Last updated 3 months ago
Cardano suffers from low liquidity and complex LP management. The lack of simple tools for users limits DeFi participation and reduces overall on-chain volume.
An AI-driven yield and liquidity optimization engine for Cardano, combining a RAG and GAN model to continuously analyze, simulate and manage optimal LP and yield strategies.
Please provide your proposal title
AI-Driven Liquidity and Yield Optimization Layer For Cardano
Enter the amount of funding you are requesting in ADA
200000
Please specify how many months you expect your project to last
12
What is the problem you want to solve?
Cardano suffers from low liquidity and complex LP management. The lack of simple tools for users limits DeFi participation and reduces overall on-chain volume.
Supporting links
Does your project have any dependencies on other organizations, technical or otherwise?
No
Describe any dependencies or write 'No dependencies'
No Dependencies
Will your project's outputs be fully open source?
Yes
Please provide details on the intellectual property (IP) status of your project outputs, including whether they will be released as open source or retained under another licence.
Yes. All non-sensitive components of this project will be released as open-source. This includes:
Smart contracts AI integration interfaces Strategy logic framework Frontend dashboard SDK and API tools
Please choose the most relevant theme and tag related to the outcomes of your proposal
DeFi
Describe what makes your idea innovative compared to what has been previously launched in the market (whether by you or others).
Currently, there are no existing Yield Aggregators on the Cardano blockchain. This is a big barrier to growing liquidity on-chain. DeFi contains many barriers to entry for all users such as impermanence risk, yield management & complex systems. By automating these processes, we allow users to easily provide liquidity to Cardano.
To do this we introduce a multi-layer AI architecture consisting of:
A Retrieval-Augmented Generation (RAG) model trained specifically on Cardano’s liquidity mechanisms, pool structures, impermanent loss dynamics, and DEX behavior.
A Generative Adversarial Network (GAN) that stress-tests thousands of liquidity and trading strategies against historical, live, and synthetic market conditions.
A self-improving optimization loop that continuously refines strategy selection over time.
No other Cardano-native tool combines AI to allow users automated LP management into a continuously learning liquidity strategy. This transforms liquidity management from a static, manual process into a reactive, adaptive, AI-driven system.
Describe what your prototype or MVP will demonstrate, and where it can be accessed.
The MVP will demonstrate
Describe realistic measures of success, ideally with on-chain metrics.
Please describe your proposed solution and how it addresses the problem
This system delivers a fully automated, AI-guided yield and liquidity aggregation platform for Cardano. It consists of five core components:
Uses Cardano-specific data, DEX behavior, liquidity events, AMM mechanics, historical volatility, and price to produce context-aware strategies that can adapt and react by taking in data from real-time conditions.
Generates synthetic market environments, tests liquidity strategies across thousands of scenarios and identifies robust, high-performance strategies.
Optimizes outputs from both AI layers, and determines capital allocation, pool selection, exposure weights, and rebalance intervals.
We want a front-end that requires no technical knowledge. The user simply selects a risk profile (low, medium, high, stable), and everything else is handled by the AI.
The system learns and adapts continuously, improving performance over time, and allowing users to engage with automated liquidity mining that adapts based on current market conditions.
Please define the positive impact your project will have on the wider Cardano community
Through the implementation of a Yield Aggregator on Cardano, we aim too:
Increase liquidity efficiency on Cardano
Attract new participants to Cardano DeFi
Improve LP profitability and risk management
Strengthen Cardano’s DeFi competitiveness by offering solutions that exist on other blockchains
Increase DEX trading activity by expanding liquidity.
We also aim to establish Cardano as a leader in AI-powered DeFi, implementing one of the most advanced AI driven liquidity management systems in all of blockchain!
What is your capability to deliver your project with high levels of trust and accountability? How do you intend to validate if your approach is feasible?
Having been consistent builders in the Space for nearly 5 years. We have conducted extensive research in AMM mathematics and LP behavior, and have a strong technical network of AI and Cardano development specialists.
