Lens by IgniteStack Our AI Visibility Framework

Lens is IgniteStack's evidence-led framework for understanding how AI platforms discover, describe, cite and recommend brands. We connect commercial prompt data, competitor analysis, brand perception and source intelligence to clear priorities for action.
01
Define the commercial Prompt Universe
Build evidence-led prompts using customer personas, buying stages, search demand, category language and real client data.
02
Measure visibility and brand perception
Benchmark brand inclusion, recommendation position, share of voice and how AI describes each brand across platforms and competitors.
03
Uncover source and authority gaps
Analyse cited pages, independent publishers, reviews and competitor evidence to pinpoint missing signals and opportunities.
04
Prioritise, deliver and remeasure
Translate findings into technical, content and digital PR actions, then track change against a consistent prompt baseline.
AI VISIBILITY INTELLIGENCE

See where your brand stands in AI search

  • Track the customer questions shaping discovery and purchase.
  • Monitor visibility across every major AI search platform.
  • Benchmark performance against the brands competing for attention.
  • Measure share of voice, recommendation rate, position and citations.
  • Understand how AI represents your brand and the sentiment shaping buyer perception.

A connected intelligence system for AI visibility growth

Prompt & Platform Intelligence
Our Prompt Universe maps customer questions, platform visibility and competitor performance across the entire buying journey.
Competitive Gap Intelligence
Our system analyses competitor content, citations and authority signals to reveal where your brand has the greatest opportunity to grow.

Lens Framework FAQs

AI-generated answers can change by platform, prompt wording, location, context, retrieval behaviour and model updates. IgniteStack therefore measures patterns across a consistent Prompt Universe rather than treating individual screenshots as fixed rankings.
Why do you use a fixed Prompt Universe?

A consistent Prompt Universe supports comparison over time. Report the prompt set, platforms, test dates and number of responses alongside each result. Changes in location, model or sampling can affect comparability. A percentage without its denominator does not show the size of the evidence base.

How do you choose which prompts to track?

We prioritise questions connected to real commercial journeys including discovery, research, comparison, validation and purchase. Branded prompts are used selectively because they can artificially inflate visibility.

How many AI platforms should a brand measure?

It depends on the audience and category. IgniteStack can analyse major platforms including ChatGPT, Google AI, Gemini, Claude, Perplexity and Copilot. We prioritise the platforms most relevant to the customer journey rather than treating every platform equally.

Why can AI visibility change between tests?

Generative outputs are probabilistic and can vary because of model updates, retrieval changes, location, context and newly available information. That is why we look for repeated patterns across a broader sample.

Is citation rate the same as recommendation visibility?

No. A mention names the brand. A recommendation presents it as a relevant choice. An owned citation links to the brand’s website. At response level, mention rate is responses naming the brand divided by eligible responses tested. For example, 20 of 100 responses means 20% mention rate, not 20% recommendations. This example is illustrative.

How do you identify why a competitor is more visible?

We compare content, entity signals, technical foundations and cited sources around the brands being recommended. This identifies evidence gaps and priorities to investigate. Observed differences do not establish which factor caused a recommendation.

How often should AI visibility be remeasured?

For active programmes, regular measurement is more useful than one-off audits. The right frequency depends on the category and pace of implementation but the underlying methodology should remain consistent.

Can AI visibility be connected to revenue?

It can be connected to commercial indicators such as referral traffic, leads, sales and branded demand where the underlying data allows it. We avoid attributing revenue to AI visibility alone when other marketing activity is also contributing.