Aevia
Aevia is the first AI agent to secure your digital legacy.
Created on 15th February 2025
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Aevia
Aevia is the first AI agent to secure your digital legacy.
The problem Aevia solves
As digital assets like cryptocurrencies, tokens, and NFTs become increasingly valuable, a critical challenge emerges:
How can individuals ensure that their digital wealth is securely passed on to their loved ones after their death?
Unlike traditional financial systems, digital assets are often held in private wallets protected by passwords and seed phrases.
If the owner passes away without sharing access details, those assets can be permanently lost, leaving heirs with no way to recover the inheritance.
Aevea addresses this problem by automating the transfer of digital assets upon the owner's death, while also offering options to invest those assets and enable heirs to withdraw them in fiat if needed. This ensures that digital wealth is preserved and easily accessible to beneficiaries, even if they lack technical expertise.
Challenges I ran into
ERC-712 Standard Implementation
Hurdle: Initially, we aimed to keep user funds in their personal wallet while enabling inheritance transfers upon their passing. The challenge was finding a secure and automated way to execute this transfer without manual intervention.
Solution: We adopted the ERC-712 standard, which allows users to sign approval messages. This enabled the agent to perform delegated transactions using the user's pre-signed parameters, ensuring secure and automated asset transfers.
StakeKit Integration
Hurdle: We wanted to utilize StakeKit's investment protocols, but keeping funds in users' wallets was incompatible with their platform. StakeKit required funds to be moved for investment purposes.
Solution: We introduced agent controlled custodial wallets. This allowed the agent to actively manage and invest user funds through StakeKit, ensuring both inheritance functionality and investment growth.
JavaScript to Python Migration
Bug: StakeKit's documentation and examples were primarily in JavaScript, while our backend was in Python. This led to data processing inconsistencies and integration errors.
Solution: We meticulously debugged message handling by comparing the expected JavaScript output with our Python implementation. We validated each step to ensure accurate data processing, successfully bridging the gap between the two languages.
Tracks Applied (2)