Backed by leading firms such as Pantera Capital, YZi Lab, and OKX Ventures, aPriori is redefining the foundation of decentralized trading. The core team includes veterans from Jump, Coinbase, Citadel Securities, and dYdX. They combine on-chain technology expertise with practical Wall Street high-frequency trading experience. aPriori is building next-generation trading execution systems on high-performance public blockchains, bringing DeFi a truly competitive trading infrastructure.

aPriori is overhauling the on-chain trading workflow: leveraging an AI-powered DEX aggregator and an MEV-based liquidity staking module, aPriori integrates the entire cycle—from order placement to matching and profit realization—into a unified, sustainable product suite.

After rolling out the AI-driven DEX aggregator Swapr last week, the aPriori team is now focusing on the “smart brain” of on-chain trading: its Order Flow Segmentation system. This innovative system merges behavioral tagging, wallet clustering, AI analysis, and on-chain feedback mechanisms to enable smarter, fairer transaction processing. The system protects against “toxic flow” like arbitrage slippage while channeling liquidity to the most effective destinations. The result is not only smarter trades, but also a more orderly and trustworthy on-chain market environment.

“Understanding each trade is the starting point for fair execution.”

Order flow identification is central to aPriori’s technology stack. By analyzing users’ trading actions, wallet histories, and market reactions, the system can determine—before a transaction executes—whether it stems from normal user behavior or from “toxic flow” such as arbitrage or sandwich attacks. Unlike conventional approaches that only check if transactions clear, this method filters risks earlier, offering LPs more secure counterparties and improving both route selection and execution fairness.

“Tech + Ecosystem: The Perfect Moment for Monad”

Different blockchains have unique data profiles: Solana delivers fast trades and an active user base, but widespread closed-source contracts restrict usable training data. In contrast, Ethereum and other EVM chains offer open data, but their performance ceilings lead to more conservative trading activity and lower data density overall.

Monad strikes a rare balance between speed and transparency—it delivers Solana-grade throughput and aggressive trading, while preserving the readability and openness of EVM architecture. This creates the ideal environment for aPriori to develop next-generation order flow recognition models.

“User data is more than participation—it's fueling the next wave of trading intelligence.”

Community Data Contribution Program: To help train smarter AI for transaction recognition, aPriori has launched a community data contribution initiative. Participants can help the model better “see” the on-chain landscape by taking these simple actions:

• Link your wallets: Connect your frequently used wallet addresses to provide a more complete behavioral picture.
• Supported chains: Ethereum, BNB Chain, Monad testnet.
• Sync social accounts: Optionally connect Twitter, Discord, and others to provide further identity signals.
• Check-in & task tracking: A dedicated dashboard tracks each user’s check-ins, trading activity, and contribution progress.

This data allows the system to associate addresses with individual users, uncover potential collaborations, and enhance the AI’s ability to identify transaction types and risks.

“How do you tell if a transaction includes toxic flow?”

At the heart of Swapr’s engine, every transaction undergoes an AI-powered risk assessment before confirmation, based on:

• Trade characteristics: buy/sell direction, token route, gas, fees, slippage, etc.
• Address history: trading frequency, past behaviors, asset changes.
• Market reactions: price movements from 1 second to 24 hours post-trade.
• Profit analysis: profitability across different periods, and the trade’s potential impact on LPs.

The model determines if a trade constitutes a “toxic flow”—such as arbitrage or sandwich attacks exploiting information advantages—and evaluates its threat to market fairness.

“It’s not about making the model more complex, but about making it truly understand trading.”

From rule-based engines to neural networks, aPriori does not rely on a single algorithm. Instead, it seamlessly integrates traditional models (XGBoost, LightGBM) with sequence models (RNN, Transformer). XGBoost and LightGBM efficiently interpret structured data, while RNN and Transformer excel at identifying behavioral patterns over time.

Swapr leverages an ensemble architecture—multiple sub-models learn from different data dimensions and timeframes, then combine their scores for more precise handling of sophisticated trading strategies.

“Who’s behind the scenes colluding on arbitrage?”

Clusters of addresses typically collaborate in arbitrage, rather than single-wallet operations. By identifying these “behavior groups,” the system can anticipate potential arbitrage rings and guard LPs against concentrated “toxic flow” impacts.

“Embedding AI in trade execution”

As training data grows, Swapr’s recognition system sets a new standard for DeFi routing. It delivers best-in-class quotes. It also adapts liquidity flows in real time to protect the interests of both users and LPs.

Founder Ray emphasizes: “A true DeFi execution engine understands, makes judgments, and knows how to protect the system. We want Swapr to be the first trading gateway that can really ‘think’.”

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