About Dageno AI

Dageno didn't start as a marketing team. We started as a data and engineering team. For years, we've provided foundational technology and data services to leading GEO/SEO tools overseas—covering data collection, cleaning, modeling, monitoring, and metric system design. Standing on the “data infrastructure” layer, we saw the shift early: users are moving from “search” to “conversation,” and AI is reshaping how brands acquire customers.

In AI search, competition is no longer about “ranking #1.” It's about whether AI sees you, trusts you, recommends you, and cites you. Many brands look strong in traditional search, but become “invisible” on platforms like ChatGPT, Perplexity, and Gemini: AI may know the brand exists, but rarely cites the official site—and doesn't treat the brand as a top choice in high-intent moments.

We built Dageno to turn this from a black box into something you can make decisions on. Dageno is a GEO data strategy platform. We use AI visibility and citation data to identify where a brand is missing in key decision queries, where competitors are hijacking demand, and which source structures actually influence AI recommendations. Then we translate these complex signals into clear priorities and action lists—so growth teams know what to do first, why it matters, and what “good” looks like.

We also believe that in this era, execution automation will become cheaper and more commoditized. The hard part is strategy and context. Dageno doesn't lock you into one automation tool. Instead, we provide a plug-in Agent/Skills system that captures proven marketing and growth playbooks on our platform—so teams can execute with the best tools available, while the loop is always driven by the same data strategy.

Our customers are teams that need scalable acquisition: B2B SaaS/PLG teams, e-commerce and DTC brands, and agencies or professional service teams delivering growth for clients. We aim to help them build brand assets that AI can understand, trust, and recommend—and achieve compounding growth with less trial-and-error.