> ## Documentation Index
> Fetch the complete documentation index at: https://docs.dacard.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# How scoring works
> Our 27-dimension scoring methodology, signal blending, and the receipts we hand back.
We score every product across 27 dimensions in 6 functions. Each dimension lands 1-5. The composite (27-135) places you on a 5-stage maturity ladder. Connect integrations to ground the score in operational data.
# How scoring works
We score products by reading observable signals across **27 dimensions** in **6 functions**. Each dimension scores 1-5. Your composite (27-135) places you on a 5-stage ladder.
## The five steps
We visit the URL and pull up to 6 pages, extracting signal from your product's public surface, docs, pricing, and UX patterns.
We score each of the 27 dimensions against the rubric for each stage. Signal includes architecture tells, business model, UX patterns, and operational indicators.
Every dimension lands a score from 1 (Foundation) to 5 (Compounding). The 27 scores sum to a composite total of 27-135.
The composite places you on the maturity ladder (Foundation through Compounding).
We surface dimension-level reads, your strengths, your gaps, and the next move we'd take. The receipts live at `/r/{id}`.
## Five maturity stages
| Stage | Score | Read |
| --------------- | ------- | --------------------------------------------------------------------------------------------- |
| **Foundation** | 27-48 | Basics missing or absent. Capability is not yet part of the product's core. |
| **Building** | 49-70 | Emerging practice, applied unevenly. Features exist; advantage doesn't. |
| **Scaling** | 71-91 | Systematic, measured, repeatable. Capability is a real lever. |
| **Leading** | 92-113 | Deeply integrated. Pull it out and the product breaks. |
| **Compounding** | 114-135 | Capability compounds across product, data, ops, and business model. The flywheel runs itself. |
## 27 dimensions across 6 functions
**Market intelligence** - how well the team collects and synthesizes market signal
**Decision quality** - evidence behind product and strategic decisions
**Roadmap discipline** - how well the roadmap reflects strategic priorities and tracks outcomes
**Competitive positioning** - clarity and defensibility of market position
**Research and discovery** - depth and consistency of user research
**Prototyping speed** - how fast the team goes from idea to testable artifact
**Experience design** - quality of interaction patterns and UX craft
**Design-dev handoff** - efficiency and fidelity of the design-to-build handoff
**Architecture and systems** - depth of technical integration
**Spec and context quality** - quality of PRDs, tickets, and context handed to builders
**Build vs buy** - decision rigor on model and infrastructure choices
**Delivery velocity** - speed and consistency of shipping improvements
**Customer signal synthesis** - quality of customer feedback collection and synthesis
**Product analytics** - depth and use of product usage data
**Data flywheel** - whether data creates a defensible compounding advantage
**Feedback loop quality** - whether usage data flows back to improve the product
**Knowledge management** - how well institutional knowledge is captured, organized, and surfaced
**Positioning and messaging** - clarity and resonance of market messaging
**Launch execution** - consistency and quality of launches
**Adoption and expansion** - how the product drives usage growth and expansion
**Pricing and packaging** - whether pricing reflects value and supports growth
**Quality and experimentation** - rigor of testing, evaluation, and quality processes
**Team orchestration** - how the team coordinates work across people and systems
**Process iteration** - how fast the team improves its own ways of working
**Cost and token economics** - how the team manages inference and infrastructure costs
**Security and compliance** - how the team manages security and compliance risk
**Reliability and resilience** - how features handle failure and maintain availability
## Signal bars
We surface scores as **signal bars** in a traffic-light system. They appear throughout the dashboard, reports, and portfolio views.
| Dimension score | Color | Read |
| ------------------------------- | ----- | ---------------------------------------------- |
| **1** (Foundation) | Red | Gap. This dimension is capping your composite. |
| **2** (Building) | Amber | Developing. Targeted move pays off fast. |
| **3** (Scaling) | Green | Strong. Maintain and extend. |
| **4-5** (Leading / Compounding) | Green | Exceptional. Defensible capability. |
## Signal-score blending
When you connect integrations (GitHub, Linear, PostHog, others), your scores get **signal-grounded**. We blend two sources:
1. **Surface assessment** - our read of your public surface
2. **Adapter signals** - operational ground truth from connected tools (deploy frequency, PR cycle time, sprint velocity, others)
### How we blend
Each integration produces metrics mapped to specific dimensions. We convert those metrics into 1-5 signal scores using calibrated thresholds, then blend with the surface read.
| Factor | Effect |
| ---------------- | ------------------------------------------------------------------------------------- |
| **Signal count** | More signals, higher blending weight (5 minimum to activate, up to 0.4 weight at 20+) |
| **Confidence** | High-confidence signals (from well-established adapters) carry more weight |
| **Weight cap** | Adapter signal never exceeds 60% weight; surface read stays the primary input |
The result: connected teams get measurably tighter scores because dimensions reflect both observable surface quality and live operational data.
Wire up GitHub and Linear first. They cover the most dimensions and produce the highest-confidence adapter signal.
## Scoring practice
Start with your own URL to calibrate. Then score 2-3 competitors to see where you sit. Each URL score costs 10 credits.
Click **Add context** before you score to drop a brief read on what your product does and who it serves. We use that to ground products with thin public surface.
Scores are point-in-time. Re-score after a major ship or quarterly to trace trajectory. We preserve score history automatically.
URL-only scoring leans on the public surface. Wiring GitHub, Linear, and PostHog adds operational ground truth that materially improves both accuracy and confidence.
Every score has a shareable URL at `/r/{id}`. Send it to leadership, investors, or your team without making them sign in.
## Anonymous scoring
Anyone can score at [app.dacard.ai/try](https://app.dacard.ai/try) without an account. Anonymous scores are rate-limited (1 per hour per IP) and don't unlock the full report.
When you sign up after an anonymous score, we can link the anonymous score to your new account. Your history travels with you.
## Related
Walk through the report section by section.
Full 27-dimension definitions and scoring rubric.
Pull live operational signal into scoring.
Step-by-step walkthrough for first-time users.