Sill vs SearchPilot: GEO Experimentation Compared
Read the postDateApr 5, 2026
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SearchPilot published a case study where moving a flight search widget on Skyscanner cost 7% of organic traffic, measured at 95% confidence using server-side page splitting and a neural network forecast model. That is rigorous science applied to a well-defined problem: did this change affect how many people clicked through from Google? SearchPilot now offers what they call GEO A/B Testing, extending their page-split methodology to measure how on-site changes affect AI-influenced traffic. The question is whether page-level splitting, designed for a search engine that crawls and ranks individual URLs, can measure outcomes in an environment where AI engines synthesize answers from multiple sources, change their recommendations 63% of the time between consecutive days, and do not expose impression data. This guide compares every tool that claims to test or measure GEO impact, from enterprise SEO experimentation platforms to AI monitoring tools with before-and-after observation, and examines what each one actually measures versus what it claims to measure.
TL;DR
The GEO testing market splits into three categories: SEO experimentation platforms (SearchPilot, SplitSignal) that measure organic traffic with page-level splitting; GEO monitoring platforms (Otterly, Profound, BrightEdge, Conductor) that track AI mentions with before-and-after observation; and GEO experimentation (Sill) that measures direct AI citation shifts using statistical controls. SearchPilot is the gold standard for SEO A/B testing and now offers GEO testing that measures AI-influenced organic traffic on variant pages; their methodology does not query AI platforms directly to measure brand mention changes. SplitSignal uses Google's CausalImpact model but has no AI/GEO capability and requires 100K+ clicks. SEOTesting ($50/mo) is the only tool with an explicit LLM test type, measuring GA4 sessions from AI chatbots before and after changes. Otterly's March 2026 controlled experiments (fictional brand reaching rank #7 in ChatGPT in 14 days) are the closest to controlled GEO testing but are one-off research, not a customer platform. Profound ($155M raised, $1B valuation), BrightEdge ($12K+/yr), and Conductor ($30K-$200K/yr) offer enterprise monitoring without experimentation. The fundamental methodological gap: SEO testing splits pages because Google ranks pages; GEO measurement requires splitting queries because AI engines synthesize entity-level answers, and their outputs change 63% between consecutive days (Sill data, 139 brands). Sill measures citation shifts directly across six AI platforms, fits models independently per platform, and uses built-in calibration to establish empirical false positive rates. First experiment: 6-8 weeks; subsequent: 2-4 weeks.
The market splits into SEO experimentation platforms (page-level, organic traffic), GEO monitoring with before-and-after observation, and GEO experimentation with statistical controls.
Every marketing channel matures through the same three stages: observation (did something happen), experimentation (did our change cause it), and attribution (was it worth the spend). As we covered in our analysis of the experimentation gap, GEO completed stage one in 18 months with 27+ monitoring platforms launched since 2024. Stages two and three remain largely open.
SEO experimentation platforms (SearchPilot, SplitSignal, SEOTesting) brought rigorous causal methodology to traditional search: controlled page splits, statistical forecasting, confidence intervals. These tools measure organic traffic changes on variant pages after on-site modifications. Some are now extending their claims to GEO, measuring AI-influenced organic traffic rather than direct AI citations.
GEO monitoring platforms (Otterly, Profound, BrightEdge, Conductor) track AI mentions and Share of Voice over time. When a team makes a content change and SOV subsequently moves, these tools report the movement. They cannot distinguish whether the content change caused the movement or whether a model update, competitor action, or citation source rotation happened to coincide. This is before-and-after observation, the same methodology the entire GEO market relies on today.
GEO experimentation is the third category: tools that attempt to isolate whether a specific content change caused an observed AI visibility shift by using statistical controls rather than simple before-and-after observation. Sill is building in this category. No other platform in the market has shipped a comparable approach, though SearchPilot's extension into GEO testing represents a different methodological path toward a related goal.
Eight tools claim some form of GEO or SEO experimentation capability; only two directly measure AI citation changes, and their methodologies differ fundamentally.
