A system built for someone else.
Ravi drives for Swiggy in Mumbai. On a good Tuesday he makes ₹1,800. On a slow monsoon Monday, ₹400. His rent is due on the 5th regardless.
Banks require minimum balances he can't always maintain. Budgeting apps ask him to set monthly limits on income that changes daily. Savings products penalise him for withdrawing during a lean week.
The tools aren't broken. They were just never designed for him.
"Your money is growing" (teal = financial health, not bank blue)
"This is yours" (piggy bank = personal savings, cultural familiarity)
"No judgment" (calm colors, no aggressive CTAs or red warnings)
"Clear, not complicated" (restrained palette, strong hierarchy)
Trust Teal
#0F766E
Ink Blue
#1E293B
#6AF9C9
#FFC563
#66BFFF
#FF8888
Percentages explained, not hidden. User overrides encourage
I created an interactive Figma prototype with working drag-to-allocate flows and tested it with 8 gig workers recruited through Uber driver forums and delivery platforms.
→ 3 Uber drivers (2-4 years experience)
→ 3 Swiggy/Zomato delivery riders
→ 2 freelancers (graphic designer, tutor)
✓ 7/8 preferred flexible pots over fixed monthly budgets
✓ Drag-to-allocate tested faster than dropdown menus
✓ Transparent percentages built more trust than "AI suggestions"
✓ Users loved naming their own pots ("Eid Shopping," "Bike Repairs")
"This is exactly how I think about money in my head. Finally someone gets it."
— Uber driver, 3 years
Validated: Weekly rhythm • User-defined pots • Visible math • Shame-free language
What This Solves
Traditional fintech forces gig workers into salaried-employee patterns—minimum balances they can't maintain, monthly budgeting when they earn daily/weekly, penalties for withdrawing during lean weeks. Paisev treats irregular income as the default, not the exception.
What I Learned
→ User research must extend beyond surface questions to behavioural patterns
→ Regulatory frameworks (Account Aggregator) can inspire ethical design
→ Limited color palettes with restraint outperform visual chaos
→ Transparency in algorithms builds trust with underbanked users
If This Were Real
Success Metrics
→ Time to complete first allocation (target: under 2 minutes)
→ Frequency of pot adjustments (high = adapting to real life)
→ % of users who override AI suggestions (transparency working)
→ Retention vs. traditional budgeting apps
Through this project, I identified a massive underserved market:
→ 77M gig workers in India (15% annual growth)
→ Zero products tailored to irregular income
→ 4-6 daily transactions per worker
By respecting how users naturally manage money, I proved we can serve underrepresented markets ethically and profitably.
Business Model Potential:
•Freemium (₹99/month premium)
•Platform partnerships
• Micro-transaction fees
Key Learning:
Financial inclusion isn't just ethical—it's an untapped business opportunity. I can spot underserved markets and design sustainable solutions.
