Your ENS identity as a universal loyalty ID. Automatic cashback on every purchase.
Cashback ID transforms loyalty programs by eliminating fragmented cards, apps, and expiring points.
Users simply provide their ENS name (e.g., lucia.eth) at any participating merchant. This name functions as their universal loyalty ID across all merchants.
Brands deposit loyalty value into cross-chain liquidity using LI.FI’s bridge and DEX aggregation rails. When users make a purchase, an automated flow converts accumulated brand points into stablecoins (USDC/USDT) and settles them directly into the user’s wallet in real time.
Loyalty coupons and NFTs are managed as dynamic Sui Objects that evolve based on user spending levels (bronze, silver, gold, platinum) with near-zero transaction costs and real-time updates.
Zero friction: no more apps, no expired points, just real instant value linked to your digital identity.
The project integrates three core technology components:
ENS (Ethereum Name Service): Serves as the base identity layer. Each user links their ENS name to a wallet storing their loyalty profile. Merchants simply request the ENS name at point-of-sale and can verify on-chain purchase history and user tier without intermediaries.
LI.FI cross-chain liquidity layer: Instead of a single DEX like Uniswap, we integrate LI.FI as a bridge and DEX aggregation protocol to handle swaps and bridging of loyalty value. When a user accumulates loyalty points (represented as ERC-20 brand tokens), our backend calls LI.FI’s SDK/API to route an optimal swap into stablecoins (USDC/USDT) on the target chain and settle them to the user’s wallet. Brands effectively fund the underlying liquidity by seeding the source assets that LI.FI then routes through aggregated bridges/DEXs.
Sui Network: Loyalty NFTs and coupons are implemented as dynamic Objects that mutate based on user actions. We use Sui Move to create gamification logic (tiers: bronze, silver, gold, platinum) based on cumulative spending. Sui transactions cost fractions of a cent, enabling frequent updates without harming system economics.
Key technical challenges solved:

