From Social Data to AI Brains: What Kind of AI Network Will Port3 Network Build for the Web3 World?

1. Introduction

In the world of Web3, data is transforming from static information into dynamic assets. In particular, user social behavior data is becoming the most valuable yet underutilized "digital mineral" in the era of AI. The immense value contained within the social data generated every moment has not yet been fully explored by people.

We see that the reality of Web3 is fragmented: on one hand, we have witnessed explosive growth of vertical protocols such as DeFi, NFT, and GameFi, with users generating a vast 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, and it cannot be truly accessed.

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

Against this backdrop, a question arises: If AI is the future, then who will build the data layer and decision-making foundation for Web3? Port3 Network provides a rather 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 behavior, but also through standardization and intent recognition, transforms data into "action templates" that can be understood, invoked, and executed by intelligent agents.

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

Next, we will delve into the product matrix, technological moat, token mechanism, and growth logic of Port3, exploring how it establishes a data circulation closed loop 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 utilizing 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 (Aillance.ai), becoming a key facility for assetizing on-chain behavior in the AI era.

Project Overview 2.2

Financing situation:

• Completed $3 million seed round financing in February 2023
• Secured a new round of millions of dollars in funding in August 2023
• Received investment from DWF Labs in October 2023 and received support from multiple institutional grants.

Team situation:

• Max D.: Co-founder, with experience working 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 years of experience in high concurrency system design and distributed architecture.

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

The product matrix of Port3 includes multiple sub-modules such as SoQuest, Rankit, OpenBQL, and on.meme. Although they appear dispersed, they can actually be summarized into a core mainline: "Behavior as an asset, 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 the triggering mechanism and user social behaviors as the collection 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 interactive behaviors on 19 chains including EVM, Solana, Aptos, and Sui, including transactions, authorizations, NFT minting, etc., forming one of the most comprehensive behavior collection systems in the Web3 field.

By mid-2025, Port3 Network has collected dynamic data from over 6 million users and 7,000 projects, with data coverage exceeding 10 million crypto users. This has generated a large-scale record of user behaviors and blockchain 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 projects to embed task systems 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 predefined 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 closed loop 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 is ultimately consolidated into the core module of the Port3 Network -- the AI Social Data Layer, which is a structured behavior database designed specifically 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 such as The Graph and Dune, which are designed with "query" as the goal, 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, creating a dynamically growing social data system. It serves as the behavioral cognitive hub of Port3, structuring and semantically interpreting complex on-chain and off-chain behavior data, providing 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 heat score: Integrates social signals from Twitter, Telegram, Discord, etc., 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 converted into structured indicators for scenarios such as community governance, lending risk control, and on-chain transactions.

• Vertical scenario 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 not only provides data, but also offers "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 cerebral cortex of Port3's data, serving as the semantic core and operating engine for the processing, organizing, and calling of 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 the multi-chain environment of EVM, BTC, and Solana.

• Standardized Execution Layer: Supports on-chain asset operations ( such as transactions, staking, and liquidity addition ) with one-click automation processing, 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 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 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 the structured data generated by Rankit/SoQuest/OpenBQL or execute instructions.

• 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 capability for the entire process 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 Port3's moat: the growth flywheel brought by business accumulation

Port3 can take the lead in Web3 AI narratives not primarily because it possesses advanced large model capabilities, but because it has built up 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. Ten million level on-chain and off-chain behavioral data accumulation

Relying on SoQuest's three-year mission platform operation, Port3 has accumulated over 10 million user participation trajectories, covering multiple dimensions such as task behavior, wallet interactions, 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," this type of structured and high-frequency interaction behavior data is undoubtedly the most valuable input resource for building Web3 AI Agents.

Deep collaboration 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 cooperation not only brings real user behaviors but also ensures the diversity and real-time nature of data sources. Through the data channels co-built with project parties, Port3 continually absorbs the latest ecological trends and user trends, constructing a dynamically evolving data engine rather than a static snapshot. This data update 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, 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. However, Port3 precisely bridges the mapping path between on-chain behavior and natural language semantics through Rankit's semantic recognition and behavioral tagging system. For example: "Wallet A participates in an airdrop on Protocol B + tweets + re-participates in governance" can be modeled as "active participant" or "early evangelist."