The problem Aurelic solves

The Problem Aurelic Solves

Leverage in DeFi is capital-inefficient, risky, and difficult to manage for most
users.

Existing leverage protocols typically require:

• Full or overcollateralized upfront capital
• Continuous monitoring to avoid liquidation
• Exposure to sudden liquidation cascades
• Reliance on external liquidators and MEV-driven execution

Aurelic introduces a prefunding-based leverage model that changes how risk is
handled.

Instead of reacting to risk through liquidations, Aurelic defines and funds risk
upfront, making leverage more predictable and safer by design.

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What Can People Use Aurelic For?

Aurelic enables users to access leverage with lower upfront capital and clearer
risk boundaries.

• Opening leveraged positions using only a fraction of upfront collateral
• Avoiding liquidation cascades through prefunded downside coverage
• Using leverage without constant position monitoring
• Accessing leverage with deterministic, protocol-enforced outcomes
• Participating in leverage strategies with bounded and predefined risk
• Building structured or automated strategies on top of prefunded positions

Aurelic shifts leverage from reactive liquidation to predefined settlement,
improving safety, UX, and capital efficiency.

Challenges we ran into

Challenges We Ran Into

During the hackathon, most challenges came from implementing a prefunded leverage
model that had to work end-to-end on-chain: from position creation, DEX execution,
to deterministic settlement — all within tight safety constraints.

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Implementing Prefunding Without Incentive Misalignment

Challenge
Prefunding introduces a coordination problem between three actors:
users, prefunders, and the protocol. Early implementations risked allowing
prefunders to take excessive exposure without clearly bounded downside.

How We Addressed It

• Enforced strict exposure caps per prefunded position
• Locked all risk parameters (leverage, coverage ratio, payout curve) at
  position creation
• Implemented payoff curves directly in smart contracts
• Ensured maximum loss is always computable upfront

This made risk explicit and prevented hidden incentive leakage.

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Integrating DEX Execution Into Prefunded Positions

Challenge
Opening and closing leveraged positions requires DEX swaps, where execution price
directly affects risk and settlement. Incorrect handling could break invariants
or shift losses beyond prefunded limits.

How We Addressed It

• Routed all swaps through a dedicated execution module
• Used exact-input swaps with strict slippage bounds
• Validated post-swap balances before finalizing position state
• Reverted the entire transaction if execution violated risk constraints

DEX execution became deterministic and bounded by design.

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Replacing Liquidations With Deterministic On-Chain Settlement

Challenge
Without liquidators, the protocol must resolve positions purely through on-chain
logic, including edge cases such as early exit or boundary outcomes.

How We Addressed It

• Implemented fully on-chain settlement functions
• Removed dependency on keepers or off-chain actors
• Computed outcomes solely from on-chain state and execution results
• Explicitly handled boundary conditions and early close paths

This eliminated liquidation races and MEV-driven execution.

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Handling Complex Accounting Across Multiple Value Sources

Challenge
Each position combines user collateral, prefunded coverage, DEX execution output,
and protocol fees. Partial failures or incorrect ordering could break accounting
invariants.

How We Addressed It

• Applied strict checks-effects-interactions across all flows
• Finalized internal accounting before external calls
• Ensured all position updates are atomic (all succeed or revert)
• Used invariant checks instead of balance assumptions

This ensured payouts and losses remained predictable and auditable.

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Balancing Capital Efficiency With Safety Under Hackathon Constraints

Challenge
Improving capital efficiency while maintaining safety required careful trade-offs,
especially with limited time and no oracle dependency.

How We Addressed It

• Started with conservative leverage and coverage parameters
• Avoided price oracles; relied on execution-time constraints
• Modeled worst-case loss scenarios explicitly
• Designed the system to fail conservatively under stress

Aurelic prioritizes correctness and survivability over aggressive optimization.