AI visibility for SaaS companies

Software buying now starts with a question, not a search page. "What is the best help-desk tool for a 20-person team?" "Alternatives to [incumbent] that integrate with HubSpot?" "Compare these two analytics platforms on pricing and security." The engine answers with named products, short reasons, and sometimes a comparison table.

Those answers are assembled from your documentation, your pricing and integration pages, review platforms, comparison and "alternatives" articles, changelogs, community threads and the bylined content your team publishes. The engine is effectively building its own buyer's guide on the fly, and your product is either in it or it is not.

This matters most at the long tail of specific, intent-rich questions. A buyer who asks a precise question is close to a decision. If your product is named for "SOC 2 compliant tool that does X for fintech" you have reached them at exactly the right moment. Read what is GEO for how this differs from classic search.

Three dimensions decide whether your product is named and recommended, and Known & Cited scores all three.

• AI Visibility. Are you surfaced for the category and use-case questions your buyers actually ask, and for "alternatives to [competitor]" prompts? A tool can have great product-market fit and still be a Ghost in the answers because nothing the engine reads connects you to those queries.
• Source Quality. AI weights structured documentation, third-party review platforms, comparison sites and clear integration and security pages. A product whose strongest signal is its own marketing site will be outweighed by a competitor with strong independent coverage.
• Narrative Fit. Does the AI describe what you actually do, for the buyer you actually want? Many SaaS products are summarised by the engine using an old category label or a feature they have outgrown. That mis-framing costs deals before a human is involved.

Knowing which of the three is holding you back stops you pouring budget into more content when the real problem is that no review platform or comparison source describes you correctly.

We test your product across ChatGPT, Google AI Overviews and Perplexity, the three engines where software buyers now do their early research. We build a SaaS-specific prompt set: category questions, use-case questions, integration and compliance questions, and the head-to-head "alternatives to" and "X versus Y" prompts that shape a shortlist.

Each answer is scored 0 to 100 and placed in one of five bands: Ghost (0 to 10), Whisper (11 to 30), Emerging (31 to 50), Cited (51 to 75) and Known and Cited (76 to 100). You see how you band per engine and per use case, and how you fare in direct comparison prompts against named competitors.

The score is built on our 12-pillar framework, weighted across AI Visibility (40%), Source Quality (30%) and Narrative Fit (30%). The detail of how we design prompts, capture citations and verify them sits on our methodology page, and the AI Visibility Score page explains the bands in full.

You start with a free Exec Brief. We run a focused SaaS prompt set across the three engines, score where your product stands today, and show you the comparison prompts where competitors are winning the shortlist and you are not. It is free and there is no commitment. Request one at start.

From there a full AI Visibility Strategy runs on an Annual, Bi-Annual or Quarterly cadence. SaaS markets move quickly and competitors ship and reposition constantly, so many software companies choose a tighter cadence. Pricing is bespoke and depends on how many use cases, competitors and engines you want tracked.

You get the scored report, the exact prompts and citations behind it, a competitor comparison view, and a clear list of what to change to climb the bands, from documentation to review-platform presence to how your category is described. Then we re-measure so you can watch the score move release after release. Be Known. Be Cited.