AuroraSafe
Monetization Strategy

The Business of Safety

The woman using AuroraSafe rarely pays directly. Instead, whoever is already responsible for her safety pays — her employer, a cab platform, a CSR budget, or eventually the city.

The Core Principle

We don't charge the scared user directly — free, trusted government alternatives like 112 and Himmat already exist. Our revenue is generated by shifting the financial burden to institutional stakeholders who use our predictive intelligence API to reduce their own liabilities.

Execution Pipeline: The 5 Revenue Streams

01

B2B API Licensing

Cab Aggregators, Transport Vendors
Example Transaction
CityCabs pays ₹1–2/API call or ₹50,000/month flat
Why they pay
Liability reduction + safety as a marketing differentiator.
02

B2B2C Corporate Subscriptions

BPOs, IT Parks, Hospitals
Example Transaction
500 employees × ₹80/month = ₹40,000/month
Why they pay
POSH Act / duty-of-care compliance, cheaper than dedicated corporate cabs.
03

CSR Partnerships

Large Corporations (CSR Budgets)
Example Transaction
GlowCo pays ₹15 lakh/year for city-wide free public access
Why they pay
Legal 2% net-profit CSR obligation (Companies Act) needs an impactful home.
04

Freemium Consumer Tier

Individual Users
Example Transaction
₹49–99/month for "Walk-With-Me" / Stealth Mode features
Why they pay
Low-friction, guilt-free spend for extreme reassurance (Secondary revenue).
05

B2G Dashboard Subscriptions

Police, Smart City Municipalities
Example Transaction
₹8–15 lakh/year per district pilot
Why they pay
Data-driven policing is a priority (e.g., UP Dial 100). Needs reference clients first.
Fastest closing & highly defensibleSlower sales cycle (B2G requires reference customers)

The Honest Risk & The Defensive Moat

The Obvious Threat

Free government apps are heavily funded and trusted. If our only feature is an "SOS Button", we will fail against state-sponsored alternatives. No one pays just for a panic button.

The Defensive Moat

Our moat isn't reactive dispatch. It is Predictive Avoidance. We don't just react; we predict, re-route, and escalate dynamically using live ML models for micro-regions that legacy apps are too slow to map. Combined with our physical phone-snatch/impact failsafe, it's an intelligence capability the state doesn't have yet.