> ## 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 > The three frameworks that power Dacard.ai scoring # Scoring frameworks Dacard.ai scores products across three frameworks. Each measures a different surface (team, process, product) and together they form one read. ## Three frameworks, one read > **Team Operations** > > How capable is your **team** at building intelligent products? 27 dimensions across 6 functions. Composite 27-135. > **Development Lifecycle** > > How mature is your **build process**? 6 stages, 36 tasks, from spec to compound learning. > **Product Assessment** > > How deep is intelligence in your **product**? 27 dimensions across UX, architecture, data flywheel, and business model. *** **Team Operations** ## Team Operations 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 | Practices 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)** > > Market Intelligence, Decision Quality, Roadmap Discipline, Competitive Positioning > **Design (4 dimensions)** > > Research and Discovery, Prototyping Speed, Experience Design, Design-Dev Handoff > **Development (4 dimensions)** > > Architecture and Systems, Spec and Context Quality, Build vs Buy, Delivery Velocity > **Operations (4 dimensions)** > > Customer Signal Synthesis, Product Analytics, Data Strategy and Flywheel, Feedback Loop Quality > **GTM (4 dimensions)** > > Positioning and Messaging, Launch Execution, Adoption and Expansion, Pricing and Packaging > **Intelligence (4 dimensions)** > > Quality and Experimentation, Team Orchestration, Process Iteration, Cost and Token Economics > **[Deep dive: Team Operations](https://docs.dacard.ai/knowledge-base/framework-maturity.md)** > > Full 27-dimension definitions, scoring criteria, and stage progression guide. **Development Lifecycle** ## Development Lifecycle Framework Maps the six stages of building intelligent products with 36 tasks across stages. Development Lifecycle uses a self-assessment model rather than automated URL scoring. ### Six lifecycle stages **1. Specify** Define the problem with clear constraints. Establish what the model can and cannot do before building. **2. Context** Assemble the knowledge layer the model needs. RAG pipelines, embeddings, grounding data. **3. Orchestrate** Wire up models, agents, and pipelines. Connect inference to product surfaces. **4. Validate** Test quality at machine speed. Automated evals, human review, and prompt tuning. **5. Ship** Deploy and manage inference costs. Token budgets, latency targets, and model selection. **6. Compound** Close the feedback loop. Usage data flows back into model and product improvement. ### Cross-cutting concerns Three forces span all stages and must be managed continuously: > **Token Economics** > > The cost per inference. Every stage has a token budget impact. > **Role Fluidity** > > The shift from specialist to generalist as model capability collapses role boundaries. > **Cognitive Debt** > > Accumulated prompt complexity, model coupling, and evaluation gaps. > **[Deep dive: Development Lifecycle](https://docs.dacard.ai/knowledge-base/framework-lifecycle.md)** > > Full stage definitions, 36 tasks, and operations stack categories. **Product Assessment** ## Product Assessment Framework Evaluates how deeply intelligence is embedded in the **product itself** across **27 dimensions**. Where Team Operations measures the team, Product Assessment measures the product: architecture, UX, data flywheel, and business model. ### Key assessment areas | Area | What it evaluates | | - | - | | **Integration Depth** | How central machine intelligence is to the product's core value | | **Personalization** | Adaptive experiences that learn from user behavior | | **Automation** | Intelligent workflows that reduce manual effort | | **Data Flywheel** | Whether usage data improves the product over time | | **Interaction Patterns** | Interface patterns designed for model-led interactions | | **Cost Architecture** | Token economics and inference cost management | ### Product Assessment scoring Product Assessment uses the same 1-5 dimension scoring as Team Operations. It runs as a separate assessment via the `/api/score/product` endpoint or through the platform UI on any scored product's **Product Assessment** tab. > **[Product Assessment API reference](https://docs.dacard.ai/api-reference/scoring/product-assessment.md)** > > Score a product using the Product Assessment framework. *** ## How scoring works When you submit a URL to the scoring API (`POST /api/score`), Dacard.ai: **1. Crawl** The platform visits the URL and reads up to 6 pages, extracting signals from the product's public-facing presence, documentation, and UX patterns. **2. Evaluate** Each of the 27 Team Operations dimensions is assessed on a 1-5 scale based on observable signals. **3. Generate** A composite score (27-135), maturity stage, dimension-level insights, and coaching moves are produced. **4. Store** The result is saved with a shareable link at `/r/{id}`. > [!NOTE] > > Scores can also be generated anonymously without authentication, subject to rate limits. See [Rate Limits](https://docs.dacard.ai/api-reference/rate-limits.md). ## Reading the three frameworks together When all three frameworks score well, the team, the build process, and the product reinforce each other. Common mismatches: | Scenario | Interpretation | | - | - | | High Team, low Lifecycle | Strong team, weak process. Talented team shipping inconsistently. | | High Product, low Team | Capable product, weak ops. Impressive surface, unsustainable operations. | | High across all three | Aligned. The target state. | ## Learn more > **[Score a URL](https://docs.dacard.ai/api-reference/scoring/score-a-url.md)** > > API reference for the Team Operations scoring endpoint. > **[Get a result](https://docs.dacard.ai/api-reference/scoring/get-a-scoring-result.md)** > > Retrieve stored scoring results. > **[DAC chat](https://docs.dacard.ai/api-reference/scoring/ai-coaching-chat.md)** > > Ask DAC for moves based on your scores. 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