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.
Scoring Frameworks
Dacard.ai scores products across three original frameworks. Each measures a different dimension of AI readiness, and together they provide a complete picture of where a product team stands and what to do next.Three frameworks, one system
F1: Product Operations Maturity
How capable is your team at building AI-native products? Evaluates 27 dimensions across 6 functions. Composite score 27-135.
F2: AI-Native Lifecycle
How AI-native is your build process? Assesses 6 stages and 36 tasks of modern product development from spec to compound learning.
F3: AI Product Assessment
How AI-native is your product? 27 dimensions evaluating how deeply AI is embedded in your UX, architecture, and business model.
- F1: Product Operations Maturity
- F2: AI-Native Lifecycle
- F3: AI Product Assessment
F1: Product Operations Maturity Framework
The flagship framework. Evaluates a product team’s operational capability across 27 dimensions organized into 6 functions, each scored 1 to 5, for a composite score of 27 to 135.Five maturity stages
| Stage | Score | What it means |
|---|---|---|
| Foundation | 27-48 | Basic or absent capabilities. Most work is manual and ad hoc. |
| Building | 49-70 | Emerging practices, inconsistently applied across functions. |
| Scaling | 71-91 | Systematic processes in place with measurable outcomes. |
| Leading | 92-113 | AI deeply embedded in the team’s operating model. |
| Compounding | 114-135 | Self-improving systems. Every cycle makes the team better. |
Six functions, 27 dimensions
Strategy (4 dimensions)
Strategy (4 dimensions)
Market Intelligence, Decision Quality, Roadmap Discipline, Competitive Positioning
Design (4 dimensions)
Design (4 dimensions)
Research & Discovery, Prototyping Speed, Experience Design, Design-Dev Handoff
Development (4 dimensions)
Development (4 dimensions)
Architecture & Systems, Spec & Context Quality, Build vs Buy, Delivery Velocity
Operations (4 dimensions)
Operations (4 dimensions)
Customer Signal Synthesis, Product Analytics, Data Strategy & Flywheel, Feedback Loop Quality
GTM (4 dimensions)
GTM (4 dimensions)
Positioning & Messaging, Launch Execution, Adoption & Expansion, Pricing & Packaging
Intelligence (4 dimensions)
Intelligence (4 dimensions)
Quality & Experimentation, Team Orchestration, Process Iteration, Cost & Token Economics
Deep dive: F1 Framework
Full 27-dimension definitions, scoring criteria, and stage progression guide.
How scoring works
When you submit a URL to the scoring API (POST /api/score), Dacard.ai:
Crawl
The platform visits the URL and crawls up to 6 pages, extracting signals from the product’s public-facing presence, documentation, and UX patterns.
Generate
A composite score (27-135), maturity stage classification, dimension-level insights, and recommendations are produced.
Scores can also be generated via the anonymous endpoint (
POST /api/try-score) without authentication, subject to rate limits.The compound readiness model
When all three frameworks score well, a product team achieves compound readiness: the state where team capability (F1), build process maturity (F2), and product AI-nativeness (F3) reinforce each other.| Scenario | Interpretation |
|---|---|
| High F1, low F2 | Strong team, weak process. Talented team shipping inconsistently. |
| High F3, low F1 | AI-native product, weak ops. Impressive product, unsustainable operations. |
| High F1 + F2 + F3 | Compound readiness. The target state for AI-native product teams. |
Learn more
Score a URL
API reference for the F1 scoring endpoint.
Get a result
Retrieve stored scoring results.
AI coaching chat
Ask DAC for recommendations based on your scores.