Buyers increasingly ask ChatGPT, Claude, or Perplexity for product recommendations before they ever hit Google. AI Monitoring tracks where your brand appears in those LLM responses — for which prompts, against which competitors, with what recommendation strength — and helps you steer the answer over time.
What it monitors
How often LLMs recommend your brand for relevant buyer prompts.
What competitors get recommended alongside you (or instead of you).
What facts and positioning the LLM uses when it mentions you.
How the answer changes over time as your content and presence change.
Core concepts
Personas
A persona is a simulated buyer with a job role, industry, and goals. Queries you run against an LLM are framed as that persona asking. Personas make the queries realistic, and let you separate what does a CMO see when they ask from what does a developer see.
Monitors
A monitor combines a persona, a set of queries, target LLMs, and a schedule. You can create separate monitors per product line, region, or buyer segment.
Queries
Queries are the prompts the monitor runs. You can write your own (best cold outreach platform for agencies) or generate them — Artemis can draft 20 to 50 realistic buyer queries for a persona at once.
Responses and recommendation analysis
The monitor records each LLM response and breaks it down: which brands were recommended, what position they appeared in, what reasons the LLM cited, and what facts it used. Recommendation strength (1 to 5) captures whether you were the #1 pick or one of several alternatives.
Setting up your first monitor
Open AI Monitoring → + New monitor.
Pick or create a persona.
Generate or write 10 to 30 queries.
Pick which LLMs to query: GPT-5, Claude 4.5, Gemini, Perplexity, and others.
Set a schedule (daily for high-priority monitors, weekly for the long tail).
Save. The first run happens immediately; ongoing runs follow the schedule.
Reading the dashboard
Visibility rate: % of queries where your brand was mentioned.
Recommendation rank: average position among recommended brands.
Sentiment: positive, neutral, or critical mentions.
Competitor co-occurrence: which competitors are recommended alongside you most often.
Fact accuracy: per-fact tracking of what LLMs say about you (price, features, founding year) — useful for catching wrong information at the source.
Recommendation analysis
For each query, the analysis explains why the LLM recommended what it did — which content it cited, what positioning it used, where the wrong info came from. Use this to:
Identify pages we should publish to influence future answers.
Find places to correct misinformation (your G2 page, Wikipedia, a partner site).
Steer the LLM toward your real differentiation.
Improving your AI visibility
This is GEO (Generative Engine Optimization) — the AI analog to SEO. Practical levers:
Publish concrete, fact-dense content on the queries you want to win.
Get cited on third-party authority sites (G2, Capterra, Reddit comparison threads).
Maintain clean structured data on your site.
Keep your Wikipedia and crunchbase profiles accurate.
Plan availability
Pro: 2 monitors, 50 queries per month per monitor.
Growth: 10 monitors, higher query budget.
Agency: pooled monitors across workspaces.
Free and Starter plans get a small preview budget so you can see what one query looks like before upgrading.