---
title: "Unique Friends | Boolean Vector’s Wang Qing: Making Commercially Viable AI Video"
author: "Unique Research"
sourcePublication: "Unique Research Substack"
originalPublishedAt: "2025-11-05T08:00:00+00:00"
canonical: "https://ffcap.cn/en/research/unique-research-2025-11-05-02"
source: "https://uniqueresearch.substack.com/p/unique-research-2025-11-05-02"
language: "en"
---

# Unique Friends | Boolean Vector’s Wang Qing: Making Commercially Viable AI Video

_Original · Unique Research · 2025-11-05_

_Editor’s note: This complete English edition preserves the original report and Wang Qing’s statements as of November 5, 2025. Financing, market estimates, user behavior, accuracy, efficiency and conversion figures are source-attributed claims, not independently audited findings. The source supplies no study methodology for the market statistics or measurement definitions for its percentage improvements and ROI ratios; the original ratios are retained without reinterpretation. Boolean Vector is a translation of the Chinese company name. The source names the Chinese editing product Jianying. No publication location is inferred._

In the familiar narrative of industrial society, we have long treated “scale” and “capacity” as equivalent. The productivity of a content team has typically been anchored to headcount, processes, and budgets. But in the deeper waters of AI technology, a quiet revolution in “de-scaling” is under way. From Nanshan, Shenzhen, a team called Boolean Vector and its product temvideo.ai are using AI to rewrite the underlying logic of content production.

The entrepreneurial story of Boolean Vector founder Wang Qing begins in familiar territory: the product logic of WeChat Pay and the engineering discipline of ByteDance’s B2B business—an era defined by platforms and processes. What ultimately led him to start an AI company, however, was not blind admiration for technology itself, but his insight into a structural imbalance between content supply and demand. As globalization transforms cross-border e-commerce, more and more brands want to use video to tell compelling product stories and reach Latin America and the Middle East. Yet expensive editing teams, stubborn language barriers, and the need for frequent content updates leave them with a shared anxiety: they want to make content, but lack the means to do so.

Wang Qing chose to tackle a question that sounds almost blunt in its simplicity: How can an operator who knows nothing about editing, scriptwriting, or multilingual adaptation still produce professional commercial videos?

Boolean Vector’s answer is a content-generation agent built for the future. It is not a bundle of tools, but a reconstruction of the entire workflow. From collecting assets and generating scripts to automatic editing, multilingual output, and performance-feedback optimization, it acts as a video-content operations specialist that can “think.” Unlike the patchwork SaaS tools on the market, Boolean Vector is more like an intelligent collaborator embedded in the business workflow. It does not wait for commands; it proactively understands, plans, executes, and adjusts. By freeing individuals from hands-on operations, it can truly deliver on the promise: “State the objective, and the result is delivered automatically.”

The rise of this Agentic AI paradigm is no accident. Wang Qing believes the true breakthrough in generative AI is not “how much copy a large model can write,” but whether it can genuinely understand complex requirements and deliver results through a closed loop. With proprietary multimodal technology and vertical industry models, Boolean Vector directly addresses the persistent problem that “general-purpose large models adapt poorly.” It enables AI to understand product logic, the rhythm of conversion, and even the visual language of different markets.

This is not a technologist’s fantasy. It is a response to commercial reality.

For one three-person cross-border consumer-electronics team that the company serves, simply importing SKU links enables AI to produce 50 multilingual videos per day, with natural lip synchronization and professional structure. After deployment, ROI even rose from 1:1.2 to 1:3. This capacity for personalized production at scale not only lowers the barrier to creation, but redraws the boundaries of productivity. The individual is no longer the final link in the content chain, but the engine of a new production model.

Yet the magic of this era comes at a price. Wang Qing recognizes clearly that as AI lowers technical barriers, content homogeneity and the scarcity of innovation become the new central tension. Individuals must therefore do more than master AI tools: they need to accumulate industry know-how and develop a distinctive content identity in specific scenarios. AI can write on your behalf, but it cannot experience on your behalf. It can edit, but it cannot empathize for you.

Boolean Vector’s strategy consequently combines three elements: technology, business, and aesthetics. Technology defines the boundaries of capability; business determines a sustainable path; and aesthetics will decide whether future AI content can truly “move people.” The team has created holiday-promotion templates for Latin America and family-scenario scripts for the Middle East, and has even used voice-cloning technology spanning more than 140 languages to localize emotional tone. Behind this work lies a deep understanding of cultural differences—and the confidence that allows Chinese AI companies to make their voices heard on the global stage.

