AI Visibility Audit for SaaS · AI Discovery Intelligence
When a buyer asks ChatGPT, Claude, Gemini, or Perplexity for the best tool in your category, or how you compare to a competitor, the engine composes an answer from review sites and comparisons you do not control - and sometimes states your features and pricing wrong. The category shortlist that used to be a Google search is now a single composed answer.
28 Labs measures your share of AI recommendations for the category, comparison, and alternative queries that drive pipeline, ties it to your traffic, and hands your teams the moves to win it back.
4 engines · intent-scored query universe · every read confidence-tagged
Measured from the outside. No SDK in your rendering path. We do not measure training-data exposure - and we say so.
The queries that move pipeline
scored
A SaaS category has thousands of meaningful buyer queries. We score each by intent and track the shortlist, comparison, and alternative set that drives pipeline.
The shift
When a buyer asks an engine for the best tool, it composes a shortlist from sources you do not control - you do not know if you made it.
The category research that used to flow to your site now resolves in one answer; if you are not on it, pipeline softens and you backfill with paid.
We measure your share of AI recommendations across category, comparison, and alternative queries on all four engines, then tie it to your traffic and pipeline.
The board-level risk
A software company's moat is being the tool buyers shortlist, compare, and choose. Answer engines now sit in front of all three. The exposure is specific, and it is measurable.
"Best {category} software" is assembled from review sites and listicles the engine trusts - if you are absent there, you are absent in the answer.
"{you} vs {competitor}" gets answered from third-party comparison pages, framing you on someone else's terms.
"{competitor} alternatives" is a high-intent switching moment - and the engine decides who gets named.
Engines state your capabilities, integrations, and pricing - and sometimes get them wrong, costing deals before a rep is involved.
Every figure we put against these is anonymised and confidence-tagged. We show the pattern and the magnitude; we never expose another software company's numbers to make the point.
The query universe
A software category's buyers do not ask four questions. They ask thousands, across a journey. The work is not to track every random query. It is to score every query by commercial intent and monitor the high-intent ones that actually drive pipeline, mapped to where the buyer is in the decision.
Category, comparison, alternative, and pricing queries carry the pipeline; definitional research carries almost none. We weight what you track to where the money is - so the share number you read is the one that matters to the business, not a vanity average across the long tail.
The decision system
Most tools stop at "you were cited less." That is a metric, not a decision. We frame it as market share - the share of high-intent recommendations you hold against the competitor taking them - and we connect that share to the numbers your business already runs on.
How we keep it honest
No one can prove a single AI recommendation caused a single lead. Anyone who claims they can is selling certainty that does not exist. We treat it the way marketing-mix modeling treats a channel: we estimate the contribution from converging signals and we label the confidence of every read, so your analysts can audit any claim back to the evidence behind it.
When a read is directly observed in your own data, we say so. When it is inferred, we say so. When it rests on industry intelligence, we say that too. You never get a confident-sounding number with nothing under it.
Directly observed. The signal is present in your own logs, CRM, or AI-engine output. We saw it happen.
Inferred. Multiple independent signals converge on the same read, but no single source confirms it outright.
Industry intelligence. The read rests on category benchmarks and external patterns, not your own data. Treated as directional.
In your stack
This is not a deck that ages the day it ships. It is a live read wired into the systems you already run, so the share number and its business impact stay current as the engines re-crawl and your competitors move.
The integrated engagement connects to your logs, CRM, and paid feeds, so the AI-share read sits next to the traffic and pipeline data your teams already trust. No SDK in your rendering path.
You get a flag when your share on a high-intent query set moves sharply or a competitor surges, and a monthly read that tracks the trend and ties it to traffic and pipeline. Not once a quarter, on a slide.
HIGH, MED, or LOW on every finding, so the team acting on it knows exactly how much weight it carries before committing engineering or budget against it.
Honest about the tiers. The live read on how much of your traffic and pipeline movement is AI needs your first-party data - your logs, CRM, and paid feeds. That is the integrated, enterprise engagement, not a free snapshot. A standalone audit measures your share of AI recommendations across the four engines from the outside and shows where you are losing ground and to whom. The traffic-and-pipeline loop comes when we wire into your systems.
What winning takes
A software company wins when it becomes the source the engines trust to answer the category. That is structural. More blog posts will not do it. Being the authoritative, reachable source for the data buyers actually ask about will.
We diagnose where the engines already trust you, where they trust a competitor instead, and the specific moves that shift that trust toward you. The output is a build plan your teams can act on, not a list of keywords.
Start here
A 60-minute working session. We walk a sample audit, show you how the share read and the traffic-and-pipeline loop would shape up for your category, and scope a live run.
No obligation. We do not measure training-data exposure, and we tell you what we can and cannot see before you commit.