Why On-Chain Simulations Are the Ultimate Truth Machine

Standard JSON-RPC endpoints like eth_call are opaque and non-deterministic. They query a single node's state, which can be stale or forked. This makes them useless for high-stakes DeFi operations where finality matters.

• State Lag: Relies on a node's mempool and sync status, introducing 100ms-2s+ of uncertainty.
• No Guarantees: Cannot simulate complex, multi-contract interactions with atomic certainty.
• MEV Leakage: Your intent is broadcast to the public mempool before execution.

Services like The Graph or Covalent provide fast, indexed historical data, but it's fundamentally post-hoc. They tell you what did happen, not what will happen. For any forward-looking operation—like a swap, loan, or bridge—this is a fatal flaw.

• Historical Only: Data is minutes to hours old, useless for real-time pricing or liquidity checks.
• Reorg Vulnerable: Indexers can be wrong on chain reorganizations, leading to incorrect balances.
• Composability Blindspot: Cannot model the side-effects of your transaction on other protocols.

Tenderly pioneered the use of full-state simulation for debugging, exposing the power of a local, forked EVM. This proved that deterministic, pre-execution analysis was possible. The next step is moving this capability into production for transaction building and validation.

• Deterministic Output: Same inputs + same state = guaranteed same result.
• Full Context: Simulates the entire transaction lifecycle, including all internal calls and events.
• Privacy: Simulations run locally or in a trusted environment, hiding intent from searchers.

UniswapX, CoW Swap, and Across Protocol don't just broadcast transactions. They use off-chain solvers who compete by simulating execution paths to find the best outcome. This is only possible with high-fidelity simulation infra. The old guard cannot support this architecture.

• Path Discovery: Solvers simulate thousands of routes across DEXs (Uniswap, Curve, Balancer) to find optimal fills.
• Cost Certainty: Users get a guaranteed output quote before signing, eliminating slippage and frontrunning.
• Solver Competition: Creates a ~$200M+ annual market for execution quality, funded by MEV capture.

Traditional financial models fail to capture the unique, high-velocity failure modes of DeFi. They miss liquidity fragmentation across DEXs, oracle lag, and MEV-driven arbitrage cascades that collapse pegs.

• Simulates real-time on-chain state from Ethereum, Arbitrum, Solana.
• Models attacker behavior with profit-maximizing agents.
• Exposes hidden dependencies on protocols like Curve, Uniswap, and Aave.

Stress tests must simulate the interconnected collapse of correlated assets (e.g., FRAX, DAI, USDC) across multiple blockchains to model real black swan events.

• Replays historical crashes like Terra/Luna with modern protocol stacks.
• Stress-tests cross-chain bridges (LayerZero, Wormhole) for liquidity drains.
• Quantifies the domino effect on lending markets and LP positions.

Simulations provide the data to harden protocol economics, moving from guesswork to quantified resilience. This is the foundation for truly robust algorithmic money.

• Optimizes collateral ratios, mint/burn fees, and incentive schedules.
• Validates the efficacy of on-chain circuit breakers and governance responses.
• Generates a risk score for protocol architects and underwriters like Nexus Mutual.

Users blindly sign transactions, creating a $1B+ annual market for validators and searchers to exploit. This is a direct wealth transfer from retail to sophisticated actors.\n- Front-running and sandwich attacks siphon ~5-10% of swap value\n- Failed transactions still cost gas, a $100M+ annual burn\n- Creates systemic risk and degrades UX for all non-professionals

Shift from specifying how to specifying what. Users submit desired outcomes (intents), and a competitive solver network simulates execution paths to fulfill them optimally.\n- Guarantees like "price ≥ X" are enforced pre-execution\n- Competition among solvers drives execution quality to theoretical limits\n- Gasless experience for users; solvers bundle and pay fees

Bridging assets is a leap of faith. Users have no visibility into execution risk, slippage, or finality across chains like Ethereum, Arbitrum, Solana. This opaque process has led to $2B+ in bridge hacks.\n- Slippage tolerance is a blunt, often ineffective instrument\n- No preview of complex multi-step cross-chain actions (e.g., bridge+swap+stake)\n- Liquidity fragmentation creates unpredictable costs

Infrastructure that pre-executes any transaction in a sandboxed environment across any EVM or SVM chain, returning a risk-scored preview. This is the firewall for web3.\n- Detects scams, phishing, and unexpected approvals in real-time\n- Simulates exact token balances, price impact, and gas costs\n- Enables complex cross-chain bundles via protocols like Across and LayerZero

Multi-signature wallets and DAOs vote on proposals with multi-million dollar implications based on static descriptions, not live execution data. This leads to catastrophic governance failures.\n- Unforeseen side-effects and contract interactions can drain treasuries\n- No A/B testing for parameter changes (e.g., adjusting a Compound interest rate model)\n- Voting becomes a social signal, not a technical verification

Create a perfect, disposable replica of mainnet state to simulate governance proposals. This turns voting into a verification of simulated outcomes, not a popularity contest.\n- Test treasury payouts, tokenomics changes, and upgrades in isolation\n- Generate precise before/after state diffs for every wallet\n- Integrate directly into Snapshot and other voting platforms