Executive readout on visibility, sentiment, citations, answer consistency, brand alignment, hallucination risk, and share-of-voice across leading language models.
Acme’s brand is gaining momentum across leading language models. On ChatGPT and Perplexity, the company enjoys high visibility and answers frequently cite official product pages and well-known media sources. Overall sentiment trends positive and the brand story is clear to most users. In Google’s AI Overviews, results are mixed. We see variation by region, and product names do not always appear in a consistent way. The fastest path to lift performance is to standardize the sources the models rely on and to publish short, credible proof points from leadership that the models can quote directly. Doing this will improve summary quality and keep the brand message aligned across systems.
Our audit team ran a 90-prompt battery across brand, product, and competitive themes. Each language model was scored on seven core metrics: Visibility, Weighted Sentiment, Citation Quality, Answer Consistency, Brand Alignment, Hallucination Rate, and Share-of-Voice. All results are normalized to a 0–100 index for clarity and comparability.
Numbers are illustrative. Replace arrays in the script with your live metrics.
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Align product names and IDs everywhere—Docs, Blog, Press—so LLMs see one story. Ship schema.org Product and FAQ in JSON-LD on product, compare, and pricing pages.
rel="canonical"Publish tight, attributed quotes and at least three measurable customer outcomes per product. That gives answer engines crisp lines to lift directly into summaries.
Add clear “Not applicable” and “Limitations” sections in docs, expand product FAQs, and publish model boundaries so answers don’t fill gaps with fiction.
Localize the top ten pages, align pricing and feature tables, and ensure
hreflang plus region-consistent product names so results don’t drift by market.
Across the leading large language models, your brand shows solid visibility and generally positive sentiment. However, inconsistencies in product naming, missing proof points, and regional gaps still weaken the way answers land for executives and customers.
The next 60 days are about hardening sources, amplifying proof points, and closing gaps in geography and consistency. This combination raises answer quality, lowers hallucination, and positions your brand as the authoritative reference across LLMs.
AI-driven discovery is now the first touchpoint. Ensuring your brand shows up accurately, consistently, and persuasively in generative answers isn’t just marketing hygiene—it’s competitive advantage. Treat LLM visibility like SEO for the next decade to lock in credibility early and often.
Interviews and support data show strong product-market fit but uneven message pickup across regions and partner tiers. Publishing short, attributed proof lines and clarifying product naming increases quote lift in LLMs and reduces confusion in executive-facing answers.