Technical Transparency

How Aurale Analyzes Your Profile

We believe you deserve to know exactly how our AI evaluates your dating profile. Here is our complete methodology — no black boxes, no hand-waving.

Last updated: April 2026

Computer Vision: Photo Analysis

Our photo analysis pipeline evaluates each image across multiple dimensions that research has shown to influence dating app match rates:

  • Facial Expression Analysis: We detect and classify expressions (genuine smile vs. neutral vs. forced) using landmark detection. Studies show that authentic Duchenne smiles increase perceived attractiveness by up to 37%.
  • Photo Composition: Rule-of-thirds alignment, subject framing, and background complexity scoring. Cluttered backgrounds reduce profile engagement by approximately 22%.
  • Lighting Quality: We measure exposure distribution, color temperature, and shadow-to-highlight ratio. Natural daylight photography consistently outperforms artificial lighting in A/B tests.
  • Photo Variety Scoring: We evaluate whether your gallery demonstrates lifestyle diversity — solo vs. social, indoor vs. outdoor, casual vs. dressed-up.

NLP: Bio & Prompt Analysis

Your written content is analyzed through a natural language processing pipeline calibrated against high-performing dating profiles:

  • Tone Classification: We classify your bio across axes like humor, sincerity, confidence, and aggression. Profiles that score high on humor + sincerity consistently generate 2-3x more right-swipes.
  • Red Flag Detection: Negative language patterns (demands, negativity, passive aggression) that correlate with low match rates are flagged with specific replacement suggestions.
  • Conversation Hook Score: We evaluate whether your bio provides clear, low-friction opening lines for potential matches to use when messaging.
  • Cliché Detection:Overused phrases (“love to travel,” “looking for my partner in crime”) are identified and alternative suggestions provided.

SWOT Framework & Match Projections

Every analysis produces a structured SWOT (Strengths, Weaknesses, Opportunities, Threats) report. Match projections are estimated based on:

  • Composite score across all analyzed dimensions weighted by empirical importance
  • Platform-specific calibration (Hinge weights prompts more heavily; Tinder weights photos)
  • Confidence intervals are provided — we never claim exact outcomes
“We provide directional guidance based on pattern analysis, not guarantees. Individual results vary based on location, demographics, and personal preferences.”

Privacy & Data Handling

Trust requires transparency about data handling. Here is our complete policy:

  • Zero Retention: Uploaded photos are analyzed in-memory and permanently deleted immediately after processing. We do not store your images.
  • No Training on User Data: Your profile content is never used to train or fine-tune our models.
  • Encryption: All data transmitted between your browser and our servers uses TLS 1.3 encryption.
  • Third-Party Processors: AI inference is processed through SOC 2-compliant cloud infrastructure. No personal data is shared with advertisers or data brokers.

Limitations & Biases

No AI system is perfect. We are transparent about our current limitations:

  • Our training data may reflect demographic biases present in publicly available attractiveness research. We actively work to identify and mitigate these biases.
  • Photo analysis accuracy decreases with heavily filtered, low-resolution, or group photos where the primary subject is ambiguous.
  • Cultural context matters — humor and attractiveness standards vary significantly across cultures. Our current models are primarily calibrated on English-language, Western dating app data.

See the methodology in action

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