Test what moves AI visibility

Run controlled experiments on your GEO strategy. Siftly splits your tracked topics into a test group and a control group, then tracks how visibility and citation metrics diverge over time. You see which changes actually move the needle.

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Proof, not guesses

Turn GEO from guesswork into a measurable channel

Siftly splits your tracked topics into a balanced test group and control group, then tracks how visibility and citations diverge. You see which changes actually move the needle.

Divide topics into balanced groups

Siftly clusters your tracked topics by citation behavior and splits them into a test set and a control set. With balanced splits, a difference in outcome reflects your intervention, not uneven groups.

Watch the two groups diverge

Compare visibility % and citation counts for test vs control over time. A widening gap after you ship a content change is the signal that the change is working.

Compounding wins

Scale what works, skip what doesn't

Every experiment you run becomes reusable knowledge, win or loss. Roll the winners out to new topics and retire what didn’t move the metric.

A library of every change you’ve tested

Every experiment becomes part of your team’s playbook, winners and losers alike. Reuse winning patterns across pages; retire tactics that didn’t move the needle.

You call the winner, not a black box

Siftly surfaces the raw comparison: test vs control, visibility, citations, the trend. When the gap is clear, you mark the winning variant. No opaque verdicts from a model you can’t inspect.

Why it matters

Why experimentation is the missing piece

AI visibility experimentation is the practice of running controlled tests: split your tracked topics into a test group and a control group, change only the test group, and compare how visibility and citation metrics move between the two over time. Without a control, any change could be noise from a model update, a competitor launch, or plain topic volatility.

Monitoring shows where you stand, not what works

Most AI visibility tools stop at monitoring. They tell you where you rank, but not what to do about it. Experimentation adds the before/after structure that turns monitoring into a system for improvement.

A control group separates signal from noise

If your AI mention rate rises 10% after a change, that means nothing on its own. Maybe the whole category moved 10%. If your test group rises 10% and control stays flat, the change did the work.

How it works

How test & control splits work

From clustering to a called winner: Siftly builds balanced groups, holds the control steady, and measures the gap your change opens up.

Cluster your topics

Siftly analyzes citation patterns across your tracked topics and uses hierarchical clustering to group similar topics together based on which sources AI engines cite for each one.

Balance the split

Topics are divided into a test set and a control set so both groups have comparable baseline visibility, citation counts, and topic coverage. Split-quality scores tell you how well-matched the two groups are before you start.

Freeze the control

Leave the content supporting your control topics unchanged. Control is your “what would have happened” baseline. It captures background movement you didn’t cause.

Ship the change on test

Implement your intervention (new content, schema additions, restructuring, freshness updates) only for pages that answer test-group topics.

Track the divergence

Siftly records visibility % and citation counts for both groups daily and renders them side-by-side, so you can see the gap grow, or not.

Call the winner

When the trend between test and control is clear and holds over a multi-week window, mark the winning variant. The experiment stays in your library for reference and reuse.

Experiment types

Experiment types that drive results

Different interventions move AI visibility in different ways. They also take different amounts of time to show a clear signal.

Experiment Type	What You Change	Typical Impact	Time to Clear Signal
Data enrichment	Add original statistics, benchmarks, or survey results	High: unique data is the strongest citation driver	2-3 weeks
Structural optimization	Reformat with GEO patterns (definitions, tables, ordered lists)	Medium: improves AI parseability	2-4 weeks
Schema addition	Add FAQ, HowTo, Speakable, or Article schema	Medium: explicit signals for AI crawlers	3-4 weeks
Content expansion	Add new sections covering subtopics competitors miss	Medium-high: improves topical authority	3-4 weeks
Freshness update	Replace outdated stats with current data, update date	Medium: AI prefers fresh content	1-2 weeks (for real-time platforms)
New page creation	Publish entirely new content targeting a visibility gap	Variable: depends on topic competition	4-6 weeks

Reading results

Reading experiment results

Every experiment surfaces the same core numbers side-by-side for test vs control. The signal isn’t a single verdict. It’s a widening, directional gap that holds across a multi-week window. When the two lines clearly diverge and the gap is stable, the change worked. One experiment per cluster at a time keeps the result attributable.

Visibility %

Share of responses mentioning your brand

Citations

URLs cited from your site

Δ vs Control

Raw gap between test and control

Trend

Direction and steepness over time

Getting started

Time to value, not time to configure

Go from a balanced split to a clear test-vs-control gap in weeks. Setup doesn’t take weeks.

Week 1

Set up the split

Pick the topics you're testing. Siftly clusters them into balanced test and control groups using citation-based similarity so the two groups start from comparable baselines.

Week 2

Ship the change

Implement your content change (new pages, schema, restructuring, freshness updates) on the pages that answer your test-group topics. Leave control-group topics untouched.

Week 3-4

Read the gap

Watch visibility and citation metrics for test vs control diverge. When the gap is clear and stable, mark the winning variant and roll the change out to other topics.

FAQ

You split your tracked topics into a test group and a control group. You leave the control group unchanged and ship a specific content change that affects only the test group. Siftly tracks visibility % and citation counts for both groups over time, showing you whether the change actually moved the needle relative to what would have happened anyway.

Common experiments include: adding original data or statistics to a page, restructuring content with GEO formatting (tables, definitions, ordered lists), adding or updating schema markup, rewriting page titles or meta descriptions, publishing new competitor comparison pages, and refreshing outdated statistics with current data.

Most AI visibility tools only monitor. They show you where you stand but not what works. Siftly adds a before/after structure: balanced test and control groups built from citation-based clustering, consistent daily measurement across both, and a historical library of every change you've tested and whether it moved the metric.

Most experiments need 2-4 weeks before the gap between test and control is clear enough to act on. The more sensitive the topics (frequent queries, fast-changing AI models), the sooner you'll see a trend. Siftly shows the daily and weekly trajectory so you can judge when the signal is strong and stable.

In website A/B testing, you show different page versions to different users at the same time. In AI visibility experiments, you change content permanently for one set of topics (the test group) and leave other topics untouched (the control group). Then you compare how visibility and citation metrics move for the two groups over time.

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