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VeluraHQ benchmarks your brand against every competitor in your workspace, measuring who gets mentioned, cited, and positioned favorably across the same AI prompts. Every time a prompt runs, VeluraHQ records the results for your brand and every competitor simultaneously — so you are always comparing apples to apples across the same questions, the same platforms, and the same time window. This turns AI visibility from a feeling into a measurable metric you can act on.

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Adding competitors

1

Go to AI Search Analytics > Competitors

Open AI Search Analytics from your workspace sidebar and select the Competitors tab.
2

Add a competitor

Click Add Competitor. Enter the brand name and the primary domain you want to track — for example, competitor.com. VeluraHQ uses the domain to identify cited URLs belonging to that brand in AI responses.
3

Repeat for each competitor

Add as many competitors as you need to benchmark against. There is no limit on the number of competitors in your workspace — track direct rivals, adjacent players, and aspirational benchmarks all at once.
4

Competitors are included in all future runs

Once added, VeluraHQ automatically includes each competitor in all subsequent prompt runs. You do not need to configure anything further — their mention counts, citation rates, and positions will appear alongside yours in every report.

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Share of voice

Share of voice measures your proportional presence in AI-generated answers relative to the entire competitive set tracked in your workspace. It is calculated as your brand’s total mention count divided by the combined mention count of all tracked brands across the same prompt runs. A share of voice of 31% means your brand appeared in 31% of all tracked AI responses that mentioned any brand in your competitive set — for every 100 times any tracked brand was mentioned, your brand accounted for 31 of those mentions. Share of voice is a more meaningful benchmark than raw mention counts because it accounts for the natural variation in how often AI engines mention brands in different topic areas. A category where AI engines rarely mention any brands will produce lower absolute mention counts for everyone — share of voice normalizes for that and shows you whether you are winning or losing relative to competitors regardless of how mention-heavy the topic is. Track your share of voice over time to see whether your content and optimization efforts are moving the needle in proportion to competitor activity.

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Citation analysis

Brand mentions and URL citations are different signals — and both matter. A mention means the AI referenced your brand by name; a citation means it linked to a specific page on your domain as a source. Citations carry more weight because they signal that the AI engine treated your content as authoritative enough to direct users to directly. VeluraHQ tracks citations at the URL level for every prompt run, so you can see:
  • Which of your pages are most frequently cited — your highest-performing content in the AI answer layer, useful for understanding what is already working and why
  • Which competitor pages are cited for prompts where you are absent — the specific URLs that are winning the citation you are not getting, giving you a direct content target
  • Citation rate trends over time — whether your overall citation rate is rising or falling, and which platform or topic category is driving the change
Use the citation analysis view to build a prioritized list of competitor pages that are consistently cited on high-value prompts. These are the pages worth studying closely for structure, depth, and the entity signals that may be driving AI citation.

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Sentiment analysis

AI engines do not just mention brands — they characterize them. The language an AI uses when describing your brand shapes how potential buyers perceive you, especially when they are in early research mode and treating AI answers as trusted summaries. VeluraHQ tracks the sentiment and framing associated with your brand versus competitors across prompt runs. You can view sentiment broken down by theme — for example:
  • Pricing — Is your brand described as affordable, premium, or enterprise-grade? How does that compare to how competitors are framed?
  • Ease of use — Are you positioned as intuitive and accessible, or technical and complex?
  • Enterprise suitability — Does the AI characterize your brand as appropriate for large teams or better suited to smaller ones?
Monitoring sentiment lets you catch reputation gaps early. If AI engines are consistently describing a competitor as the “enterprise standard” in your category while framing your brand as a smaller alternative, that is a positioning signal worth addressing in your content before it becomes entrenched in model outputs.

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Search terms discovery

When an AI engine generates an answer, it draws on an internal set of queries and associations to retrieve and rank information about each brand. VeluraHQ surfaces these internal queries — the terms the AI associates with your brand when formulating responses about you. These search terms reveal the entity map the AI has built around your brand. If the terms closely match your actual positioning, your content is doing its job. If the terms are off-target — reflecting outdated messaging, a narrow feature set, or a topic area you have since moved away from — that is a signal your content has an entity coverage gap. Use the search terms view to identify topics where your brand has strong associations and topics where coverage is thin. The gaps make a direct input to your content planning: publishing authoritative, structured content on under-covered topics helps AI engines build richer, more accurate associations with your brand over time.

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Turning gaps into actions

Identifying a gap is only useful if you act on it. When you find a prompt where competitors are cited and your brand is absent, VeluraHQ gives you a one-click path to create a content action directly from the benchmark view. The action carries the full context forward — which prompt triggered the gap, which competitors are winning it, and the gap score based on the volume and frequency of the competitive advantage. That evidence travels into your workflow queue so the team member picking up the action understands exactly what the opportunity is and why it matters, without needing to reconstruct the analysis from scratch. From the action, you can launch a content workflow, assign the task to a team member, or add it to a batch run for prioritization alongside other open actions. See Actions for more detail on how the workflow queue works.
Run competitor benchmarks on a weekly schedule rather than monthly. AI citation patterns can shift quickly — a competitor publishing a well-structured piece of content can go from absent to consistently cited within days of an AI model crawl. Weekly benchmarks give you enough lead time to respond before a visibility gap becomes a traffic gap.