Not another dashboard. Mersel AI applies an agent-as-a-service model to implement GEO end-to-end, improving citation and recommendation rates.

Mersel AI, Inc. announced the launch of its Generative Engine Optimization (GEO) execution platform, designed to help brands improve how they appear in AI-generated answers and recommendations across major AI assistants.

As AI search tools such as ChatGPT, Perplexity, Gemini, and Claude become a common starting point for product research, category discovery, and vendor comparisons, many marketing and growth teams are adopting AI visibility tools that measure brand mentions, prompt level position, and share of voice. Mersel AI said that measurement is useful, but it rarely changes outcomes on its own because AI citations depend on whether a brand’s information can be interpreted, verified, and summarized reliably by large language models.

Mersel AI is positioning its approach around an agent-as-a-service model, reflecting a broader shift from licensing tools to buying outcomes. Instead of asking teams to add another interface and backlog of tasks, the platform is built to ship implementation and iterate continuously based on what AI systems actually cite and recommend.

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“Many teams can measure where they are missing in AI answers, but they still need an execution layer that ships the fixes,” said Joseph Wu, Founder of Mersel AI. “AI systems cite sources that are easier to parse, consistent across pages, and supported by credibility signals. Our goal is to make brands eligible for citation through end-to-end GEO execution.”

The GEO execution platform operationalizes four areas that influence citation and recommendation behavior:

– Machine-readable layer on top of existing websites
Mersel AI implements structured data, schema markup, and semantic signals to improve how AI systems interpret brand and product information without requiring a website rebuild or any code changes. This layer is designed to reduce ambiguity in core facts such as product attributes, pricing context, policies, and positioning.

– Content structured for AI summarization and citation
The platform supports recurring publication of prompt-aligned content built around the questions people ask AI assistants, including category queries, comparisons, and real use cases. Content is structured for summarization and citation so AI systems can extract the key points with less friction.

– Third-party presence and trust signals
Mersel AI strengthens off-site brand presence across relevant review sites, social media platforms, and editorial sources by leveraging internal agentic interactive tools. These signals can influence how AI systems validate claims and form recommendations, especially in categories where products and messaging look similar.

– Cross-platform AI visibility measurement tied to iteration
The platform tracks visibility across multiple AI platforms and reports on brand-mention rate, prompt-level position, and share of voice versus competitors. Mersel AI uses these signals to guide ongoing updates and refresh cycles, connecting measurement directly to shipped changes.

Mersel AI said the platform is designed for teams that want to operationalize GEO without building an internal function from scratch, including organizations that need consistent coverage across AI assistants and frequent iteration as models and user prompts evolve.