Looking across today’s AI startup landscape, Boolean Vector may not be the most prominent name, but it represents a more pragmatic force for innovation. It neither worships theories of a large-model singularity nor limits itself to isolated tool features. Instead, it is rooted in genuine commercial pain points and sustainable user value.

With an agent-based solution, the company gives small teams, individual creators, and even global brands trapped in a “content dilemma” a way out of the confusion. This is not only a technological revolution in video-content production, but also a paradigm for reconstructing individual productivity.

Over the next several years, as AI permeates the content ecosystem, the decisive competitive question will be: “Who can become their own content command center?” Providers of agents such as Boolean Vector are not merely empowering individuals; they are accelerating the arrival of the “era of the intelligent individual.” In this era, the winners will not be those with large teams, but those who use AI to build a “supercharged self.”

This is a golden window for intelligent individuals. Boolean Vector has quietly turned the first page.

Interview Highlights: Q&A

Q1: Mr. Wang, we understand that you previously held senior positions at Tencent and ByteDance. What led you to leave and start Boolean Vector in 2021?

Wang Qing: I had considerable experience working on B2B products at Tencent’s WeChat Pay and ByteDance. In 2021, I recognized a major opportunity to combine AI technology with cross-border e-commerce, so I founded Boolean Vector. We set out to bring together “technology, business, and aesthetics,” with a specific focus on AI video generation. Development has gone well so far: we raised an angel round of nearly US$10 million from Linear Capital and Volcano Stone Capital.

Q2: Your company sounds highly technical. Could you explain your core positioning in plain language?

Wang Qing: Our positioning is to build an “AI video-content operations specialist agent.” Put simply, it is a one-stop intelligent video platform. Give it an image-and-text post or a product link, and it automatically converts that material into multilingual marketing videos. We aim to become the infrastructure for video creation in cross-border e-commerce, enabling people to produce video at scale and at very low cost.

Q3: What was your original motivation for starting the company? What drives you to keep pushing so hard in AI video?

Wang Qing: My original motivation was to solve a central pain point for cross-border sellers: “They want to market globally, but the barriers to video production hold them back.” I wanted marketers who know nothing about editing or planning to be able to “direct” professional commercial videos through AI.

There are two main sources of motivation:

The demand is very real: 78% of overseas consumers decide whether to buy only after watching a video. Traditional methods cannot possibly meet merchants’ need for daily updates, multilingual content, and bulk product launches. AI is the only solution.

The trend is unmistakable: The global video-marketing market is worth more than US$200 billion and continues to grow. Demand for AIGC—AI-generated content—in cross-border e-commerce is surging, and the trend is irreversible.

Q4: Your flagship product, the “AI video-content operations specialist agent,” sounds impressive. What major problem does it solve for customers in practice?

Wang Qing: Its greatest value is an “end-to-end closed loop.” In the past, customers had to jump among scriptwriting tools, editing software, and translation platforms to make a single video. It was extremely cumbersome.

Our product handles assets, scripts, editing, translation, and multi-platform distribution in one workflow, improving efficiency by 70%.

And we do not work blindly. We have developed more than 120 industry templates for sectors such as fashion and beauty. Ads made with our templates deliver conversion performance 30% above the industry average.

Q5: AI technology is evolving extraordinarily quickly. What do you consider the most important breakthroughs today, and how do you master and apply these new technologies in your product?

Wang Qing: I see three:

Multimodal integration: AI once processed text or images in isolation. It can now understand text, images, and audio together. Using this technology, we have achieved 98.7% accuracy in asset recognition.

Agentic AI: AI is evolving from a “tool” into an “autonomous employee.” You do not have to direct it step by step; it can independently plan the entire process of matching assets, writing scripts, and optimizing performance.

Vertical large models: General-purpose large models know a little about everything, but lack depth. We combine general models with industry know-how—such as the logic of video in cross-border e-commerce—to solve the problem of poor fit in specialized contexts.

Our core product is itself a practical implementation of an agent. We integrate the industry’s leading AI models while also developing foundational models for core scenarios in-house.

