From Social Data to AI Brain: How does Port3 Network build an AI network for the Web3 world?

1. Introduction

In the world of Web3, data is transforming from static information into dynamic assets. Users' social behavior data is becoming the most valuable yet underdeveloped "digital minerals" in the AI era. The enormous value contained in the social data generated every moment has yet to be fully tapped.

We see that the reality of Web3 is fragmented: on one hand, we have witnessed explosive growth in vertical protocols such as DeFi, NFTs, and GameFi, with users generating a significant amount of behavioral data both on-chain and off-chain; on the other hand, this data is scattered across isolated DApps, transaction records, and social platforms, lacking structured integration, making it difficult to build a unified profile that can be truly utilized.

At the same time, the rise of AI is rapidly reshaping the entire digital world. Projects such as OpenAI's ChatGPT, Anthropic's Claude, and Web3-based Agent projects like Autonolas, Morphpad, and Mind Network have all proposed the vision of "callable data + executable intent."

Against this backdrop, a question arises: if AI is the future, then who will build the data layer and decision-making foundation for Web3? There is a project that provides a rather ultimate answer: Port3 Network.

From the initial SoQuest task platform, to Rankit's social behavior scoring engine, and then to OpenBQL's cross-chain intent execution language, Port3 has built a "social data infrastructure" centered around user behavior and friendly to AI models. It not only integrates on-chain data with off-chain social behavior, but also standardizes and recognizes intents, transforming data into "action templates" that agents can understand, invoke, and execute.

In other words, Port3 is no longer a single task platform or tool, but has strategically occupied the position of "Web3 data brain" ahead of the narratives of data sovereignty, on-chain identity, social finance, and others being truly integrated.

This article will deeply analyze Port3's product matrix, technological moat, token mechanism, and growth logic, exploring how it establishes a closed loop of data flow for AI Agents in the fragmented Web3 world, and becomes the hidden infrastructure of the next trillion-dollar trend.

2. Project Introduction

What is 2.1 Port3?

Port3 Network is a Web3 social data infrastructure project led by Jump Crypto, aiming to build a cross-chain, programmable, and callable social data layer. By aggregating user behavior data from Web2 and Web3, and using an AI engine for standardized processing, Port3 has created a complete closed loop from data collection ( SoQuest ), structured scoring ( Rankit ), intelligent querying ( OpenBQL ) to Agent invocation ( Ailliance.ai ), becoming a key facility for the assetization of on-chain behavior in the AI era.

Project Overview 2.2

2.2.1 Financing Situation

February 2023: Completed a $3 million seed round financing, led by Jump Crypto, with other participants including SNZ, Block Infinity, Dragon Roark, ViaBTC, Cryptonite, Lapin Digital, Cogitent, and Momentum6.

August 2023: Secured a new round of financing worth millions of dollars, with investors including EMURGO, Adaverse Accelerator, and Gate Labs.

October 2023: Announced the investment from DWF Labs and received grant support from multiple institutions.

2.2.2 Team Situation

Max D.: Co-founder, with work experience at Apple; possesses rich experience in Web3 project incubation and ecosystem expansion.

Anthony Deng: Co-founder, formerly engaged in backend development at Tencent and Viabtc Technology Limited, with many years of experience in high-concurrency system design and distributed architecture.

3. The Vision of Port3: From "Task Platform" to "AI Social Data Infrastructure"

Although Port3's product matrix includes several submodules such as SoQuest, Rankit, OpenBQL, and on.meme, which may seem scattered, they can actually be summarized into a core main line: "Behavior as an Asset, with Port3 responsible for the closed loop of data flow from collection to transformation."

3.1 Port3 Core Infrastructure

3.1.1 Data Aggregation - SoQuest

SoQuest is the core data entry built by Port3 Network, a Web3 user behavior capture platform that integrates task distribution, behavior verification, community growth, and data collection. Essentially, it is a data generation system that uses tasks as a trigger mechanism and collects user social behaviors as its targets, bridging the behavioral pathways between on-chain interactions and Web2 social platforms.

SoQuest supports mainstream Web2 platforms such as Twitter, Telegram, and Discord, and is compatible with interactions on 19 chains including EVM, Solana, Aptos, and Sui, covering behaviors such as transactions, authorizations, and NFT minting, thus forming one of the most widely covered behavior collection systems in the Web3 field.

As of mid-2025, Port3 Network has collected dynamic data from over 6 million users and 7,000 projects, covering more than 10 million crypto users. This has generated a vast amount of user behavior records and chain social interaction events, creating a real, multidimensional, and high-frequency Web3 social behavior database.

To enhance the platform's scalability and data collection capabilities, SoQuest has launched the QaaS( Quest-as-a-Service) module, allowing project teams to embed the task system into their own dApp or Telegram Mini App. In 2025, the verification API will be further opened, enabling the completion of verification logic embedding without the need for pre-set templates, greatly improving the standardization and universality of the task system.

SoQuest is not just a task platform; it is the starting point of the Port3 full-chain behavior asset closed loop and also the original source of the behavioral semantic data required for AI inference.

3.1.2 Data Accumulation - AI Social Data Layer

The user behavior data captured by SoQuest is ultimately deposited into the core module of the Port3 Network - the AI Social Data Layer, which is a structured behavioral database specifically designed for AI applications. It is also the underlying facility for Port3 to achieve "behavioral assetization" and "information financialization (InfoFi)."

