How Scoring Works
Dacard.ai evaluates products using AI to assess observable signals across 10 dimensions. Each dimension is scored 1-4, producing a total score of 10-40 that maps to one of five maturity stages.The scoring process
Crawl
The platform visits the URL and extracts signals from the product’s public-facing presence, documentation, and UX patterns.
Five maturity stages
| Stage | Score | What it means |
|---|---|---|
| Legacy | 10-15 | AI is not part of the product, UX, or competitive strategy |
| AI-Curious | 16-21 | Experimenting with AI features, but no proprietary value yet |
| AI-Enhanced | 22-27 | AI is a real differentiator, but the core product could survive without it |
| AI-First | 28-33 | AI is the product. Remove it and nothing works |
| AI-Native | 34-40 | AI compounds across every layer: product, data, operations, and business model |
Ten scoring dimensions
Dimensions are grouped into four clusters:Foundation, Value Proposition, Architecture, Data Strategy
Foundation, Value Proposition, Architecture, Data Strategy
- Value Proposition, Is AI central to the product’s core value, or bolted on?
- Architecture, Are models, data pipelines, and inference deeply integrated?
- Data Strategy, Does the product build proprietary data moats?
Market Position, Pricing, Competitive Moat
Market Position, Pricing, Competitive Moat
- Pricing, Does pricing reflect AI value (usage-based, outcome-based)?
- Competitive Moat, Is the AI advantage defensible and compounding?
Execution Engine, Team Structure, Build vs Buy, Iteration Speed, Feedback Loop
Execution Engine, Team Structure, Build vs Buy, Iteration Speed, Feedback Loop
- Team Structure, Is the team organized around AI-native workflows?
- Build vs Buy, Are model/infra decisions strategically sound?
- Iteration Speed, Can the team ship AI improvements rapidly?
- Feedback Loop, Does usage data flow back into model improvement?
Outlier, User Experience
Outlier, User Experience
- User Experience, Does the AI UX feel native and delightful, not awkward?
Signal bars
Scores are visualized as signal bars, a wifi-style 5-bar indicator using traffic-light colors. Signal bars appear throughout the dashboard, reports, and portfolio views for quick visual scanning.| Dimension score | Signal strength | Color |
|---|---|---|
| 1, Legacy | 1-2 bars | Red |
| 2, AI-Curious | 2-3 bars | Amber |
| 3, AI-Enhanced | 3-4 bars | Yellow-green |
| 4, AI-Native | 4-5 bars | Green |
Scoring best practices
Score your own product first
Score your own product first
Start with your own product URL to calibrate. Then score competitors to see how you compare.
Re-score after shipping improvements
Re-score after shipping improvements
Scores are point-in-time snapshots. Re-score after major releases to track your trajectory. Score history is preserved automatically.
Use the Operations framework too
Use the Operations framework too
Product maturity tells you about the product. Operations maturity tells you about the team. Together they reveal the full picture.
Share results with stakeholders
Share results with stakeholders