Semrush Launches Official Connector for Claude, Bringing Marketing Intelligence into AI Conversations
New native integration enables access to search and market intelligence directly within Claude to automate marketing workflows.
August 26, 2026
New Reddit and Negative Sentiment Analysis capabilities give brands deeper visibility into the sources shaping their perception in AI search.
Semrush, an Adobe company, today announced the launch of two new automations in its Enterprise AI Optimization (AIO) solution: Reddit Analysis and Negative Sentiment Analysis.
As AI search grows in adoption, brand perception becomes increasingly influenced by the complex mix of publishers, reviews, editorial content, and user discussions that act as its sources. These new automations help bring structure to that complexity, enabling teams to move from manual observation to precise, source-level understanding.
Refining Brand Perception Through Source Insights
Both workflows are designed to reduce the manual effort required to analyze AI search citations, saving teams hours of investigation while improving the accuracy of insights.
- Reddit Analysis focuses on one of the most influential sources in AI responses, helping brands understand where they are missing from high-impact conversations.
- Sentiment Analysis expands this view across all cited domains, identifying sources and topics that contribute to negative brand perception.
Together, they provide a more granular understanding of how a brand is framed, which sources are driving that narrative, and what to do next.
Reddit Analysis: Finding the Conversations Impacting AI Visibility the Most
Reddit has emerged as one of AI’s most frequently cited domains, making it a critical visibility channel. However, valuable insights can be buried deep in threads, easily missable with high-level analysis.
The new Reddit Analysis workflow enables users to:
- Identify threads where competitors are cited but their brand is absent.
- Surface the discussions influencing AI answers the most in their market.
- Prioritize engagement in conversations that AI already utilizes.
Recognizing that participation in these conversations is a directional lever, this allows brands to focus efforts where they are most likely to influence downstream AI visibility.
Negative Sentiment Analysis: Understanding What’s Shaping Perception
While a brand’s overall visibility is most crucial in AI performance, ensuring accurate and positive perception comes a close second. However, discerning the levers behind a brand’s sentiment and appropriate responses, is difficult and time-consuming.
The Negative Sentiment Analysis workflow addresses this by automatically analyzing cited sources across tracked prompts to:
- Identify domains contributing most to negative brand perception.
- Pinpoint topics or concept clusters receiving unfavorable framing.
- Find opportunities for content, PR response, or outreach to rebalance narratives.
This transforms source data into a clear picture of what and why negative sentiment could be emerging in responses, helping teams prioritize corrective action where it matters most.
Supporting More Precise, Scalable AI Search Strategy
Both workflows are built to support enterprise teams managing AI visibility at scale:
- Brand and PR teams can identify reputation risks earlier and respond quickly and more strategically.
- SEO and content teams can align efforts with the sources and content that influence AI answers.
- Marketing leaders gain a more grounded view of how their brand’s perception is evolving, and the triggers behind it.
AI search is a dynamic environment where outputs shift based on model updates, source weighting, and user context. In response, these workflows provide structured guidance helping teams act with greater confidence without relying on static assumptions.
Turning Source Complexity Into Competitive Advantage
Through automated Reddit Analysis and Negative Sentiment Analysis, Semrush Enterprise gives brands a more complete view of the influences behind their AI visibility and customer perception.
The result is a more precise approach to managing AI visibility, one that enables teams to understand the underlying source material shaping AI answers.
