When users ask AI "Which one is more suitable for me" and your brand is not mentioned at all, Adgine aims to turn "making brands clearly and accurately articulated by AI" into a deliverable business.
Adgine: Empower Brands to Gain the "Eligibility" for AI Recommendations
——Starting from official information sources, turn "enabling brands to be clearly articulated by AI" into a deliverable business, and launch financing simultaneously
"Which one is more suitable for me?" "How to choose within this budget range?"
If you have recently thrown such questions directly to ChatGPT, Perplexity, or domestic AI assistants Doubao, Kimi, you have already stood at a new entry point. In the past, to get answers to these questions, you had to browse five or six web pages, watch three reviews, and then compare information on forums; now, you can get the answers in a single conversation.
The change takes place on the user side, but the pressure falls on the brand side. Because the list given by AI may include your brand or not; when it introduces what your brand does, it may get the facts right, or only get half of them right.
A company operating in this track is called Adgine. Its operating entity is Shanghai Parallel Flash Network Technology Co., Ltd.
I. Brand Competition Has a New Entry Point: From "Being Seen" to "Being Recommended"
The traditional brand growth system basically revolves around keywords, landing pages, search rankings and ad placements. This system solves the problem of "whether users can find me".
Generative AI uses a completely different evaluation logic. In a specific conversation, it integrates information from different sources, product descriptions and user scenarios, and finally gives a judgment. In other words, AI does not care whether you have bought traffic, it cares whether it can clearly describe your brand.
For enterprises, this is no small change. When brand information is missing, inconsistent in caliber, or lacks verifiable evidence and cases, AI is very likely to fail to identify who you are at all; even if it recognizes you, it has no reason to mention you when users make comparisons and choices.
Search rankings compete for positions, while AI recommendations compete for "eligibility".
Adgine summarizes this path as a continuous process from "being seen" to "being understood", and from "being trusted" to "being recommended": the first three steps are the process, and the last step is the actual business. The project team believes that the emergence of the AI recommendation entry does not mean that the original search, content and media channels are invalid — it adds a new decision-making entry that requires long-term operation in brand competition.
Two types of enterprises have the most obvious demand: one is B2B companies with complex business information, long product cycles and the need for professional explanations; the other is B2C brands that highly rely on comparison, word of mouth and purchase decisions. What they need is not one-time exposure, but a set of brand fact system that they can control, update continuously, and adapt to different AI platforms.
Precisely because of this, the value of this market should not be measured by "single mention" or "short-term ranking". What really determines the value is whether the brand can be continuously and accurately represented in real user queries, and every representation is traceable.
II. Not Generating Marketing Scripts, But Producing "Information Sources"
The core proposition of Adgine is only one sentence: Starting from official information sources, build a credible foundation for brands to be included in AI recommendation answers.
Translated into actionable steps, it is the following path — starting from users' real scenarios and decision-making tasks, form a repeatable question set and test samples; then collaborate with the brand's own channels such as corporate websites, official accounts, product materials, cases and FAQs to establish a unified fact baseline, making them official information sources that AI can read, cite and verify; update content and information sources collaboratively around the question set; finally, review the results based on AI's real answers, cited sources, brand mentions and competitors' performance.
It is worth noting that the starting point is set as "official information sources" rather than "content quantity".
Behind this is a very practical judgment: AI is more willing to cite first-hand information with clear structure, unified caliber and cross-verifiability in its answers. If the enterprise's own official caliber is ambiguous, no matter how many external press releases there are, they will only add noise.
The whole industry is still exploring how to measure the effect of GEO (Generative Engine Optimization). Adgine's approach is not to take a single answer as the conclusion — it records question samples, baselines, execution actions, time windows and result changes, and separates "observed changes", "correlation" and "further verifiable impacts". It does not promise an unverifiable number, but provides a verifiable process.
Existing project practices have produced comparable results. Taking the game industry site Lovesudoku.net as an example, the team spent one month increasing the mention rate of this site in AI answers from 0% to 30.4%, and the first recommendation rate to 40.8%. To put it simply — the former refers to "whether AI will mention it in relevant questions", and the latter refers to "when it is mentioned, whether it ranks first". In the same period, indicators such as site traffic, number of AI-referred sessions and number of AI-referred users all increased by more than 500%, and the same practice has been successively verified in other projects.
More importantly is the starting point. 0% means this is not moving the existing exposure to another position, but realizing the transition from "AI does not know who you are at all" to "being ranked in the front of the answers when relevant questions are asked".
This set of results is currently mainly used to verify the connection path between official information source construction, content collaboration, AI recommendation performance and business growth, and scenarios in the education industry are also being promoted simultaneously. For different AI recommendation entries in China and overseas, the team is also continuously studying their differences in indexing, citing and recommendation mechanisms, and making adaptations according to the project scope.
III. Agents Do the Work, Humans Make Judgments
At the technical level, Adgine adopts self-developed AI Agents and algorithm workflows, covering brand diagnosis, problem analysis, content generation, platform monitoring and phased review, aiming to improve the consistency and efficiency of delivery.
But the team has made it very clear where the boundary lies: strategic judgment, brand caliber, fact verification, compliance control and key releases are still jointly confirmed by the professional team and the brand side.
Automation is responsible for speeding up the work, while the right of judgment and responsibility remain in human hands.
This division of labor is not just a gesture, but part of the methodology. Once content generation or automated monitoring is equated with the final judgment, brand assets will stay in the platform, instead of being precipitated into the enterprise's self-controllable knowledge base, official information sources and content evidence.
The team has a solid background in traffic monetization. The predecessor of Adgine has long been engaged in user acquisition, ad monetization and data operation, with business covering game R&D and global distribution. Its core members come from the game, Internet and technology industries, with compound experience in products, technology, traffic and brand growth. The project was incubated in the second half of 2025, and has gradually productized brand diagnosis, problem research, content and information source execution, monitoring and review, now in the stage of customer verification and continuous iteration.
A team with a background in traffic and monetization turning to build brand fact baselines seems counterintuitive at first. But on second thought, these two things test the same capability: turning uncertain attention into measurable, repeatable business operations.
IV. Financing and Next Steps
In terms of business model, Adgine provides GEO services for B2B and B2C enterprises with long-term brand building needs, covering brand fact baseline, user problem research, official information source construction, content collaboration, AI monitoring and verification, and phased review. The deliverables include not only pre-diagnosis and strategy design, but also execution and continuous optimization. In the future, the team also plans to expand service coverage through cooperative channels such as brand marketing service providers, industry institutions and media.
The project team stated that Adgine is currently seeking financing, and the funds are planned to be used for three things: product iteration, delivery capability building, and market expansion.
The focus of the next stage is also very clear — improve the monitoring and evaluation system for real queries, supplement the adaptation capability for different AI platforms, and promote the iteration of products and delivery standards on the basis of traceable data and evidence. The goal is not to close a single deal with a short-term promise, but to help brands build an AI recommendation foundation that can be operated in the long term.
Looking back, the difficulty of GEO does not lie in making AI mention you once, but in making AI accurately describe you every time relevant questions are asked. This does not require speculative skills, but long-term, evidence-based basic capabilities.
AI recommendation is not a one-time traffic dividend, but a trust contract that needs to be renewed every year.
The story of Adgine has just begun.