Unlike traditional on-chain data platforms that are designed with the goal of "querying," Port3's data layer focuses on: how to make data usable for AI models and support on-chain reasoning and interaction that can be executed automatically.

The AI Social Data Layer integrates tens of millions of on-chain interaction records and social task behavior data, continuously updated in real-time through application modules such as SoQuest and Rankit, creating a dynamically self-growing social data system. It serves as the behavioral cognitive hub of Port3, structuring and semantically interpreting complex on-chain and off-chain behavioral data to provide agents with "understandable, combinable, and callable" data fuel.

3.1.3 Data Application - Rankit + OpenBQL + Ailliance.ai → AI Agent System

Rankit: AI-driven social behavior analysis engine

Rankit is the flagship application of Port3's social data capabilities, serving as the "visual execution" of BQL data capabilities at the AI layer.

The capabilities and paradigm innovations of Rankit:

• Cross-platform social heat score: Integrating social signals from Twitter, Telegram, Discord, etc., to identify key trends, hot projects, and shifts in sentiment in the Web3 world.

• Semantic recognition and scoring modeling: Through NLP and large model sentiment analysis, the discussion focus, KOL influence, and user trust are converted into structured indicators for community governance, lending risk control, on-chain transactions, and other scenarios.

• Vertical scenario landing demonstration: For example, the newly launched USD1 ecological data engine, which links heat maps, social activity, and on-chain momentum to track potential projects on the BNB Chain in real time, becoming an intelligent compass for DeFi users to capture Alpha.

With the support of Rankit, Port3 can not only provide data but also "explanatory data"—not only telling you what happened but also telling you what to do.

OpenBQL: Intent-driven On-chain Execution Language

If SoQuest is the data entry point, then BQL(Blockchain Quest Language) is the data cortex of Port3, serving as the semantic core and operational engine for processing, organizing, and invoking all behavioral data.

The Role and Mechanism of BQL:

• Universal Language Layer: BQL provides a natural language-friendly query structure, allowing developers or agents to execute on-chain operations with commands like "buy NFT on the Aptos chain", bridging EVM, BTC, and Solana multi-chain environments.

• Standardized Execution Layer: Supports on-chain asset operations ( such as trading, staking, and liquidity addition ) with one-click automation, which is the key hub for on-chain behavior automation.

• Data Semantic Extractor: Provides standard structured data support for AI models and Agents, achieving high-frequency data updates and calculations required for information financialization ( InfoFi ).

With the help of BQL, Port3 is promoting the construction of a new "on-chain natural language protocol" in the Web3 world, elevating on-chain actions from the "code layer" to the "intent layer"—machines not only execute the commands you give but can also understand your intentions.

AI Agent Integration Capability: Ailliance.ai

• Port3 is building a universal Agent API layer, allowing developers to directly call structured data generated by Rankit/SoQuest/OpenBQL or execute commands.

• Applications include automated investment assistants, interactive robots, blockchain game smart assistants, etc., covering various scenarios such as trading decisions, task publishing, community operations, and more.

This entire product structure makes Port3 the only platform in the Web3 social data track that possesses the full process capability of "from collection → analysis → application → invocation."

The ultimate goal is to build a Web3 AI standard protocol network based on behavioral data, enabling AI Agents to understand, recognize, and operate on-chain assets.

3.2 The Moat of Port3: The Growth Flywheel from Business Accumulation

Port3 can take a leading position in Web3 AI narrative not primarily due to its advanced large model capabilities, but because it has built a high-value social behavior data asset with significant depth and breadth during its business accumulation process. This data advantage lays a unique foundation for Port3's AI applications, Agent construction, and model training:

3.2.1. Tens of millions of on-chain and off-chain behavioral data accumulation

With the three-year operation of the SoQuest mission platform, Port3 has accumulated over 10 million user participation trajectories, covering various dimensions such as task behavior, wallet interactions, on-chain assets, and community engagement. This data spans Web2 and Web3, including Twitter posts, Discord activity, Telegram retention, on-chain transactions, staking, and holdings, forming a highly dense social behavior map. In the current context of AI models where "data is fuel," this type of structured and high-frequency interaction behavior data is undoubtedly the most valuable input resource for building Web3 AI Agents.

3.2.2 Deep cooperation with thousands of project parties, data continuously updated in real-time.

Port3 is not a platform focused on a single product, but has established partnerships with over 7000+ Web3 projects, covering various scenarios such as airdrop issuance, task design, community governance, and on-chain interactions. This collaboration not only brings real user behavior but also ensures the diversity and real-time nature of data sources. By building data channels with project parties, Port3 continuously absorbs the latest ecological trends and user trends, constructing a dynamically evolving data engine rather than a static snapshot. This data updating capability provides a continuously evolving "training material pool" for AI models.

3.2.3 Forming a dedicated dataset for AI model training to provide semantic support for on-chain Agents

Compared to general Web2 data, Web3 users' on-chain identities, interaction paths, and asset behaviors exhibit high levels of anonymity and structural complexity, making it difficult for traditional models to adapt. Port3, on the other hand, just through Ran