From Social Data to AI Brain: How Port3 Network Builds the AI Network of Web3?

1. Introduction

In the Web3 world, data is shifting from static information to dynamic assets. Users' social behavior data has become the most valuable yet underdeveloped "digital commodities" in the AI era. The immense value contained in the social data generated every moment has yet to be fully tapped.

The reality of Web3 is fragmented: on one hand, vertical protocols such as DeFi, NFT, and GameFi are experiencing explosive growth, creating a large amount of behavioral data from users 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, and it cannot be truly accessed.

At the same time, the rise of AI is rapidly reshaping the entire digital world. Projects like ChatGPT and Claude propose the vision of "callable data + executable intent."

In this context, if AI is the future, who will build the data layer and decision-making foundation for Web3? Port3 Network provides an ultimate answer:

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 intent, making the data an "action template" that agents can understand, invoke, and execute.

Port3 is no longer a single-task platform or tool, but has strategically occupied the "Web3 Data Brain" position in advance, before narratives such as data sovereignty, on-chain identity, and social finance have truly been 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 aimed at 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 an AI-driven Web3 social data infrastructure project aimed at building a cross-chain, programmable, and callable social data layer. By aggregating user behavior data from Web2 and Web3, and standardizing it with an AI engine, 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 investors 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 acquisition of investment from DWF Labs, and received grant support from a certain trading platform Labs, a certain project, and a certain public chain.

2.2.2 Team Situation

Max D.: Co-founder, has work experience in a certain technology company; possesses extensive experience in Web3 project incubation and ecosystem development.

Anthony Deng: Co-founder, previously worked in backend development at a social media platform and a tech company, with years of experience in high-concurrency system design and distributed architecture.

3. Port3's Vision: From "Task Platform" to "AI Social Data Foundation Layer"

Although Port3's product matrix includes several sub-modules like SoQuest, Rankit, OpenBQL, and on.meme, which may seem scattered, they can actually be summarized into a core main line: "Behavior is an asset, and Port3 is 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 triggering mechanism and collects user social behaviors as its targets, bridging the behavioral paths between on-chain interactions and Web2 social platforms.

SoQuest supports multiple mainstream Web2 platforms and is compatible with interactions on 19 chains including EVM, Solana, Aptos, Sui, etc., including transactions, authorizations, NFT minting, etc., 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 massive amount of user behavior records and on-chain social interaction events, creating a real, multi-dimensional, and high-frequency Web3 social behavior database.

To enhance platform 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 dApps or Telegram Mini Apps. In 2025, the verification API will be further opened, enabling the completion of verification logic embedding without preset templates, greatly improving the standardization and universality of the task system.

SoQuest is not just a task platform; it is the starting point for Port3's full-chain behavioral asset ecosystem and also the original source of the behavioral semantic data required for AI reasoning.

3.1.2 Data Accumulation - AI Social Data Layer

The user behavior data captured by SoQuest ultimately settles in the core module of the Port3 Network--AI Social Data Layer, which is a structured behavioral database specifically designed for AI applications, and serves as the underlying infrastructure 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 inference 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, and continuously updates in real-time through application modules such as SoQuest and Rankit, building a dynamically growing social data system. It serves as the behavioral cognitive hub of Port3, structuring and semantically analyzing complex on-chain and off-chain behavior data, providing "understandable, composable, and callable" data fuel for intelligent agents.

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

Rankit: AI-powered 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 innovation of Rankit:

• Cross-platform social popularity score: Integrates signals from multiple social platforms to identify key trends, hot projects, and sentiment shifts in the Web3 world.

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

• Vertical scene landing demonstration: For example, the newly launched USD1 ecological data engine, which links heat maps, social activity, and on-chain dynamics to track potential projects on a public chain in real time, becoming a smart compass for DeFi users to capture Alpha.

With the support of Rankit, Port3 not only provides data but also offers "explanatory data"—not only telling you what has happened but also advising you on 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 the processing, organizing, and calling of all behavioral data.

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 a certain public 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 processing, 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 the financialization of information ( InfoFi ).

With the help of BQL, Port3 is promoting the construction of a new "on-chain natural language protocol" in the Web3 world, allowing on-chain behavior to rise from the "code layer" to the "intention 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, identify, and operate on-chain assets.

3.2 Port3's moat: the growth flywheel brought by business accumulation

Port3 can take the lead in the Web3 AI narrative not primarily because it possesses advanced large model capabilities, but because it has built up a highly valuable social behavior data asset with great 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. Ten million level on-chain and off-chain behavioral data accumulation

With the operation of the SoQuest task platform for nearly three years, Port3 has accumulated user participation data at the level of over 10 million, covering multiple dimensions such as task behaviors, wallet interactions, on-chain assets, and community engagement. This data spans across Web2 and Web3, including activities like posting, engagement, retention, on-chain transactions, staking, and holdings, forming an extremely dense social behavior map. In the current context of AI models where "data is fuel", such 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 timeliness of the data sources. By building data channels in cooperation 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 continuous evolution "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, the on-chain identity, interaction paths, and asset behaviors of Web3 users exhibit high levels of anonymity and structural complexity, making it difficult for traditional models to adapt. Port3 precisely connects on-chain behaviors with natural language semantics through Rankit's semantic recognition and behavioral tagging system. For example: "Wallet A participates in an airdrop in Protocol B + tweets + secondary participation in governance" can be modeled as semantic tags like "active participant" or "early evangelist," enabling AI agents to be more effective.