AutoVault AI — Fund Manager On-Chain You sleep, your money doesn’t

:rocket: Project Name

AutoVault AI — Fund Manager On-Chain
You sleep, your money doesn’t.


:puzzle_piece: Problem Statement

DeFi users often miss out on optimal yields because navigating cross-chain vaults, assessing risk, and managing rebalance manually is too complex.
This complexity leads to low capital efficiency and high opportunity cost — especially for non-technical users.


:light_bulb: Solution Overview

AutoVault AI is a fully automated, AI-native fund manager for DeFi vaults. It scans 50+ DEXs and chains, scores the best vaults based on yield, risk, gas, and slippage, and zaps users in with just one click.
Users get a real-time simulator, AI reasoning terminal, and auto-compound tracking — turning DeFi into a passive, intelligent experience.
Its secret sauce: AI scoring logic + auto-routing + reward simulation in one seamless loop.


:blue_book: Project Description

AutoVault AI is designed to make high-yield DeFi accessible, safe, and intelligent.
Powered by an AI-driven vault scoring engine (based on our YEC formula), the system evaluates pools across Arbitrum, Optimism, Ethereum, Metis, and more. Users interact through a simple prompt (e.g. “Find me a vault with APR ≥14% and low IL”), and AutoVault AI handles everything else.

Key features include:

  • AI Reasoning Terminal: Live decision logs with vault score, IL, gas, and route
  • Simulator: ROI projections over 1d–365d with full breakdown
  • Zap Engine: Cross-chain swap + deposit in one click
  • Vault Dashboard: Timeline of rebalances, compound events, health score
  • How It Works Hub: Transparent engine logic (25 modules)

We’re excited because this brings institutional-grade yield management to everyday DeFi users — with full explainability and no spreadsheets. It’s DeFi made frictionless and intelligent.


:video_game: Community Engagement Features

:white_check_mark: Testable Features & Tasks:

  • :magnifying_glass_tilted_left: Submit AI prompt (vault preferences)
  • :brain: View AI reasoning terminal logs
  • :money_with_wings: Simulate ROI for different vaults
  • :high_voltage: Execute “Zap” to a vault
  • :chart_increasing: View dashboard and milestones
  • :outbox_tray: Try withdraw (partial or full)
  • :books: View “How It Works” and match 3 vaults to their engine

:coin: Points System:

Task Points
Submit prompt & view AI log 10
Run 1 simulation 15
Execute 1 zap 20
View dashboard & milestones 10
Try withdraw or add funds 15
Complete all steps +25 bonus

:bullseye: Gamified Experience:

  • Progress bar + Vault NFT badge for those who complete all tasks
  • Leaderboard for “Smartest Yield Hunter of the Week”
  • Users feel like they’re training their own DeFi AI

This system makes onboarding not only easy, but fun, with instant rewards and clear actions that also validate the product UX.


:handshake: Getting Involved

  • :speech_balloon: Join our Discord (coming soon) to give feedback or suggest vault integrations
  • :man_technologist: Developers can contribute to strategy modules & vault adapters on GitHub
  • :loudspeaker: Content creators & yield influencers can embed the simulator and terminal in their sites
  • :writing_hand: Users can submit vault requests via AI prompt and see their idea live!
9 Likes

Hello @x0_fikridev

  1. What is the biggest bottleneck for AI reasoning when scanning 50+ chains/DEXs simultaneously , latency, data quality, or gas estimate variance?
  2. Is the simulator connected to live APR/APY data or using estimations from third-party APIs? How do you prevent simulator-based yield farming from being gamed?
7 Likes

:magnifying_glass_tilted_right: Q1: How does AutoVault AI handle latency, data quality, and gas variance when scanning 50+ chains and DEXs simultaneously?
Answer:

Thanks for raising a core technical challenge in building AI-native real-time DeFi agents — this touches on both performance and trustworthiness.

In our current engine stack, we’ve already implemented:

:white_check_mark: MultiChainScanEngine, which parallelizes RPC calls across L1/L2 chains, with fallback support and timeout handling.
This ensures that latency spikes on less-optimized chains (e.g. Metis, Base) don’t block the entire reasoning pipeline.

:white_check_mark: VaultConfidenceScorer, which scores each vault’s data freshness.
Vaults with stale APR or TVL data are penalized or excluded to protect accuracy.

:white_check_mark: GasEstimatorEngine, which retrieves real-time gas estimates across chains and integrates LI.FI oracles to ensure zap feasibility.

And based on these foundations, we’re extending the architecture with:

:satellite_antenna: LiveRouteOptimizer, simulating bridge + swap paths across chains with gas/delay prediction

:repeat_button: ScanScheduler, which throttles scan intensity based on vault update frequency (e.g., daily vs hourly auto-compound)

This lets AutoVault AI maintain a real-time reasoning loop even across volatile multi-chain environments — balancing speed with trust.

:bar_chart: Q2: Is the yield simulator based on live APR/APY or third-party estimations?
Answer:

Great question — simulator accuracy is critical for trust and defensibility.

We’ve implemented a hybrid APR data ingestion system:

:white_check_mark: YieldSourceRouter, which fetches APR/APY from live subgraphs (e.g. Uniswap, Aura), Chainlink, or DeFiLlama.
If unavailable, it falls back to cached 30m–60m snapshots.

