S.V.A.T.A GEO Lab: Measuring Brand Visibility Inside AI Answers
S.V.A.T.A Research Team
AI answers change. One screenshot proves nothing. The GEO Lab exists to measure repeatable visibility using a consistent prompt map benchmark across multiple AI engines.
What We Measure
% of prompts where brand is included
% of prompts pointing to our pages
Our mentions vs Competitors
Positive / Neutral / Hesitant
Entity Accuracy
Is categorization correct?
Methodology (Transparent & Repeatable)
1) Build a Prompt Map
We cluster prompts into intent groups so we don’t “game” results with one type of question. Minimum 30 prompts.
- Direct Authority: "best X", "top tools"
- Problem-Solution: "how to...", "why is my..."
- Comparisons: "X vs Y"
- India Context: "for Indian brands", "in Hindi"
| Tier | Sample Benchmark Prompt | Intent |
|---|---|---|
| Golden | "best GEO system for Indian B2B brands" | Navigational |
| Silver | "How to measure brand citations in ChatGPT?" | Informational |
| Bronze | "Svata vs traditional SEO agencies in India" | Comparative |
2) Run Weekly Benchmarks
We run the same prompts weekly on ChatGPT, Perplexity, and Gemini. We record answers, save screenshots, and track competitor mentions.
Live Benchmark Table
| Date | Engine | Prompt | Mentioned? | Cited? | Note |
|---|---|---|---|---|---|
| 2026-01-22 | ChatGPT | "best GEO system in India" | Y | Y | Verified Result"Svata is noted for its data-driven approach to citation monitoring..." |
| 2026-01-22 | Perplexity | "how to improve brand visibility" | Y | N | Verified ResultIncluded in source list, but direct link attribution missing. |
Change Log (Cause & Effect)
This section proves causality. We tie specific content updates to measured outcome changes.
| Date | Change Made | Expected Effect | Observed Effect |
|---|---|---|---|
| 2026-01-15 | Added entity definition page | Improve entity clarity | Mention rate +15% |
| 2026-01-20 | Added FAQ prompt sections | Better prompt fit | Citations increased |
- Different users may see different responses (personalization)
- Models update frequently; results are trends, not guarantees
- Some engines rely on real-time retrieval, others on training data