Q6: Many companies talk about balancing technological innovation with commercial implementation, but it is genuinely difficult. How do you balance “spending heavily on R&D” with “earning enough to sustain the business”?

Wang Qing: R&D accounts for more than 80% of our investment, which is indeed high. Internally, however, we follow a “70/30 rule”:

70% of our R&D effort must focus on solving customers’ immediate pain points and on features that can be implemented quickly.

30% goes into advance research on frontier technologies, such as multimodal large models.

Our strategic logic is clear: “Enter through a vertical scenario → validate the outcome → promote it at scale.” We are determined to avoid technology that spins its wheels without producing value.

Q7: You operate across borders. What are the greatest opportunities and challenges in global expansion?

Wang Qing: The opportunity is enormous. Global cross-border e-commerce exports amount to trillions, and overseas companies’ demand for professional video has grown 55% year on year. The market is hungry.

The challenge, of course, is competition:

International giants such as OpenAI and Google dominate the high-end market.

Chinese tools such as Jianying compete for consumer users.

Many vertical competitors are also battling over individual features.

Q8: Cultures and languages differ greatly across countries. How does your AI handle localization? Surely you cannot push the same videos to users in the Middle East and Latin America.

Wang Qing: That is exactly the right question. We work on two fronts:

Content localization: We have built a “regional template library.” For example, we developed holiday-promotion templates for Latin America and family-scenario templates for the Middle East.

Technical localization: We developed multilingual lip synchronization supporting more than 140 languages, as well as voice cloning that can preserve emotional intonation. A client producing short-form dramas used this capability and improved the adaptation efficiency of multilingual videos by 90%.

Q9: The theme of this conference is “Pioneering Intelligence | The Era of the Individual.” How do you interpret it?

Wang Qing: To me, the core idea is that AI breaks the traditional link between productivity and team size.

In the past, anyone who wanted to “generate multilingual videos in bulk” needed a team of at least 10 people, including editors, translators, and planners.

Today, an “individual” or a 3-person team can achieve the same thing with AI. In the future, “intelligent tools” will replace “resource scale” as the key source of competitiveness.

Q10: This idea of an “era of the individual” is compelling. Could you share a real customer example showing how your AI helps a “one-person company” or small team accomplish something substantial?

Wang Qing: Certainly. We work with a cross-border 3C consumer-electronics team of just 3 people—1 operator and 2 customer-service staff—yet they manage more than 500 SKUs.

Bulk generation: They imported 500 product links into our system. AI automatically generated two types of scripts, for “unboxing” and “reviews,” matched them with the relevant assets, and produced more than 50 videos per day, covering every SKU.

Multilingual adaptation: With one click, the system generated English and Spanish versions with accurate lip synchronization, eliminating the need for outsourcing.

Performance optimization: Based on campaign data, AI also automatically adjusted the first 3 seconds of each video to improve click-through rates. Ultimately, their ROI rose from 1:1.2 to 1:3.

Q11: For “AI Creators” hoping to make money with AI, what do you see as their greatest opportunity and most serious challenge today?

Wang Qing: The greatest opportunities are clear:

A substantial demand gap: In areas such as cross-border e-commerce and the international expansion of short-form dramas, demand for AI video has grown by 55%, but supply has not kept up.

Lower barriers: Tools like ours allow people without technical expertise to produce strong content.

The globalization dividend: With AI localization capabilities, individuals can quickly reach markets around the world.

The most serious challenges are:

Content homogeneity: When everyone rushes to use AI, the resulting content all begins to “taste the same,” making creativity increasingly scarce.

Pressure from technological iteration: AI technology changes every two or three months, so individuals must continue learning.

Q12: One final question. Over the next 1–3 years, what practical advice would you give “individuals” or “small teams” that want to capture the opportunities created by AI?

Wang Qing: I have two recommendations:

Go deep; do not “cast a wide net”: Do not experiment with one AI product today and another tomorrow. Commit to the AI tools in 1–2 vertical fields and develop genuine depth and mastery.

Build industry know-how: Do not depend entirely on AI generation. You must build your own understanding and judgment of the industry. AI is a tool that amplifies your capabilities, not your brain.

Try it at: https://temvideo.ai/en

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Original publication: https://uniqueresearch.substack.com/p/unique-research-2025-11-05-02
On-site reading page: https://ffcap.cn/en/research/unique-research-2025-11-05-02