Continuous Selection System
Simulate · Observe · Model · Track
A continuous system that simulates, observes, and models how AI selects entities, then tracks how that selection evolves across models and time.
Stage 01
Simulate
Rotates prompts through evaluation cycles to probe how AI selects entities across phrasing, context, and model behavior. Sampling frequency adjusts automatically based on prompt stability.
15 queries × 4 engines, per cycle
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Competitive landscape map
Maps which brands, competitors, and topics AI engines associate with yours, and how those associations shift over time.
Engine-aware scoring
Each AI engine is measured on its own terms, so a low-volume engine doesn't distort the picture and a noisy one doesn't drown it out.
Shift tracking
Tracks how selection patterns move across models and time, surfacing the prompts and engines where your position is shifting fastest.
Adaptive scoring
The scoring model recalibrates to your category and engine mix over time, so the number reflects what matters in your market.
5
AI engines evaluated per scan cycle
15
Queries per project (10 system + 5 client)
~1,260
Baseline observations before adaptive evaluation
14d
Impact burst window on every executed change
Baseline observation count reflects 15 queries × 4 engines × 21 days of daily evaluation during Phase 1. Afterward, the system concentrates its cycles where selection behavior is shifting most. Engines evaluated: ChatGPT, Perplexity, Google Gemini, Anthropic Claude.
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