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

1. Introduction

In the world of Web3, data is shifting from static information to dynamic assets. Especially user social behavior data is becoming the most valuable yet underdeveloped "digital mineral" in the AI era. The enormous value contained in the social data generated every moment has yet to be fully explored.

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 large 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 construct a unified profile and unable to be truly utilized.

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 intent."

Against this backdrop, a question arises: If AI is the future, who will build the data layer and decision foundation of Web3? There is a project that provides a more 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, making the data an "action template" that can be understood, invoked, and executed by agents.

In other words, Port3 is no longer a single task platform or tool, but has strategically occupied the "Web3 Data Brain" position ahead of the narrative integration of data sovereignty, on-chain identity, social finance, and others.

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 aimed at AI Agents in the fragmented Web3 world, and becomes the hidden infrastructure of the next trillion-level 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 from data collection (SoQuest), structured scoring (Rankit), intelligent querying (OpenBQL) to Agent invocation (Ailliance.ai), becoming a key facility for on-chain behavior assetization in the AI era.

Project Overview 2.2

2.2.1 Financing Status

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 a certain investment institution.

October 2023: Announced the acquisition of investment from DWF Labs, along with grant support from a trading platform's lab, Mask Network, and Aptos.

2.2.2 Team Situation

Max D.: Co-founder with experience working at a tech giant; possesses extensive experience in Web3 project incubation and ecosystem expansion.

Anthony Deng: Co-founder, formerly engaged in backend development at a certain social platform and a certain technology company, with 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 multiple sub-modules such as SoQuest, Rankit, OpenBQL, on.meme, etc., which seems scattered, it 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 user social behavior as its 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 on 19 chains including EVM, Solana, Aptos, and Sui, covering activities such as trading, authorization, and NFT minting, 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, with coverage extending to over 10 million crypto users. This has generated a vast array of user behavior records and blockchain social interaction events, creating a real, multi-dimensional, and high-frequency Web3 social behavior database.

In order to enhance the platform's 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 up, enabling the completion of verification logic embedding without the need for 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 closed loop and also the original source of the behavioral semantic data needed to support 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 - the AI Social Data Layer, which is a structured behavioral database specifically designed for AI applications, and also serves as the underlying infrastructure for Port3 to achieve "behavioral assetization" and "information financialization (InfoFi)."

Unlike traditional on-chain data platforms (such as The Graph, Dune, etc.) that are designed with the goal of "querying," Port3's data layer focuses on how to make data usable for AI models and supports 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, providing agents with "understandable, combinable, and callable" data fuel.

3.1.3 Data Applications - 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 scoring: 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: By utilizing NLP and large model sentiment analysis, the focus of discussions, KOL influence, and user trust levels will be converted 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 uses heat maps, social activity, and on-chain momentum to dynamically track potential projects on the BNB Chain, becoming an intelligent compass for DeFi users to capture Alpha.

With the support of Rankit, Port3 not only provides data but also "explanatory data"—not only telling you what happened but also what you should 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, 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: A one-click automation process that supports on-chain asset operations (such as trading, staking, and liquidity addition) is the key hub for automating on-chain activities.

• Data Semantic Extractor: Provides standard structured data support for AI models and Agents, enabling 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 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 instructions.

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

This entire product structure makes Port3 the only platform in the Web3 social data track that possesses the full process capability of "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.

The Moat of Port3: The Growth Flywheel Generated by Business Accumulation

Port3 can take the lead in Web3 AI narratives not primarily because of its advanced large model capabilities, but due to its accumulation of high-value social behavior data assets that have been built with great depth and breadth during its business development process. This data advantage lays a unique foundation for Port3's AI applications, Agent construction, and model training:

3.2.1. Millions-level on-chain and off-chain behavioral data accumulation

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

3.2.2 In-depth cooperation with thousands of project parties, with 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 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 timeliness of data sources. By building data channels together with project parties, Port3 continuously absorbs the latest ecological trends and user behaviors, constructing a dynamically evolving data engine rather than a static snapshot. This capability for data updates provides support for AI models.