The table below maps every tool with a testing or experimentation claim across pricing, methodology, minimum requirements, and whether it measures AI visibility directly or through organic traffic proxies. Pricing reflects publicly available information as of April 2026.
| Tool | Category | Pricing | What It Measures | Statistical Method | Min Requirements | Measures AI Directly? |
|---|---|---|---|---|---|---|
| SearchPilot | SEO A/B | Custom (enterprise, annual) | Organic traffic on variant pages including AI-influenced visits | Neural network forecast | 30K+ organic sessions/mo, 1000s of template pages | Indirect |
| SplitSignal | SEO A/B | Custom (enterprise) | Organic clicks on variant pages | CausalImpact (Bayesian time-series) | 100K+ clicks over 100 days, 300+ pages | No |
| SEOTesting | Time-based + LLM test | $50-$375/mo | GA4 sessions including LLM referrals | Before/after comparison | GA4 integration | Partial (LLM traffic only) |
| Otterly AI | GEO monitoring | $29-$489/mo | Brand mentions, SOV, GEO audit (25+ factors) | Before/after observation | None | Observation only |
| Profound | GEO monitoring | $99-$5K+/mo | SOV, citations, prompt volumes, conversion attribution | Before/after observation | None | Observation only |
| BrightEdge | SEO + AI monitoring | $12K+/yr | AI mentions, sentiment, source influence | Before/after observation | Enterprise budget | Observation only |
| Conductor | AEO monitoring | $30K-$200K/yr | Brand visibility, citation tracking across 13K+ domains | Before/after observation | Enterprise budget | Observation only |
| Sill | GEO experimentation | Free-$225/mo | AI citation shifts on affected vs. control queries | Bayesian estimation with statistical controls | 25+ prompts | Yes (direct AI measurement) |
SearchPilot runs server-side page splits with neural network forecasting at 95% confidence; their GEO testing measures AI-influenced organic traffic, not direct AI citations.
SearchPilot is the most rigorous SEO experimentation platform available. Their methodology splits pages into statistically similar control and variant buckets, applies changes server-side (via proxy, API, or edge integration) so there are no source code modifications, and uses a proprietary neural network model to forecast expected traffic while accounting for seasonality, competitor activity, and algorithm updates. Tests typically reach statistical significance within 14 days. Named clients include M&S, Skyscanner, Adidas, and Vistaprint, with published results showing effects as large as 50% organic traffic uplift from adding pros/cons content to product pages and a detected -7% drop from repositioning a flight search widget.
SearchPilot's GEO testing offering extends this methodology to what AI engines do with your content. They identify four levers of influence: ranking for new keywords that expand coverage across AI fan-out queries; ranking better in the sub-searches AI systems perform; appearing more compellingly in the search results that AI retrieves; and improving pages so information is more prominently featured in AI outputs. Their insight that AI engines perform a "whole buyer's journey of searches based on a fan-out set of queries" is correct, and testing whether on-site changes improve performance in those fan-out queries is a legitimate measurement approach.
The distinction worth understanding is what SearchPilot's GEO testing measures. It measures whether an on-site change affected the organic traffic that arrives at your pages, including traffic from users who were influenced by AI answers before clicking through. It does not query ChatGPT, Perplexity, or Gemini directly to measure whether your brand is mentioned more or less frequently in AI responses. Their own blog acknowledges this scope: "the outputs of LLMs don't constitute 'search results' in the same way that we are used to, and there isn't the same concept of 'ranking' within a conversation."
This matters because AI-influenced organic traffic and direct AI citation are two different signals. A page change might improve your Google ranking for a query that ChatGPT uses in its RAG retrieval, increasing the likelihood that ChatGPT cites you, which in turn drives traffic. SearchPilot measures the last step of that chain (the traffic) but not the intermediate steps (the citation, the mention, the recommendation). For ecommerce sites with high traffic volumes and thousands of template pages, this is a powerful and valid approach. For brands trying to understand whether ChatGPT is recommending them more often, it answers a related but different question. If SearchPilot's GEO testing does measure AI citations directly and we have mischaracterized the scope, we invite them to reach out so we can correct this comparison.
| Dimension | Details |
|---|---|
| Method | Server-side page splitting with neural network forecast |
| Pricing | Custom annual contracts (Essential, Advanced, Enterprise tiers) |
| Minimum requirements | 30K+ organic sessions/mo; thousands of same-template pages |
| GEO scope | AI-influenced organic traffic on tested pages (indirect) |
| Strengths | Gold standard for causal SEO measurement; server-side (no cloaking risk); proven at enterprise scale |
| Limitation | Does not query AI platforms directly; requires high traffic and page volume; enterprise pricing |
| Best for | Enterprise ecommerce with thousands of template pages |
SplitSignal uses Google's CausalImpact model with 100 days of historical data to build control groups; it requires 100K+ clicks and has no AI/GEO measurement capability.
