AI Recommendation Intelligence
Is AI recommending your business?
Your customers are asking ChatGPT, Gemini, Claude, Perplexity and Google AI who they should hire.
Greenbug.ai measures whether your business appears in those answers, and shows which competitors are being recommended instead and what sources support them.
Then we help strengthen those signals — and run the same measurement again to see what actually changed.
Measure it. Improve it. Prove it.
How it works
Repeats every cycle
- Customer question“Who would you recommend?”
- AI recommendationA handful of businesses get named
- Greenbug measuresAre you one of them, and where
- Greenbug finds gapsWho appears instead, and why
- Business improvesSignals, sources, entity, content
- Greenbug measures againDid any of it actually change
Then back to the top, with the identical benchmark.
The shift
Search is changing.
For years, businesses competed for a position on a page of links. Now customers increasingly ask an assistant directly — and an AI answer usually returns a handful of businesses, not ten. Either you are one of them or you are not considered.
Traditional search
- Customer types a query
- Ten or more links returnedEvery business on page one gets a chance to be considered
- Customer researches and compares
- Customer chooses
The question this answers: where do I rank?
AI-assisted search
- Customer asks a question in their own words
- AI evaluates the information available to it
- A small set of businesses is recommendedOften two or three names, with a reason attached
- Customer contacts one of them
The question this answers: does AI recommend me?
The new question isn’t only “where do I rank?” It is “does AI recommend me?”
What Greenbug measures
Stop guessing about AI visibility.
Actual recommendation behaviour — what AI systems did when asked, recorded and counted, not estimated.
AI presence
How often your business appears at all when customers ask relevant questions.
Top recommendations
How often you are named first, and how often you are in the first three.
Competitor visibility
Which competitors appear in the answers where you do not.
Citations and source signals
Which websites and sources the answer leaned on when it decided.
Accuracy
Whether AI describes your services, locations and expertise correctly.
Share of voice
How much of the recommendation landscape you hold against competitors.
AI Source Gap Analysis
Find out why your competitors are getting recommended.
When AI recommends three businesses and none of them is you, there is usually a reason in the evidence. We collect the sources cited across the measured answers, work out which ones repeatedly support the businesses being recommended, and compare that against where your business actually appears.
Who appears instead
The competitors named in the answers you lost, prompt by prompt.
Which sources support them
Directories, association listings, local press, review platforms, industry publications.
Which authority signals they have
What corroborates them repeatedly across independent sources.
Where you are missing
The same list checked against your business. That gap is the plan.
Report structure — source types shown, not measured results
| Source | Competitors present | You | Relevance | Recommended action |
|---|---|---|---|---|
| Regional trade association directory | 3 of 3 | Absent | High | Join and complete the public listing |
| County-level local news | 2 of 3 | Absent | High | Earn coverage; cannot be purchased |
| Major review platform profile | 3 of 3 | Present | High | Present — correct services and service area |
| Industry certification register | 2 of 3 | Absent | Medium | Verify credentials are publicly listed |
| General-purpose business directory | 3 of 3 | Absent | Low | Deprioritise — low observed influence |
Instead of blindly creating content, you get a ranked list of specific sources where the businesses AI already trusts are present and you are not.
The Greenbug loop
Measure → Diagnose → Improve → Measure again
Most marketing reports tell you what was done. Greenbug measures whether the outcome changed.
Measure current AI visibility
A frozen set of buyer-intent questions, run across the benchmark platforms, every answer recorded in full.
Identify competitors and source gaps
Who was recommended instead of you, and which third-party sources those answers relied on.
Improve website, entity, content and authority signals
Work chosen from what the measurement found, not from a standard checklist.
Run the same benchmark again
Identical prompts, identical platforms, identical geography. Nothing about the test changed.
Track whether AI visibility actually improved
Movement is attributable. No movement is reported as no movement.
Step 5 returns to step 1. That is the whole product.
We don't just tell you what we changed. We measure whether AI visibility changed.
The AI Recommendation Score
Know where you stand.
One number, from 0 to 100, built from six measured components: 24 fixed buyer-intent prompts across 5 platforms, each run 2 times — 240 recorded observations per cycle.
It reflects observed AI behaviour only. Technical work we complete is tracked separately and can never inflate this number.
Worked example — not a measurement of any business
42.8
/ 100
EstablishedMeaningful visibility but not dominant.
How the number is built
| Component | Rate | Weight | Points |
|---|---|---|---|
| Presence Rate | 50% | 40% | 20 |
| Top-1 Recommendation Rate | 12.5% | 20% | 2.5 |
| Top-3 Recommendation Rate | 25% | 10% | 2.5 |
| Citation Rate | 25% | 15% | 3.8 |
| Accuracy Rate | 100% | 10% | 10 |
| Positive Recommendation Rate | 80% | 5% | 4 |
| Total | 42.8 | ||
Based on a business mentioned in 120 of 240 observations, ranked first in 30, in the top three in 60, cited in 60, factually correct in every checkable mention, and framed positively in 96 of its 120 mentions.
Research and proof
We don’t just talk about AI. We measure it.
Original measurement is the only thing that makes a company in this category worth citing. Here is what is underway, with its honest current status.
No published measurements yet
Greenbug.ai, built in public
We run the same benchmark on our own brand and publish the result, including when it is unflattering.
Follow the experiment →
DEL Trees
A brand-new tree services division with no website and no history — a chance to measure AI visibility from the beginning.
DMV AI Recommendation Index
Which businesses AI actually recommends across DMV categories. Placement cannot be purchased.
See the index →
Further studies
Citation studies, platform comparisons, industry benchmarks and the annual State of DMV AI Recommendations.
The research programme →
Who we help
Built for businesses that depend on being chosen.
The methodology is industry-neutral; the prompt library is not. Every benchmark is built from the language buyers in a specific category actually use.
- Home services
- Healthcare and dental
- Professional services
- Personal brands and coaching
- Consumer and hospitality
A fair question
Is AI visibility just SEO?
Not exactly. Strong SEO remains a foundation, because AI systems rely on crawlable web information. But recommendation visibility also depends on entity clarity, third-party authority, citation ecosystems, reviews and consistent facts — none of which a ranking report measures.
Greenbug does not replace good SEO. We build beyond it.
| Traditional SEO measures | AI visibility measures |
|---|---|
| Search rankings | Recommendations |
| Keywords | Buyer questions |
| Clicks | Mentions |
| Organic traffic | Citations |
| Position in a list | Position in an answer |
| — | Factual accuracy |
| — | Share of voice |
| — | Entity authority |
Do you know what AI says about your business?
We'll establish your baseline, show which competitors appear, and identify where your biggest visibility gaps are.