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An experiment is the core unit of measurement in XLR8.ai. Each experiment defines what you’re tracking — your brand, the type of analysis, the queries to test, and the AI models to run against. You run experiments repeatedly over time to see how your AI visibility and brand sentiment change.

Query Generation

Build reusable query sets — company briefing, categories, and queries — then pick a set when you create an experiment.

Visibility experiments

Measure how often your brand appears in AI-generated responses across leading models.

Sentiment experiments

Understand how AI models describe and perceive your brand in their responses.

Reading results

Interpret mention rates, sentiment scores, citations, and AI-generated analysis.

Analytics

Compare executions over time with trends, baselines, and source concentration.

Scheduling

Automate recurring experiment runs so your data stays current without manual effort.

Visibility vs. sentiment — which should you use?

Choose a Visibility Analysis experiment when you want to measure whether your brand is being mentioned in AI responses at all.Visibility answers questions like:
  • “Does Perplexity mention us when users search for our category?”
  • “How often do we appear vs. our competitors?”
  • “Is our website being cited as a source?”
Visibility results show mention rates per model, citation presence, and competitor comparison — all broken down by query category.Best for: Brands establishing an AI presence baseline, competitive benchmarking, and tracking the impact of content changes over time.

Creating a new experiment

Company details, experiment type, and queries now live on a query set. Create or edit those in Query Generation first. Creating an experiment then has two steps: Query set and Create.
1

Create a query set (if you do not have one)

From the sidebar under Strategy, click Query Generation, then New query set. Enter company details, analyze the domain, review categories, aliases, and tracked competitors, generate or import queries, and Save query set.See Query Generation for the full walkthrough, including CSV import, Keyword Planner, and comments.
2

Open New Experiment

From the sidebar under Strategy, click Experiments. Then click New Experiment.
3

Step 1 — Query set

Pick a saved query set that already has queries. Experiment type, company, domain, aliases, and tracked competitors come from that set — you will not enter them again.The selector shows each set’s name, company, category count, and query count. After you select one, a summary shows company, domain, type/subtype, aliases, tracked competitors, and query counts. Use Edit to open the set in Query Generation, or Manage query sets to go to the list.If you have no saved sets with queries yet, create one in Query Generation first.Click Continue.
4

Step 2 — Create

Confirm the Experiment Summary — including company aliases and tracked competitors when the query set has them. Those names are saved on the experiment for competitor mention tracking. Then configure:
  • Model Selection — choose which AI models to test. If you select GPT Instant or GPT Thinking, an Enforce Web Search switch appears. New experiments start with this on, so those models search the web before answering unless you turn it off.
  • Location Information (optional) — country, region/state, and city for geo-targeted answers
Click Create Experiment. XLR8.ai saves the configuration, opens the experiment detail page, and starts the first execution automatically.

Managing your experiments

Filtering and sorting

On the Experiments page, search experiments and filter by type, subtype, or archival status. Sort by creation date or name.

Pinning

Pin important experiments to keep them at the top of your list. Click the pin icon on any experiment card to toggle the pinned state.

Tags

Add tags to categorize and filter your work. Tags are editable from the experiment detail view.

Archiving

Archive experiments you no longer actively track. Archived experiments are hidden from the default view but remain accessible via the archived filter. Archiving does not delete any data.

Rerunning and scheduling

From an experiment detail page you can:
  • Rerun Experiment — start a new execution with the current configuration
  • Schedule — set a recurring cadence (see Scheduling)

Experiment settings

Open Settings on an experiment to manage:
  • Web search — organization admins can toggle Enforce web search. When on, supported GPT models search the web before responding. New experiments start with this on. The change saves immediately and applies to future runs, not past executions.
  • Company aliases — copied from the query set; you can still edit them here
  • Competitors — pre-filled from the query set’s tracked competitors; edit names and aliases here
  • Tracked URLs — or add the same URLs to several experiments from Settings → Tracked URLs
  • Queries and categories
  • Outreach excluded domains (visibility experiments)

Deleting

Delete an experiment permanently from the experiment detail view. This removes the experiment and all of its executions and results.
Deleting an experiment is irreversible. All execution history and results are permanently removed. Archive the experiment instead if you may need the data later.

What’s next?

Query Generation

Build and edit the query sets you pick when creating an experiment.

Visibility experiments

Configure tracked URLs, company aliases, geo-targeting, and model selection for visibility.

Sentiment experiments

Understand sentiment subtypes, action item sync, and how to interpret sentiment scores.

Analytics

Track how visibility and sentiment change across executions.

Scheduling

Set up automatic recurring executions to track changes over time.