:white_check_mark: APRNormalizer, which adjusts raw APR to reflect compounding frequency, vault fees, and risk weight.

:white_check_mark: ILCalculatorEngine, which integrates live token volatility to estimate impermanent loss.

Users see not just APR, but also:

Final Projected Return = Compound(Deposit, DailyAPR, IL, Fees, Duration)
We’re also building:

:chart_increasing: ROIChartModule (in-progress): interactive chart for 1w–365d returns with confidence intervals

This ensures simulations are grounded in real yield behavior, not wishful marketing math.

:brain: Q3: How do you prevent users from gaming the simulator using unrealistic inputs or spiky APRs?
Answer:

Simulator abuse is a real threat — and we designed 3 layers of protection:

:white_check_mark: APRVolatilityFilter, which flags vaults with >15% APR fluctuation in the last 72h as :chart_decreasing: Unstable.
These vaults receive lower YEC Score and visual warnings.

:white_check_mark: YECScoreEngine, which penalizes vaults with mismatched APR/TVL/Age balance — preventing manipulation via low-liquidity pools.

:white_check_mark: SimulationLockLayer, which fixes core inputs (APR, compounding, IL) from verified vault metadata.
Users cannot override formulas — just duration and deposit.

Planned:

:name_badge: BadgeAlertSystem: “AI Warning” badges for suspicious APRs

:video_game: SimulatorProofNFT (optional): to verify real simulator interactions

This way, AutoVault AI delivers realistic, defensible projections — not clickbait ROI.

3 Likes

Looks really interesting! Here are three questions:

  1. How does the AI scoring engine weigh risk versus yield when recommending vaults?

  2. Can users customize their preferences beyond simple APR and impermanent loss filters?

  3. Will the “Zap” feature support all chains equally, or are some networks prioritized?

2 Likes

This is incredibly well thought-out , really appreciate the depth of your answers.

Kudos to the team , definitely feels like you’re solving the right problems, not just shipping hype.

4 Likes

:chart_increasing: Q1: How does the AI scoring engine weigh risk versus yield when recommending vaults?

Answer:

Great question — this touches the core of our yield reasoning engine.

:white_check_mark: What we’ve built:

AutoVault AI uses a custom scoring model (YEC Score) to balance high yield and real risk:

YEC_SCORE = (APR ^ 1.2 × Age × Volume × HalalBonus) ÷ (IL + Gas + Volatility + Slippage)
  • :white_check_mark: APR is amplified for strong compounding potential.
  • :white_check_mark: Risk factors (IL, slippage, gas, volatility) are penalized.
  • :white_check_mark: Age & liquidity reward stable, time-tested vaults.
  • :white_check_mark: AI explains its choices via the Reasoning Terminal, and vaults show Risk Label, Score, and Confidence.

:hammer_and_wrench: What’s planned:

  • :wrench: We’re building a weight control UI so users can adjust “Risk Tolerance” (e.g., safe vs. aggressive).
  • :wrench: An internal Vault Behavior Memory system to blacklist previously failed vaults over time.

:control_knobs: Q2: Can users customize their preferences beyond simple APR and impermanent loss filters?

Answer:

:white_check_mark: What we’ve built:

Yes — we already support AI prompt-based customization.
Users can type requests like:

“Find stablecoin vaults with APR ≥ 12%, halal, low IL.”

This gets mapped to:

  • :white_check_mark: APRNormalizer
  • :white_check_mark: HalalScreeningEngine
  • :white_check_mark: ILCalculatorEngine
  • :white_check_mark: GasEstimatorEngine

The filters respond to natural language and are shown in the vault list + Reasoning Terminal.

:hammer_and_wrench: What’s planned:

  • :wrench: A visual slider system (e.g. Risk vs. Return, Speed vs. Cost)
  • :wrench: Preset strategies: “Long-Term Stable”, “Max Yield”, “Halal Only”, etc.
  • :wrench: AI memory to learn from your previous prompt behavior

:globe_with_meridians: Q3: Will the “Zap” feature support all chains equally, or are some networks prioritized?

Answer:

:white_check_mark: What we’ve built:

AutoVault AI supports all EVM-compatible chains with dynamic routing using LI.FI SDK.

  • :white_check_mark: Zap logic chooses cheapest + fastest route automatically.
  • :white_check_mark: Chains currently prioritized in routing:
    • Arbitrum, Optimism, Metis, Base, Polygon, Ethereum
  • :white_check_mark: Gas estimates and slippage per route are shown in the simulator and terminal.

:hammer_and_wrench: What’s planned:

  • :wrench: Dynamic ChainScore Engine: will prioritize vaults based on live chain condition (gas, bridge delay, vault trust)
  • :wrench: Selective chain opt-in/out in UI (e.g., “only use L2s” or “avoid zkSync”)
  • :wrench: Zap optimization memory to cache best paths over time
1 Like

This is an incredibly thoughtful breakdown it’s clear a lot of care went into balancing transparency, customization, and intelligent automation. The YEC Score formula is both elegant and practical, and I especially appreciate the emphasis on explainability through the Reasoning Terminal. Excited to see how the planned features like risk tolerance sliders and memory-based optimization will enhance the user experience even further. Great work!