/ OUR VISION
AI visibility should be evidence, not theater.
Recall helps teams understand which organizations AI recommends for consequential buyer questions, why they make the shortlist, what to change, and whether that work held up when measured again.
01 — THE SHIFT
The shortlist is moving into the answer
People increasingly ask an AI system to narrow complex choices before they visit a website, contact a vendor, choose a school, book a workspace, or evaluate an investment. Being accurately considered in that answer is becoming part of market access.
The answer is probabilistic and the evidence is fragmented. A single screenshot cannot reveal a trend, and a proprietary score cannot tell a team what created it. The category needs a measurement contract, not more certainty than the data can support.
02 — THE STANDARD
What a credible AEO product must do
01
Store the evidence
A percentage without its prompt, answer, citations, engine, locale, timestamp, and run outcome cannot be audited. Recall keeps those inputs attached to the metric.
02
Measure a distribution
AI answers vary. Recall samples the same high-intent questions over time and separates absence, refusal, and provider failure instead of turning one response into a ranking claim.
03
Connect findings to work
The useful output is not another dashboard. It is a ranked, evidence-backed change to a page, entity profile, source, claim, or comparison that a team can actually ship.
04
Compare like with like
Before-and-after measurement locks the prompt, engine, locale, scoring method, and sampling plan. Movement is reported with uncertainty, not sold as guaranteed causation.
03 — WHO IT SERVES
Markets where credibility changes the decision
Recall is built first for venture funds and their portfolios, deep-tech companies, coworking operators, and private education. These markets look different, but the buying behavior is similar: people ask AI to understand an unfamiliar category and reduce a high-consideration shortlist.
Each program begins with the real questions, evidence boundaries, competitors, geography, and decision criteria of that market. A venture fund can use the same operating model for its own founder-facing discoverability and as a repeatable portfolio service.
/ START WITH A BASELINE
Bring us the questions your buyers ask.
We will define a bounded pilot, capture the current shortlist and evidence, and agree how any shipped change will be measured before provider spend begins.