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

1. Introduction

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

The current 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 massive amounts 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 and unable to be truly utilized.

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

Against this backdrop, a question arises: If AI is the future, who will build the data layer and decision-making foundation of Web3? Port3 Network provides a somewhat ultimate answer:

From the initial SoQuest task platform, to the Rankit social behavior scoring engine, and then to the OpenBQL 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 behaviors but also standardizes and recognizes intents, making 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, and social finance being fully integrated.

This article will deeply analyze the product matrix, technological moat, token mechanism, and growth logic of Port3, exploring how it establishes a closed loop of data circulation 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 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 supplemented by an AI engine for standardized processing, Port3 has created a complete closed loop ranging 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 multi-million dollar funding, with investors including EMURGO, Adaverse Accelerator, and Gate Labs.

October 2023: Announced the acquisition of investment from DWF Labs, along with grant support from Binance Labs, Mask Network, and Aptos.

2.2.2 Team Situation

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

Anthony Deng: Co-founder, previously worked 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 sub-modules 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 conversion."

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 the triggering mechanism and user social behaviors as the collection target, bridging the behavioral paths between on-chain interactions and Web2 social platforms.

SoQuest supports mainstream Web2 platforms such as Twitter, Telegram, and Discord, and is compatible with interactions across 19 chains including EVM, Solana, Aptos, and Sui, encompassing transactions, authorizations, NFT minting, etc., forming one of the most comprehensive behavioral collection systems in the Web3 field.

By 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 record of user behavior and chain social interaction events, building 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 parties to embed the task system into their own dApp or Telegram Mini App. In 2025, the verification API will be further opened, allowing 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 of Port3's full-chain behavioral asset closed loop, and it is 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 into the core module of the Port3 Network------AI Social Data Layer, which is a structured behavior database specifically designed for AI applications, and also the underlying facility for Port3 to achieve "behavior assetization" and "information financialization (InfoFi)".

Unlike traditional on-chain data platforms ( such as The Graph and Dune, which 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, and continuously updates in real-time through application modules such as SoQuest and Rankit, building a dynamically self-growing social data system. It serves as the behavioral cognitive hub of Port3, structuring and semantically enriching 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 innovation of Rankit:

• Cross-platform social popularity 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 focus of discussion, KOL influence, and user trust will be 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 tracks potential projects on the BNB Chain in real-time through heat maps, social activity, and on-chain momentum, becoming an intelligent compass for DeFi users to capture Alpha.

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

OpenBQL: Intent-driven On-chain Execution Language

If SoQuest is the data entry, 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 Aptos chain", bridging the multi-chain environment of EVM, BTC, and Solana.

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

• Data Semantic Extractor: Provides standard structured data support for AI models and Agents, achieving the high-frequency data updates and calculations required for the 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 behavior from the "code layer" to the "intention layer" ------ machines not only execute the instructions 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 operation, 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 manipulate on-chain assets.

![From Social Data to AI Brain: What Kind of AI Network Will Port3 Network Build for the Web3 World?])https://img-cdn.gateio.im/webp-social/moments-e99f6dd0ad2c1543a76148cd77b3d3a7.webp(

) 3.2 The moat of Port3: The growth flywheel brought by business accumulation

Port3 can take a leading position in Web3 AI narratives not primarily because of its advanced large model capabilities, but because it has built a highly valuable asset of deep and broad social behavior data through its business accumulation process. This data advantage lays a unique foundation for Port3's AI applications, Agent construction, and model training:

(# 3.2.1. Millions of on-chain and off-chain behavioral data accumulation

Relying on SoQuest's three-year operation of the task platform, Port3 has accumulated user participation trajectories at the level of over 10 million, covering multiple dimensions such as task behavior, wallet interaction, on-chain assets, and community participation. This data spans Web2 and Web3, including Twitter posts, Discord activity, Telegram 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 interactive behavior data is undoubtedly the most valuable input resource for building Web3 AI Agents.

)# Deep collaboration with thousands of projects, data continuously updated in real-time

Port3 is not a platform oriented towards a single product, but has established partnerships with over 7000+ Web3 projects, covering multiple 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 co-constructing data channels with project parties, Port3 continuously absorbs the latest ecological trends and user trends, building a dynamically evolving data engine rather than a static snapshot. This capability for data updates provides a continuously evolving "training material pool" for AI models.

3.2.3 Create 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 anonymity and structural complexity, making it difficult for traditional models to adapt. However, Port3 precisely connects on-chain behavior with the mapping path of 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 governance participation" can be modeled as "active participant."