Sill vs SearchPilot: GEO Experimentation Compared
Read the postDateApr 5, 2026
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SplitSignal and Sill both use Bayesian statistical methods, but they apply them to fundamentally different problems. SplitSignal, built inside the Semrush ecosystem, uses Google's CausalImpact methodology to forecast what organic traffic would have been without an on-site change. Sill models the probability of AI citation shifts across six platforms after a content intervention. SplitSignal has no AI or GEO testing capability. Semrush's separate AI Search monitoring tab ($99/mo add-on) tracks AI mentions but is not connected to SplitSignal's experimentation engine. This comparison breaks down the methodology, requirements, and fit for each.
TL;DR
SplitSignal is Semrush's SEO A/B testing product, built on Google's CausalImpact methodology: a Bayesian structural time-series model using 100 days of historical click data to forecast expected organic traffic. Client-side JavaScript implementation. Minimum requirements: 300+ pages and 100,000+ clicks. Custom enterprise pricing, separate from Semrush subscriptions. SplitSignal has no AI or GEO testing capability. Semrush's AI Search monitoring tab ($99/mo add-on) tracks brand mentions across AI engines but is observation-only and is not connected to SplitSignal's experimentation engine. Sill measures AI citation shifts across six platforms (ChatGPT, Perplexity, Gemini, Google AI Overviews, Claude, Grok) using Bayesian estimation with statistical controls. Prompts affected by a content change serve as the treatment group; unaffected prompts serve as within-brand controls. Per-platform models surface divergence rather than averaging across engines. Built-in calibration establishes empirical false positive rates. Minimum: 25 prompts. Pricing: Free (5 prompts) / $90/mo Basic (120 prompts) / $225/mo Pro (360 prompts) / Enterprise (custom). Both tools use Bayesian methods but for different problems: SplitSignal forecasts organic traffic on page groups; Sill models citation probability across AI platforms. SplitSignal for large-traffic SEO testing inside Semrush; Sill for direct AI visibility experimentation. The tools are not substitutes; they address different layers of the discovery funnel.
SplitSignal tests SEO changes on organic traffic using CausalImpact inside Semrush; Sill tests GEO changes on AI citations across six platforms.
SplitSignal is Semrush's SEO A/B testing product. It uses Google's CausalImpact methodology: a Bayesian structural time-series model that uses 100 days of historical click data to construct a synthetic control group and forecast expected performance. This is a well-validated statistical approach published by Google Research and widely used in econometrics. The implementation uses client-side JavaScript to apply changes, which is simpler to set up than server-side approaches but less robust for tests that depend on how crawlers render pages. Minimum requirements: 300+ pages and 100,000+ clicks over the prior 100 days. Pricing is custom and enterprise-tier, separate from standard Semrush subscriptions.
Sill is a GEO experimentation platform that measures how content changes affect AI citations across ChatGPT, Perplexity, Gemini, Google AI Overviews, Claude, and Grok. Instead of forecasting organic traffic, Sill models citation probability shifts using a Bayesian estimation approach with statistical controls. Prompts affected by a content change serve as the treatment group; unaffected prompts serve as controls. The system fits models independently per AI platform and uses calibration to establish empirical false positive rates. Minimum requirement: 25 prompts. Pricing: Free (5 prompts), $90/mo Basic (120 prompts), $225/mo Pro (360 prompts), Enterprise (custom). All plans include all 6 AI platforms.
The core distinction: SplitSignal answers "did this page change affect organic traffic?" Sill answers "following this content change, did AI platforms mention our brand more or less frequently?" These are different questions directed at different channels.
SplitSignal forecasts organic traffic on page groups using CausalImpact time-series; Sill models citation probability across AI platforms with statistical controls.
Both tools apply Bayesian inference, but the structure of the problems they solve is different. SplitSignal uses CausalImpact to build a time-series forecast: given 100 days of historical click data, what would traffic on these pages have been without the intervention? The synthetic control is constructed from the behavior of untreated page groups during the same period. This works well when you have large, stable traffic volumes on groups of structurally similar pages.
Sill faces a different statistical challenge. AI outputs change 63% of the time between consecutive days, and 91.6% of cited URLs appear on only one platform. The unit of measurement is not page traffic but entity citation: whether and how prominently a brand is mentioned in an AI response. Sill models this per platform rather than averaging across them, because a change that moves ChatGPT may leave Gemini unchanged. Built-in calibration establishes the empirical false positive rate so confidence levels have a measured basis.
SplitSignal has no AI or GEO visibility testing capability. Semrush has added an "AI Search" monitoring tab ($99/mo add-on) that tracks brand mentions across AI engines, but this is observation-only and is not connected to SplitSignal's experimentation functionality. The two products exist in parallel without integration. A team using both would need to manually correlate SplitSignal test results with Semrush AI Search observations, reintroducing the before-and-after problem that experimentation is designed to solve.
SplitSignal requires 100K+ clicks and 300+ pages for SEO testing; Sill requires 25+ prompts for GEO testing across 6 AI platforms.
| Dimension | SplitSignal | Sill |
|---|---|---|
| Target | Organic traffic on page groups | AI citations across 6 platforms |
| Bayesian approach | CausalImpact: Bayesian structural time-series forecast | Bayesian estimation with statistical controls |
| Implementation | Client-side JavaScript on your site | No site changes required |
| Minimum requirements | 100K+ clicks over 100 days; 300+ pages | 25+ prompts |
| AI platforms | None (no AI/GEO capability) | ChatGPT, Perplexity, Gemini, AI Overviews, Claude, Grok |
| Ecosystem | Semrush (keyword, competitive, backlink data) | Standalone; GEO monitoring, recommendations, Brand Watchdog |
| Pricing | Custom enterprise (separate from Semrush subscription) | Free (5 prompts) / $90/mo / $225/mo / Enterprise |
| AI monitoring add-on | Semrush AI Search ($99/mo); not connected to SplitSignal | Included in all plans; integrated with experimentation |
SplitSignal fits large-traffic SEO testing inside Semrush; Sill fits direct AI visibility experimentation for brands of any size.
Use SplitSignal if you are already invested in the Semrush ecosystem and need to test on-site SEO changes on a site with 100,000+ clicks and 300+ template pages. SplitSignal's strength is that it layers causal SEO testing into an existing workflow without adopting a separate platform. The CausalImpact methodology is well-validated and the integration with Semrush's keyword and competitive data provides context that standalone tools lack.
Use Sill if you need to understand whether content changes are affecting how AI platforms mention and recommend your brand. Sill's minimum of 25 prompts (compared to SplitSignal's 100K+ clicks) makes it accessible to brands that lack the traffic volume for traditional SEO split testing. The per-platform models surface divergence: a change that lifts ChatGPT citations may leave Gemini unchanged, and Sill reports that difference rather than averaging it away.
These tools are not substitutes. SplitSignal tests SEO; Sill tests GEO. A team running SplitSignal to measure the organic traffic impact of title tag changes could run Sill in parallel to measure whether the same changes affected AI citations. The results address different layers of the discovery funnel.
For a full comparison across all testing and monitoring tools in the space, see our guide to GEO testing and experimentation tools in 2026.
Sill measures AI citations across six platforms with statistical controls and per-platform models. Free tier includes monitoring, GEO recommendations, and Brand Watchdog. Experimentation starts at $90/mo.
Request your first analysis today to see where you stand.