ADHELIX_
System_Boot_Sequence
AdHelix disassembles multi-modal social video ads into granular, quantifiable attributes across 8 universal scientific dimensions to calculate causal lift without correlation bias.
Browse the 8 universal scientific dimensions and 40+ computer vision markers used by AdHelix to deconstruct video ads and measure marginal causal KPI lift.
Analyzes the raw visual container of the advertisement. Video ads are deconstructed into 0.5-second temporal slices to extract aspect ratio compliance, camera motion kinematics (handheld jitter vs. stabilized rigs), frame-to-frame shot cut velocity, and lighting color temperatures.
AdHelix extracts Pillar 1 attributes every 0.5 seconds using these verified computer vision models:
C_{\text{velocity}} = \frac{\sum_{i=1}^{k} \mathbb{I}(\Delta \text{Scene}_i \le 1.5s)}{T_{\text{total}}} \times 100Quantifies visual entropy. Ads with cut velocity > 0.67 cuts/second in the initial 3.0s window achieve significantly higher thumb-stop survival.
Core Principle: Early visual pacing and rapid camera kinematics prevent spontaneous attention drop-off in social feed environments.
1080x1920 native full-screen mobile vertical aspect ratio with zero black letterboxing bars.
Natural creator handheld camera shake simulating authentic user-generated content.
Two or more distinct visual camera scene changes within the first 3.0 seconds.
Natural cool white color temperature matching ambient daylight illumination.
Fixed tripod framing commonly associated with traditional high-production TV spots.
Upload your Meta and TikTok video ads. AdHelix extracts all 40+ computer vision markers across 0.5s intervals and runs TreeSHAP marginal attribution to isolate exactly which elements are driving your ROAS.