The best way to gauge the feasibility of a yield aggregation tool is to look at existing Auto-Harvesters on other blockchains. As an example, let's look at Yearn. The current TVL (as of 26/11) is $441,970,000. This makes it the 15th most staked protocol on Ethereum alone, and shows that there is a clear value to the service for users to engage. The largest yield aggregator, DeSyn Liquid Strategy, has a TVL (as of 26/11) of $1.2 billion.
Beefyswap, another yield aggregation tool, currently holds a TVL (as of 26/11) of $220,100,000 USD. Each of these systems, individually, hold more liquidity than the entirety of the Cardano DeFi ecosystem. From looking at other chains, it's clear that there is a strong market for Automated Liquidity Management.
Milestone Title
AI Model Development (RAG + GAN)
Milestone Outputs
A full multi-layer AI system consisting of a Cardano-specific RAG model trained on AMM mechanics, liquidity behaviour and market data, combined with a GAN capable of generating and stress-testing synthetic market scenarios to optimise liquidity and yield strategies over time.
Acceptance Criteria
Both the RAG and GAN models must successfully ingest Cardano DEX data and generate actionable strategy signals. The system must demonstrate the ability to simulate thousands of scenarios and output optimized, risk-aware liquidity strategies with measurable performance improvements.
Evidence of Completion
Public GitHub repository containing model architecture, training logs, strategy output samples, experiment results, and demonstration notebooks showing successful performance.
Delivery Month
10
Cost
70000
Progress
30 %
Milestone Title
Smart Contracts & Infrastructure
Milestone Outputs
A set of production-ready smart contracts enabling trustless user deposits into AI-managed liquidity strategies, including vaults, allocation logic, automated rebalancing triggers and integrations with Cardano DEXs such as VyFi and others.
Acceptance Criteria
All contracts must compile, deploy and function correctly on Cardano testnet and mainnet. They must support secure deposits, withdrawals, automated rebalancing and accurate accounting, and demonstrate resistance to common smart contract vulnerabilities.
Evidence of Completion
Transaction hashes of deployed contracts on testnet/mainnet, open-source contract code, and documentation showing correct execution of deposits, rebalances and withdrawals.
Delivery Month
8
Cost
50000
Progress
20 %
Milestone Title
Frontend & UX
Milestone Outputs
A web-based dashboard that allows users to connect a Cardano wallet, select AI-managed strategies based on risk level, deposit assets, and view real-time performance metrics including ROI, impermanent loss, fees, and rebalancing activity.
Acceptance Criteria
Users must be able to easily connect their wallet, choose a strategy, deposit funds, and monitor performance without technical knowledge. The dashboard must be responsive, secure, and display accurate real-time and historical data.
Evidence of Completion
Live web application link, UI screenshots, demo videos, and user testing feedback confirming successful wallet connections and strategy interactions.
Delivery Month
9
Cost
20000
Progress
10 %
Milestone Title
Data Pipelines & Oracles
Milestone Outputs
High-quality data pipelines that ingest historical and real-time Cardano DEX data, liquidity events, token metrics, and price feeds, combined with oracle integrations to supply accurate market data to the AI system and smart contracts.
Acceptance Criteria
The pipelines must reliably stream clean, structured data into the AI engine and smart contracts with minimal latency. Historical datasets must be complete and usable for backtesting, strategy evaluation and model training.
Evidence of Completion
Documented pipeline architecture, sample datasets, live data feeds, and logs demonstrating continuous and accurate data ingestion into the system.
Delivery Month
6
Cost
15000
Progress
10 %
Milestone Title
Security & External Audits
Milestone Outputs
A comprehensive external and internal audit process covering smart contracts, AI-triggered automation, data handling, and system architecture to ensure robustness against exploits, manipulation, data poisoning and protocol-level vulnerabilities.
Acceptance Criteria
All critical and high-severity issues must be identified and patched. Contracts and pipelines must pass internal fuzz testing, edge-case simulation, and an independent third-party security review prior to mainnet release.
