Current Product
Clad
AI personal color service
Clad analyzes a user-submitted photo and presents a personal color result with a palette and beauty recommendations. The product focuses on making the reasoning understandable and the result easy to apply.
- Personal color analysis
- Palette recommendations
- Beauty recommendation UX
Clad product screen
Live Clad service
Screen details
Live Clad service
The English mobile homepage from the current production service.
Service development record
How the analysis became a usable decision
The same service principles and evidence carry from the moment a photo is submitted to the moment someone understands the result and chooses colors or products.
- 01Observation & evidence
Find where people get stuck after diagnosis
- Question examined
- Where do people curious about personal color face the most friction and uncertainty?
- Decision made
- Focus on a low-friction first analysis and usable next choices, not a replacement for professional diagnosis.
- Evidence used for the decision
- The initial constraints were the time and cost of professional diagnosis, the difficulty of applying a result, and photo variance caused by lighting and editing.
- 02Problem & principle
An understandable starting point, not an absolute answer
- Question examined
- How can a photo-based analysis with uncertainty become a responsible product?
- Decision made
- Explain reasoning and limits, provide a path to retake the photo, and present recommendations as a starting point for exploration.
- Evidence used for the decision
- The product hypothesis was that a season label alone would not help someone understand the result or apply it to a color decision.
- 03Hypothesis & scope
One core flow from photo to recommendation
- Question examined
- Which user journey must the first version complete?
- Decision made
- Concentrate on photo preparation, analysis, explanation, palette, and beauty recommendations without recreating every context of professional diagnosis.
- Evidence used for the decision
- The smallest useful result was defined as a choice someone could understand and reference immediately—not merely a type label.
- 04Experience & system
Build the experience around the model
- Question examined
- How should the analysis capability become a dependable service experience?
- Decision made
- Connect input validation, an AI vision workflow, result normalization, palette and recommendation UI, cloud delivery, and observation.
- Evidence used for the decision
- Flutter, Python, Terraform, and explicit error and retry boundaries were selected for a system that can be operated directly at its current scale.
- 05Launch & learning
Treat launch as the start of observation
- Question examined
- Does the result support a real choice beyond producing an interesting answer?
- Decision made
- Use result-explanation views, palette and recommendation exploration, reasons for reanalysis, and qualitative feedback as signals for the next iteration.
- Evidence used for the decision
- Observation currently centers on explanation clarity and recommendation confidence; outcomes are not claimed before meaningful evidence accumulates.
- Current focus
- Clarity of result explanations
- Decision signals
- Palette and recommendation exploration, plus reasons for reanalysis
- Improvement loop
- User feedback → small change → observe again
These are inputs for the next operating or validation decision, not outcomes already achieved.
System architecture
One path from photo to recommendation
Clad combines photo validation, AI-assisted color analysis, result normalization, palette generation, and recommendation mapping in one guided experience.
Input Layer
Photo upload, validation, image preparation
AI Workflow
Vision analysis, prompt pipeline, result normalization
Product Layer
Result explanation, palette UI, recommendation mapping
Operations Layer
Analytics, feedback, iteration backlog
Current priorities
The question I want feedback on now
Which part of the result helped you understand it, and where did choosing from the palette or recommendations become difficult?