Product Design
Bluestar Fleet Management
Transformed a fragmented system into a unified experience, and reduced the manual efforts to ~80%
work in progress.
Nature of Work
freelance
Timeline
6 months
Status
beta launched
Team
2 designers
HIGHLIGHT
designed built by a designer.
Supabase
BACKEND
Figma MCP
DESIGN
Claude Code
AGENTIC AI
Cursor
IDE
Vercel
SERVER
CONTEXT
chaos in operations
Bluestar operates a dispatcher-led fleet system for high-volume, pre-scheduled transport.
AT THE TIME
Most of Bluestar’s workflow - from trip booking to dispatch, driver assignment, and payment reconciliation was manual and fragmented.
Leading to inefficiencies, delays, and limited visibility into operations.
PERSONA IN FOCUS
operators. the nervous system of the fleet
60–80 vehicles per operator
~80% of operations were manual
~35% of effort spent reconciling data
BOOKINGS
DISPATCH
PAYMENTS $ BILLINGS
MAINTANENCE
FLEET LOG AND TRACKING
Often out of sync data
Required manual coordination between teams.
Reconciled not in real time - errors compounded
PROBLEM: Operations were breaking WITH scale.
CONTEXT
chaos in operations
Bluestar operates a dispatcher-led fleet system for high-volume, pre-scheduled transport.
AT THE TIME
Most of Bluestar’s workflow - from trip booking to dispatch, driver assignment, and payment reconciliation was manual and fragmented.
Leading to inefficiencies, delays, and limited visibility into operations.
RESEARCH
understanding the problem in the current scenario
Before delving deeper into the research, it was essential to first understand the ecosystem of the operations
RESEARCH
RESEARCH
understanding the problem in the current scenario
understanding the problem in the current scenario
POST DELIVERY FINDINGS
key business metrics did not improve
fragmented → unified
WHAT WENT WELL
Manual operations → Almost half.
Booking conflicts reduced from 12–15% → ~4–6%
WHAT DID NOT GO WELL
Fleet utilization percentage did not improve
APPROACH
design process
01
booking created
A job request is logged in the platform replacing manual calls and spreadsheet entries.
02
driver and vehicle assigned
The duty gets assigned to an available driver automatically- they get an update on the driver centric PWA
03
duty executed
Booking level and duty level status for the operator and a reliable system
No real-time, reliable visibility
incorrect next assignments | cascading delays
→
04
billing triggered
On duty completion, an invoice is
generated automatically — no manual
data entry, no delays.
RESEARCH
understanding the problem in the current scenario
Before delving deeper into the research, it was essential to first understand the ecosystem of the operations
CURRENT DUTY
TIME →
NEXT DUTY
delay for expected reasons (such as traffic)
time it takes to reach next duty
driver informed completion here
BUFFER
STATUS CHANGE TO OPERATOR
NEED VISIBILITY HERE
→
→
Dispatched
Passenger picked
Completed
RESEARCH
understanding the problem in the current scenario
Before delving deeper into the research, it was essential to first understand the ecosystem of the operations
CURRENT DUTY
TIME →
NEXT DUTY
delay for expected reasons (such as traffic)
time it takes to reach next duty
driver informed completion here
BUFFER
STATUS CHANGE TO OPERATOR
NEED VISIBILITY HERE
→
→
Dispatched
Passenger picked
Completed
NEXT DUTY
CURRENT DUTY
CURRENT DUTY
6:00 PM
6:08 PM
BUFFER
45 MINS
7:00 PM
7:30 PM
6:30 PM
7:45 PM
7:45 PM
DRIVER A
DRIVER B
COULD COVER DRIVER A’s DUTY
delay for expected reasons (such as traffic)
hello
hola
salut
prego
namaste
ni hao
olá
ciao
s̄wạs̄dī
hallo
Hey, thanks for scrolling all the way till here! Let's have a chat? I am open to collaboration and opportunities.
Just say hi!
yashvardhanbhardwaj@outlook.com
YASHVARDHAN
Bhardwaj
Copyrights 2025. All rights & wrongs reserved
hello
hola
salut
prego
namaste
ni hao
olá
ciao
s̄wạs̄dī
hallo
Hey, thanks for scrolling all the way till here! Let's have a chat? I am open to collaboration and opportunities.
Just say hi!
yashvardhanbhardwaj@outlook.com
YASHVARDHAN
Bhardwaj
Copyrights 2025. All rights & wrongs reserved
60–80 vehicles per operator
~80% of operations were manual
~35% of effort spent reconciling data