SplitSignal is Semrush's SEO A/B testing product, built on 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 and causal inference. The implementation uses client-side JavaScript (easier setup than SearchPilot's server-side approach, but less robust for testing that depends on how crawlers render pages).
The minimum requirements are high: 300+ pages and 100,000+ clicks over the prior 100 days. This limits accessibility to sites with substantial organic traffic on templated page sections. Pricing is custom and separate from standard Semrush subscriptions. The strength is that teams already using Semrush for keyword research and competitive analysis can layer split testing into an existing workflow without adopting a separate platform.
SplitSignal has no AI or GEO visibility testing capability. Semrush has added an "AI Search" monitoring tab to its core platform (brand mentions across AI engines, available as a $99/mo add-on), but this is monitoring 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, which reintroduces the before-and-after problem that experimentation is supposed to solve.
SEOTesting offers an LLM test type that measures GA4 sessions from AI chatbots before and after page changes, starting at $50/mo with no traffic minimums.
SEOTesting is a time-based testing platform (before-and-after comparisons using Google Search Console and GA4 data) rather than a page-split testing platform. It also supports split testing with defined control and test URL groups, though the statistical methodology is less rigorous than SearchPilot or SplitSignal. What makes it relevant to this comparison is a specific feature: the LLM test type.
The LLM test type tracks user sessions originating from AI chatbots and search assistants (ChatGPT, Claude, Perplexity) using GA4 as the data source. Users select pages to monitor, specify the content change being made, and define tracking periods before and after implementation. SEOTesting then shows how LLM-generated traffic trended across those periods. Setup is minimal: connect GA4 and the test is running. At $50/mo for a single site, it is by far the most accessible tool in this comparison that addresses LLM traffic measurement at all.
The limitations are structural. SEOTesting's LLM test is time-based, meaning it compares traffic before and after a change without controlling for other variables that may have shifted during the same period. Their documentation acknowledges this directly: "LLM answers are generated from a mix of sources, and it's not always obvious how or when your content gets included." It also depends on GA4 referral data, which misses over 70% of AI traffic because AI platforms strip referrer headers. The tool measures what it can with the data available, and at $50/mo it provides a useful signal for teams that have nothing else. It should not be confused with causal experimentation.
Otterly ran controlled GEO experiments including building a fictional brand to rank #7 in ChatGPT in 14 days, but these are one-off research projects, not a customer-facing testing platform.
Otterly AI ($29-$489/mo) is a GEO monitoring platform with a strong audit capability: 25+ evaluation factors covering fluency, authority, and technical structure, with Gartner Cool Vendor 2025 recognition. Their standard product tracks brand mentions, Share of Voice, and citation analysis across ChatGPT, Google AI Overviews, Perplexity, and Copilot, with AI Mode and Gemini available as add-ons ($9-$149/mo depending on tier). As a monitoring tool, it is well-regarded and competitively priced.
What makes Otterly relevant to a testing comparison is their published research experiments. In March 2026, Otterly ran two controlled studies. The first tested whether deploying an llms.txt file affected AI bot traffic; after 90 days, only 84 of 62,100+ AI bot visits targeted the file, performing 68% worse than average content pages. The second created a fictional GEO agency ("BE VISIBLE") with a minimal 7-page website, no blog, no backlinks, no domain history, and tracked whether off-page citations alone could generate AI visibility. The result: rank #7 in ChatGPT within 14 days, with 76% of mentions coming from location-specific, year-tagged prompts. As we discussed in our GEO tactics analysis, these experiments produced findings that before-and-after monitoring cannot.
The key distinction is that these were one-off research exercises, not a repeatable experimentation platform available to Otterly customers. Otterly's product is monitoring and auditing; the experiments were internal research published on their blog. A team buying Otterly gets GEO monitoring and content audits, not a tool for running their own controlled tests. This is not a criticism of Otterly; their research experiments are some of the most valuable public contributions to GEO methodology. The point is that the product and the research serve different functions.
Profound ($99-$5K+/mo), BrightEdge ($12K+/yr), and Conductor ($30K-$200K/yr) offer AI visibility monitoring at enterprise scale with no experimentation or causal testing capabilities.