Evidence of Completion
Published third-party audit report, remediation log, and signed verification statement confirming that all vulnerabilities have been addressed.
Delivery Month
10
Cost
30000
Progress
10 %
Milestone Title
Research & Strategy Modelling - Documentation & Community
Milestone Outputs
Advanced AMM and LP modeling research, including mathematical frameworks and optimisation strategies tailored to Cardano’s UTXO model. This includes simulation models for impermanent loss, fee capture, volatility exposure and risk-adjusted returns.
Comprehensive documentation including user guides, developer guides, API references, tutorial videos, and onboarding materials explaining how to use, integrate, and build on top of the AI yield aggregation platform.
Acceptance Criteria
Research outputs must produce quantifiable improvements in LP strategy design. Models should be validated against historical data and integrated into the AI system to inform smarter allocation, positioning and rebalancing decisions.
All documentation must be structured, accessible, and easy for non-technical users and developers to follow. It must include visual guides, step-by-step instructions, working code examples, and live support references.
Evidence of Completion
Published research documents, simulation results, mathematical models, charts, and integration references within the AI system’s decision logic.
Public documentation portal, tutorial videos, GitHub wiki, and downloadable guides linked directly from the project website and repositories.
Delivery Month
12
Cost
25000
Progress
10 %
Please provide a cost breakdown of the proposed work and resources
**AI Model Development (RAG + GAN) **– 70,000 ADA
Timeframe: Months 1–10
Smart Contracts & Infrastructure – 50,000 ADA
**Timeframe: **Months 4–8
Frontend & UX (Dashboard) – 20,000 ADA
Timeframe: Months 6–9
Data Pipelines & Oracles – 15,000 ADA
**Timeframe: **Months 2–6
Security & External Audits – 30,000 ADA
**Timeframe: **Months 8–10
**Research & Strategy Modelling **– 15,000 ADA
Timeframe: Months 1–7
Documentation & Community – 10,000 ADA
Timeframe: Months 9–12
How does the cost of the project represent value for the Cardano ecosystem?
This project will deliver open-source DeFi infrastructure, and automated liquidity for DeFi users on Cardano. Given observation from similar projects being developed on other blockchains, we can expect a strong economic impact to be delivered to the Cardano Blockchain through increased TVL and growth in network utility.
Yield aggregation is a tool that is currently lacking on our blockchain, reducing the competitiveness of Cardano against other chains when it comes to holding liquidity. If we plan on implementing the stable coins, this degree of infrastructure will be needed to onboard users from other blockchains to ours.
For the level of innovation and infrastructure delivered, this represents exceptional value for the Cardano ecosystem.
I confirm that evidence of prior research, whitepaper, design, or proof-of-concept is provided.
Yes
I confirm that the proposal includes ecosystem research and uses the findings to either (a) justify its uniqueness over existing solutions or (b) demonstrate the value of its novel approach.
Yes
I confirm that the proposal demonstrates technical capability via verifiable in-house talent or a confirmed development partner (GitHub, LinkedIn, portfolio, etc.)
Yes
I confirm that the proposer and all team members are in good standing with prior Catalyst projects.
Yes
I confirm that the proposal clearly defines the problem and the value of the on-chain utility.
Yes
I confirm that the primary goal of the proposal is a working prototype deployed on at least a Cardano testnet.
Yes
I confirm that the proposal outlines a credible and clear technical plan and architecture.
Yes
I confirm that the budget and timeline (≤ 12 months) are realistic for the proposed work.
Yes
I confirm that the proposal includes a community engagement and feedback plan to amplify prototype adoption with the Cardano ecosystem.
Yes
I confirm that the budget is for future development only; excludes retroactive funding, incentives, giveaways, re-granting, or sub-treasuries.
Yes
I Agree
Yes
Project Lead / DeFi Architect: Steven Ward
AI Engineer and Data Scientist: John Ward
Lead Blockchain Engineer: Skylar O'Quinn
Lead Frontend Engineer: Ryan Feltkamp
Technical Writer & Community: Dylan Mccoy