Control | ✅ |
| Fuel Logs (120 records, 6-month history) | ✅ |
| Operational Expense Tracking (150 expenses) | ✅ |
| BI Analytics & Financial ROI | ✅ |
| CSV Import & Export Engine | ✅ |
| Vehicle Document Management (RC, Insurance, PUC, Fitness, Permit) | ✅ |
| Email Reminder System | ✅ |
| Enterprise Audit Log (immutable trail) | ✅ |
| Driver Self-Service Portal | ✅ |
| Dark Mode Premium UI | ✅ |
| Responsive Design (mobile → control room) | ✅ |
🟠 11 Enterprise Features (click to expand)
| Feature | Status |
|---|---|
| Global Command Palette Search (⌘K) | ✅ |
| Smart Alert Engine (8 alert types) | ✅ |
| Persistent Demo State (localStorage) | ✅ |
| RBAC Navigation & Data Isolation | ✅ |
| One-Click Demo Scenarios (5 scenarios) | ✅ |
| Vehicle Lifecycle State Machine (BR-001 to BR-013) | ✅ |
| Role-Specific Dashboard Variants | ✅ |
| Compliance Enforcement Engine | ✅ |
| Advanced Table Sorting & Filtering | ✅ |
| Maintenance Ticket Queue Management | ✅ |
| Vehicle Document Expiry Tracking | ✅ |
🤖 5 AI & Copilot Features (click to expand)
| Feature | Status |
|---|---|
| Groq AI Fleet Copilot (LLaMA-class) | ✅ |
| AI Incident Investigator (structured reports) | ✅ |
| AI Root Cause Analysis | ✅ |
| AI Fleet Analytics Interpretation | ✅ |
| AI Operational Recommendations | ✅ |
🧠 7 Intelligence Engines (click to expand)
| Engine | Status |
|---|---|
| Fleet Digital Twin Engine | ✅ |
| Predictive Maintenance Engine | ✅ |
| Smart Dispatch Recommendation Engine | ✅ |
| Driver Risk Engine | ✅ |
| ROI Ranking Engine | ✅ |
| Carbon Optimization Engine | ✅ |
| OSRM Real Road Route Engine | ✅ |
⭐ 10 Wow Factor Features (click to expand)
| Feature | Status |
|---|---|
| Live Fleet Operations Map (25 assets, OpenStreetMap) | ✅ |
| Real OSRM Road Route Simulation (5 NH corridors) | ✅ |
| Operations War Room (/command-center, TV mode) | ✅ |
| Fleet Replay Mode (1x/2x/5x/10x playback) | ✅ |
| Regional Hub Heatmap Layer (multi-ring thermal overlays) | ✅ |
| Digital Twin Modal (health gauge, grade, lifecycle) | ✅ |
| Executive Daily Briefing + PDF Export (pdf-lib) | ✅ |
| Carbon Sustainability Dashboard (ESG-rated rankings) | ✅ |
| AI Incident Investigator (structured incident reports) | ✅ |
| Smart Dispatch AI Panel (embedded in trip modal) | ✅ |
┌─────────────────────────────────────────────────────────────────┐
│ TransitOps Platform │
├──────────────────┬──────────────────┬───────────────────────────┤
│ Frontend │ AI Layer │ Intelligence Engine │
│ │ │ │
│ Next.js 16 │ Groq LLaMA │ Digital Twin Engine │
│ React 19 │ Fleet Copilot │ Predictive Maintenance │
│ TypeScript │ Incident AI │ Dispatch Recommender │
│ Tailwind CSS │ Root Cause AI │ Driver Risk Engine │
│ Recharts │ Fleet Analytics │ ROI Ranking Engine │
│ React Leaflet │ │ Carbon Optimizer │
│ OpenStreetMap ├──────────────────│ OSRM Route Engine │
│ │ Backend │ │
├──────────────────┤ ├───────────────────────────┤
│ Mock Data │ Express 5 │ Map & Routing │
│ │ Node.js │ │
│ 25 Vehicles │ REST API │ OpenStreetMap Tiles │
│ 35 Drivers │ Groq API Proxy │ OSRM Road Routing │
│ 50 Trips │ Centralized │ 5 NH Corridors │
│ 120 Fuel Logs │ Mock Store │ 5 Geofenced Hubs │
│ 150 Expenses │ │ 25 GPS Telemetry Assets │
└──────────────────┴──────────────────┴───────────────────────────┘
All 13 business rules (BR-001 to BR-013) are programmatically enforced at the data layer — not just UI warnings.
| Rule | Enforcement |
|---|---|
| BR-001 Unique Registration | Hard error on duplicate VIN/Reg |
| BR-002 Retired Vehicle Lock | Excluded from all dispatch pools |
| BR-003 In Shop Vehicle Lock | Excluded from all dispatch pools |
| BR-004 Expired License Lock | Driver blocked from assignment |
| BR-005 Suspended Driver Lock | Driver blocked from assignment |
| BR-006 Driver Single Assignment | Already On Trip = cannot dispatch |
| BR-007 Vehicle Single Assignment | Already On Trip = cannot dispatch |
| BR-008 Max Load Capacity Check | Cargo weight > capacity = hard reject |
| BR-009 Dispatch Transition | Vehicle + Driver → On Trip |
| BR-010 Completion Transition | Vehicle + Driver → Available |
| BR-011 Cancellation Transition | Vehicle + Driver → Available |
| BR-012 Maintenance Open Lock | Vehicle → In Shop, removed from pools |
| BR-013 Maintenance Close Release | Vehicle → Available (if not Retired) |
| Metric | Value |
|---|---|
| Lines of Code | ~279,000 |
| Operational Routes / Screens | 25 |
| React Components | 81 |
| Library Modules | 28 |
| Vehicles in Dataset | 25 |
| Drivers in Dataset | 35 |
| Trips in Dataset | 50 |
| NH Road Corridors | 10 |
| Geofenced Logistics Hubs | 5 |
| Intelligence Engines | 7 |
| AI Capabilities | 5 |
| Business Rules Enforced | 13 |
| Smart Alert Types | 8 |
| RBAC Roles | 6 |
| Total Implemented Features | 56+ |
| Document | Purpose |
|---|---|
TRANSITOPS_FEATURE_SHOWCASE.md |
📖 Enterprise product showcase & architectural analysis |
TECHNICAL_DOCUMENTATION.md |
🔧 Architecture, data models, and technical reference |
PHASE_1_6_TEST_REPORT.md |
🧪 Comprehensive Phase 1–6 verification & audit report |
Ayush Kumar
Built for Enterprise Haulage & Fleet Intelligence
"The goal was never to build a fleet management app.
The goal was to build the fleet management app."
⭐ If TransitOps impresses you, give it a star.