These three platforms represent the enterprise tier of GEO monitoring, and their inclusion here is to clarify what they do not do. Each offers strong AI visibility tracking, competitive benchmarking, and citation analysis. None offers causal testing or experimentation. A team making content changes and monitoring SOV movement on any of these platforms is running before-and-after observation with the same confounding variables that affect every monitoring tool: model updates every 2-6 weeks, 40-60% monthly citation source volatility, competitor actions, and the platform divergence where 55% of brands show a 10+ point SOV spread across AI engines.
Profound ($155M raised, $1B valuation, 700+ enterprise customers) is the market leader in AI visibility monitoring. Their unique asset is Prompt Volumes: real AI search demand data showing how often specific queries are asked across AI platforms. Their Actions feature generates content briefs based on competitive gaps. Profound connects AI visibility data to behavioral analytics and conversion tracking, which is the closest the monitoring tier gets to attribution. Starting at $99/mo for ChatGPT only; meaningful multi-platform coverage requires enterprise pricing from $5K/mo.
BrightEdge launched AI Hyper Cube in March 2026, tracking prompts mentioning a brand, identifying citation sources, labeling sentiment, and providing 24 months of historical data across 50+ AI surfaces. Jim Yu, BrightEdge's CEO, correctly identified the core challenge: "Each engine characterizes your brand differently, and CMOs must treat them as distinct, dynamic environments." BrightEdge is strongest for enterprise teams already using their SEO platform who want AI visibility added to their existing workflow. Contracts run $12K-$100K+/yr.
Conductor published the 2026 AEO/GEO Benchmarks Report analyzing 3.3 billion sessions across 13,000+ domains, finding that ChatGPT drives 87.4% of AI referral traffic and AI referrals represent approximately 1% of total traffic. Their Visibility Heatmap and Brand Visibility Rank features provide enterprise-scale observation. At $30K-$200K/yr, Conductor is the most expensive option in this comparison and is suited for Fortune 500 teams with dedicated analytics staff.
SEO A/B testing splits pages to measure Googlebot behavior; AI engines synthesize entity-level answers from multiple sources, and their outputs change 63% between consecutive days.
SEO A/B testing works because Google ranks individual pages. You can split pages into two groups, modify one group, and compare the traffic each group receives, because Google treats each page as a distinct unit with a measurable outcome (its ranking position and the clicks it generates). The control group tells you what would have happened without the change. This is sound experimental design, and SearchPilot's implementation of it is excellent.
AI engines do not work this way. When a user asks ChatGPT "what is the best project management tool," the response is not a ranked list of pages. It is a synthesized answer drawn from training data, real-time RAG retrieval across multiple queries, and the model's learned associations about brands and categories. The unit of measurement is not the page but the entity: how often and how prominently your brand is mentioned. You cannot split your brand into a control group and a variant group in the way you can split pages.
| Dimension | SEO A/B Testing | GEO Measurement |
|---|---|---|
| Unit of measurement | Page rankings and clicks | Entity mentions and citations |
| Output stability | Rankings relatively stable day-to-day | 63% of competitor sets change daily |
| Control mechanism | Split pages into matched groups | Cannot control what AI retrieves |
| Impression data | Search Console provides impressions, clicks, CTR | No native impression data from AI platforms |
| Ranking model | Retrieve best matching page per query | Synthesize answers from multiple sources |
| Signal type | Page-level: on-page factors, backlinks | Entity-level: off-site mentions, brand authority, training data |
The AirOps 2026 State of AI Search report quantified the instability: only 30% of brands maintain visibility between consecutive AI answers, and just 20% remain visible across five consecutive runs. Sill's own data shows that only 2.7% of day-over-day competitor sets are identical, with an average overlap of 37%. This level of stochasticity means any before-and-after comparison carries a high risk of attributing random variance to a content change. The experimental design needs to account for this noise, which page-level splitting was not designed to do.
Sill measures AI citation shifts directly across six platforms, using statistical controls to separate content impact from background noise like model updates and competitor movements.
