# LSD AI Engine

#### Purpose

The LSD AI Engine is responsible for analyzing staking protocols and recommending SOL allocation to achieve optimal risk-adjusted returns, while strictly respecting strategy constraints defined by users and governance.<br>

The AI does not execute transactions.

All execution and custody are handled by on-chain smart contracts.

The system is designed to:

* Optimize sustainable yield rather than short-term spikes
* Control downside risk through strict allocation limits
* Adapt to changing protocol conditions on an epoch basis
* Support transparent, explainable allocation decisions

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#### Strategy Constraints (Important)

AI recommendations are always constrained by the active pool-wide strategy, which is determined through quadratic aggregation of user preferences:

* Safe Strategy

  → 100% allocation to large, proven protocols (TVL > $1B)
* Balanced Strategy

  → Up to 30% allocation to higher-yield, moderate-risk protocols
* Max Returns Strategy

  → Up to 50% allocation to higher-yield, higher-risk protocols

The AI cannot exceed these limits, regardless of yield potential.

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#### Data Inputs

The AI engine evaluates protocols using on-chain and market data, primarily sourced from DefiLlama and other public sources, including:

* Total Value Locked (TVL) and TVL stability
* Protocol revenue and fee generation
* Base staking APY
* Incentive programs (tokens, points, or additional rewards)
* Historical yield performance
* Liquidity and withdrawal constraints
* Protocol maturity and operational track record
* On-chain activity and usage trends

All data inputs are normalized to allow fair comparison across protocols.

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#### Risk & Reward Analysis

Rather than optimizing for headline APY, LSD prioritizes risk-adjusted returns.<br>

Each protocol is evaluated across two core dimensions:

**Reward Factors**

* Base staking yield
* Additional incentive value
* Yield sustainability
* Potential upside from incentive or token programs

**Risk Factors**

* TVL size and concentration risk
* Protocol maturity and history
* Revenue consistency
* Liquidity and exit latency
* Smart contract complexity and integration risk<br>

These inputs are combined into internal risk and reward scores, which are used to rank protocols.

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#### Allocation Recommendation Logic

Based on the active strategy and scoring outputs, the AI:

* Ranks protocols by risk-adjusted return
* Recommends allocation percentages across multiple protocols
* Avoids single-protocol concentration
* Favors diversification within allowed risk bounds

The output is an allocation recommendation, not an instruction.

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#### Rebalancing Cycle

* AI analysis and allocation updates occur once per Solana epoch (\~2–3 days)
* Strategy preference is recalculated at each epoch
* Allocation changes are applied only at epoch boundaries

This prevents excessive churn and ensures predictable behavior.

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#### Performance Feedback Loop

After each epoch:

* Realized returns are compared against AI projections
* Deviations are analyzed
* Persistently underperforming protocols are deprioritized
* Risk and reward weights are adjusted conservatively over time

This creates a controlled feedback loop without autonomous execution.

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#### Summary

DAO proposes protocols → AI analyzes risk & reward → strategy defines limits → AI recommends allocation → smart contracts execute → results feed back into analysis.

The LSD AI Engine acts as a decision-support layer, not a black box or autonomous trader.