Make discovery and reward value obvious.
Users need to find useful merchants, understand incentives, and trust that rewards are tracked correctly.
Rewards Bunny case study
Building a rewards platform with users, merchants, and real GMV.
Rewards Bunny is a Singapore-built rewards platform spanning cashback, merchant discovery, affiliate attribution, reward rules, payout workflows, and emerging AI-assisted commerce.
The problem
Rewards are simple at the surface. Underneath, the hard part is connecting users, merchants, incentives, attribution, and payout operations into one reliable product experience.
Connect supply without manual chaos.
The platform needed to handle a broad merchant and brand network while keeping the user journey simple.
Turn cashback into a dependable system.
Affiliate attribution, reward logic, customer support, and payouts create operational complexity behind each transaction.
Product system
The platform evolved into a set of connected product and operating layers rather than a single cashback page.
User journeyDiscovery, clicks, rewards intent
Merchant layerBrands, offers, partner supply
AttributionTracked commissions, validation
Reward logicRules, segmentation, incentives
Payout opsBalances, support, reliability
AI-ready accessAgent-readable workflows, APIs, MCP concepts
Founder scope
The strongest signal is not one narrow function. It is end-to-end ownership across product, technical, commercial, and operational decisions.
Owned the journey.
- Rewards discovery
- Incentive design
- User retention loops
Bridged implementation.
- APIs and integrations
- Databases and cloud infra
- Production troubleshooting
Built the network.
- Merchant ecosystem
- Affiliate relationships
- Partnership tradeoffs
Kept it working.
- Payout processes
- Support workflows
- Vendor coordination
“The useful skill is turning a messy business system into something users can trust and teams can operate.”
What this work demonstrates: product, systems, and operating judgment across a multi-sided platform.What this demonstrates
Capabilities evidenced through the product, operating, and technical work described above.
AI ProductExperience turning ambiguous commerce/rewards opportunities into product direction, user journeys, and agent-ready workflow concepts.
Technical ProductComfort with APIs, attribution, databases, infrastructure, operational reliability, and product-engineering tradeoffs.
Founder’s OfficeEvidence of broad ownership across strategy, product, partnerships, operations, vendors, and execution without hand-holding.
AI AutomationA natural bridge from real business workflows into structured prompts, agent-readable docs, MCP-style interfaces, and API-based automation.