Tracking Control Tower
Live shipment map with ETA, carrier status, lane health, and exception queues.
Logistics / B2B
Jhatpat Logistics
A B2B logistics command center that gives shippers, carriers, and operations teams real-time shipment visibility.
Year
2023
Team Size
9 engineers
Timeline
8 months
Technology
React, Go, Kafka
The Challenge
Jhatpat Logistics had strong carrier relationships but limited digital visibility. Shipment updates came through phone calls, spreadsheets, and delayed carrier portals, making exception management reactive.
Enterprise customers wanted proactive ETA alerts, lane performance analytics, and a single operational view across regions. Internal teams were spending too much time chasing updates instead of preventing delays.
The business needed a scalable visibility platform that could ingest GPS, carrier, warehouse, and order events while maintaining reliability during peak shipment windows.
Client Name
Jhatpat Logistics
Industry
Logistics / Supply Chain
Engagement Type
B2B Platform Build
Timeline
8 months
Team Size
9 engineers, 1 consultant
Budget Range
$500K-$700K
Status
Live, market leader
Live Users
5,000+ business users
50K+
Shipments / Day
99.99%
Platform Uptime
5K+
Business Users
How We Solved It
The Solution
We built an event-driven logistics platform with a React command center, Go microservices, Kafka event streams, and map-based tracking. The platform normalizes carrier feeds and turns them into reliable shipment milestones.
Operational workflows were designed around exceptions: late departures, route deviations, dwell time, and missed delivery windows. Customers gained self-service visibility while Jhatpat teams gained a control tower for proactive support.
Event Ingestion Pipeline
Kafka streams normalize GPS, order, warehouse, and carrier status events.
Customer Visibility Portal
Role-based dashboards and alerts reduce support calls and improve SLA confidence.
Months 1-3
Control Tower MVP
Complete
Tracking map, shipment list, alerts
First 5,000 shipments/day
Months 4-6
Carrier Integrations
Complete
GPS feeds, event pipeline, customer portal
40+ carrier feeds
Months 7-8
Scale & Analytics
Complete
Lane analytics, SLA dashboards, uptime hardening
99.99% uptime
Technology
Architecture & Tech Stack
Frontend
React
API Gateway
Secure APIs
Backend
Go
Data Layer
PostgreSQL
Integrations
Secure APIs
Frontend
- React
- TypeScript
- Map UI
- Tailwind CSS
Backend
- Go
- Node.js
- gRPC
- REST APIs
Data & Storage
- PostgreSQL
- Kafka
- Redis
- BigQuery
Infrastructure
- GCP
- Kubernetes
- Docker
- Terraform
Tools & Services
- Google Maps API
- Datadog
- GitHub Actions
- PagerDuty
Features
What We Built
Live Shipment Map
Tracks shipment position, ETA, route deviation, and delivery windows in real time.
Exception Workbench
Prioritizes late, stalled, or high-risk shipments for operations teams.
Carrier Feed Normalization
Converts inconsistent carrier events into a clean logistics event model.
Customer Portal
Gives customers role-based visibility into their own shipments and SLAs.
Lane Analytics
Measures lane performance, dwell time, carrier reliability, and cost leakage.
Proactive Alerts
Sends email, SMS, and dashboard alerts before SLA misses become escalations.
Results
The Impact
The platform now processes more than 50,000 shipments per day and serves over 5,000 business users. Jhatpat reduced support calls, improved SLA transparency, and gave enterprise customers the visibility they had been requesting.
The event-driven architecture sustained peak windows with 99.99% uptime and gave the business a foundation for predictive ETAs and automated carrier scoring.
50K+
Shipments / Day
Across active lanes
5K+
Business Users
Customer and ops users
99.99%
Uptime
Peak season validated
-41%
Support Calls
After portal launch
Return on Investment
- Manual tracking work reduced by 46%.
- Customer support volume dropped by 41%.
- Enterprise renewal conversations improved with live SLA evidence.
- Carrier performance analytics identified underperforming lanes within 30 days.
The control tower changed how our customers experience logistics. Everyone sees the same truth now.
Operations Director, Jhatpat Logistics
The Team
People Behind the Project
MR
Meera Rao
Product Lead
B2B workflows, logistics operations
VS
Vikram Sethi
Backend Lead
Go, Kafka, distributed systems
NK
Nina Kapoor
Frontend Engineer
Maps, dashboards, accessibility
OK
Omar Khan
Cloud Engineer
GCP, reliability, observability
Challenges & Lessons
Overcoming Obstacles
Inconsistent Carrier Data
Each partner used different milestone labels and update intervals.
Created a canonical logistics event model.
Normalize the domain before optimizing the UI.
Peak Load Reliability
Shipment updates spiked during regional dispatch windows.
Buffered ingestion with Kafka and backpressure controls.
Logistics systems need graceful spikes by default.
Map Performance
Dense map views became slow with thousands of moving assets.
Clustered markers and split live feeds by viewport.
Visual scale needs its own architecture.
Evolution
From Launch to Continuous Improvement
Month 4
Launch
Complete
Core tracking
First enterprise accounts
Month 8
Scale
Complete
Carrier integrations
50K shipments/day
Months 9-12
Optimization
Complete
Lane analytics
41% fewer support calls
Future
Predictive
Planned
Predictive ETAs and carrier scoring
Earlier exception prevention
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