LayR Methodology
Last reviewed: February 16, 2026 · Author: LayR Editorial Team · Reviewer: LayR Product Team
Direct answer
LayR recommendations are produced by combining wardrobe inventory, user preferences, and context signals (occasion, weather, and style inputs). The system ranks practical combinations and exposes them through planning and try-on workflows.
1) Inputs used by the system
LayR can use the following user-provided inputs to improve recommendation quality:
- Photos of clothing items and closet metadata (type, color, usage).
- Occasion and schedule context (work, casual, event, travel).
- Optional style preferences and feedback signals (saved or skipped outfits).
2) Recommendation process
The system generates candidate combinations from available items, filters them using compatibility heuristics (for example color balance and category pairing), then prioritizes options based on context relevance and prior user feedback.
The output is a ranked set of wearable combinations intended to reduce decision time while preserving style consistency.
3) Try-on and styling features
Virtual try-on and styling assistants are used to preview options before commitment. These capabilities are support tools, not guarantees of exact real-world appearance.
Details about third-party AI services and data sharing are documented in our Privacy Policy.
4) Limitations and quality controls
- Recommendations may be less accurate with incomplete closet coverage.
- Visual previews can vary from real-world lighting and fit.
- User feedback loops are used to improve relevance over time.
5) Updates
We update this methodology as product capabilities evolve. Material changes are reflected by updating the review date on this page.