Sill's approach to GEO experimentation starts from the observation that while you cannot split a brand, you can split the queries that mention it. A content change on your pricing page should affect prompts about pricing and purchasing but should not affect prompts about your company's founding story or leadership team. The unaffected prompts serve as controls: if SOV on pricing prompts increases while SOV on unaffected prompts stays flat, the evidence that the pricing page change caused the shift is stronger than a simple before-and-after comparison where everything might have moved due to a model update.
The system fits models independently per AI platform rather than averaging across them. This matters because 91.6% of cited URLs appear on only one platform; a change that moves ChatGPT may leave Gemini unchanged, and the results need to surface that divergence rather than hiding it in an aggregate number. Built-in calibration checks establish empirical false positive rates so that confidence badges have a stated basis rather than a theoretical assumption.
The output is not "your SOV went up." It is "following these changes, SOV on affected prompts increased while control prompts remained stable, with high confidence." The language is deliberately "following these changes" rather than "caused by," because this is intervention effect estimation, not a randomized controlled trial. The stated limitations are part of every result.
The first experiment takes 6-8 weeks; subsequent experiments take 2-4 weeks, comparable to SearchPilot's typical 14-day SEO test cycle. Experimentation requires 25+ prompts to ensure sufficient controls; plans with fewer prompts are monitoring-only. Sill's experimentation is in development and we are transparent about limitations: brand-level authority changes (PR campaigns, viral earned media) lift all queries uniformly, leaving no unaffected control group, and the system flags reduced confidence in those cases.
The right tool depends on what you need to measure: organic traffic impact (SearchPilot), LLM referral traffic (SEOTesting), AI monitoring (Otterly/Profound), or direct AI citation shifts (Sill).
These tools solve different problems, and framing the choice as a head-to-head comparison misses the point. SearchPilot and Sill are not competing for the same measurement; they are measuring different layers of the same phenomenon. A large ecommerce brand might reasonably use SearchPilot for page-level organic testing and Sill for direct AI citation measurement. An SMB with 50 prompts and no engineering team has different needs from an enterprise with thousands of template pages and 30K+ monthly organic sessions.
| If you need... | Use this | Why |
|---|---|---|
| Causal SEO testing at enterprise scale | SearchPilot | Gold standard for page-level organic experimentation; server-side, neural network forecasting, proven case studies |
| SEO split testing within an existing Semrush stack | SplitSignal | CausalImpact methodology; integrates with Semrush keyword and competitive data |
| Low-cost LLM referral traffic measurement | SEOTesting | Only tool with explicit LLM test type; $50/mo; GA4-based, accessible, honest about limitations |
| GEO monitoring with page-level audits | Otterly AI | 25-factor GEO audit, competitive pricing, published research that advances the field |
| Enterprise AI visibility monitoring with conversion attribution | Profound | Market leader with Prompt Volumes data, behavioral analytics, 10+ AI engines at enterprise tier |
| Direct AI citation measurement with experimental controls | Sill | Statistical controls that separate content impact from noise; per-platform models; 6 AI platforms included at every tier |
For most teams, the practical starting point is GEO monitoring (understanding where you stand across AI platforms) combined with content optimization (knowing what to change). As we covered in our analysis of the GEO proof gap, the experimentation layer is what separates "we changed our content and SOV moved" from "we changed our content and have evidence that it caused SOV to move." The first statement is observation; the second is measurement. The GEO market is in the process of building the infrastructure for the second statement, and the tools in this comparison represent different approaches to that problem from different starting points.
Sill's free tier includes monitoring across all 6 AI platforms, GEO recommendations, and Brand Watchdog. Experimentation requires 25+ prompts, available from the $90/mo Basic plan. For a full comparison of monitoring-focused platforms, see our AI visibility platform comparison.
For detailed methodology breakdowns, pricing analysis, and specific guidance on how each tool compares to Sill's experimentation approach, see the individual comparisons below.
Page-level SEO experimentation vs entity-level GEO experimentation
Sill vs SplitSignalCausalImpact SEO testing vs direct AI citation measurement
Sill vs SEOTestingLLM test type vs direct AI citation measurement
Sill vs Otterly AIGEO audit and research experiments vs experimentation platform
Sill vs ProfoundPrompt Volumes and enterprise monitoring vs experimentation layer
Sill vs ConductorEnterprise AEO platform vs mid-market experimentation
Sill monitors your brand across six AI platforms, generates GEO recommendations, and is building the experimentation layer to prove which content changes actually move AI visibility.
Request your first analysis today to see where you stand.