Soxoa research · 2026 benchmark
What 25,220 AI answers and 1,253 buying signals reveal
Two products built and operated by Soxoa measure business signals that are usually difficult to see: who AI recommends and when companies show a reason to buy.
Data snapshot · Published · Revised
The two corpora
Separate datasets, one operating question
Where is a commercially useful signal hiding, and what evidence would make it actionable? These snapshots answer that question from two different points in the customer journey.
MentionedOn · AI visibility
25,220
AI answers
1,261 local leaderboards · 8,299 businesses · 21 trades · 40 U.S. cities
Intakra · B2B buying signals
1,253
market signals
806 watched companies · 459 companies with a signal · 129 with 2+ signal types
Finding 01 · AI visibility
AI recommendations remain fragmented
MentionedOn collected 25,220 answers and organized them into 1,261 local leaderboards spanning 21 trades and 40 U.S. cities. The corpus contained 8,299 distinct businesses. On average, the leading business in a leaderboard appeared in just 24% of its sampled answers, while 91% of businesses appeared in only one city.
The practical lesson is not that one company “wins AI.” Recommendations vary by category, geography, model, and prompt. A business needs market-level measurement, source evidence, and repeated checks—not a single vanity query.
Source: MentionedOn State of AI Search. The sample measures the prompts, markets, and collection period described in that report; it is not a census of all AI answers.
Finding 02 · Sales timing
Buying intent becomes more useful when signals stack
Intakra recorded 1,253 public market signals across 806 watched companies. Of the 459 companies with at least one signal, 129 showed two or more signal types—28.1%. Hiring, funding, and product-launch activity accounted for 970 signals, or 77.4% of the corpus.
A public event is a reason to investigate, not proof that a company will buy. Multiple relevant signal types can give a seller a more specific, source-cited reason to act now, but frequency alone does not establish purchase intent or conversion probability.
Source: Intakra State of Buying Signals. The report documents the categories, sources, and snapshot limits behind these totals.
From signal to action
Use the right evidence at the right operating moment
| Operating question | Evidence to capture | Action | Soxoa product |
|---|---|---|---|
| Does AI recommend us? | Market, prompt, model, position, cited sources | Strengthen the sources and gaps tied to a specific market | MentionedOn |
| Why contact this account now? | Relevant public event, source, date, signal type | Prioritize the account and ground the opening line | Intakra |
| Where is document work stuck? | Document type, fields, exception rate, handoff | Automate extraction and route exceptions for review | Soxoa document tools |
Read the limits
What these findings do not prove
- The two corpora do not share a denominator and cannot be combined into a single market statistic.
- MentionedOn measures its disclosed prompts, models, trades, cities, and collection windows—not every possible AI recommendation.
- Intakra records public events that may justify research or outreach; an event does not prove a company intends to purchase.
- All figures are a dated snapshot. Follow the source reports for current totals and full methodologies.
Soxoa operating model
Turn an invisible process into a measurable system
01
Define the event
Name the decision or operational change that matters.
02
Capture evidence
Record the source, timing, state, and context needed to trust it.
03
Automate the action
Route the evidence into a specific workflow with human review where needed.
04
Measure the result
Track whether the system changed time, quality, risk, or revenue.