The methodology

Three frameworks.
88 scoring units.
One diagnostic.

Team Operations measures how your team operates. Development Lifecycle measures how you build. Product Assessment measures what you ship. The diagnostic is the divergence between them.

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◆ Three frameworks
01 · PeopleTeam Operations27 dims · 6 functions
02 · ProcessDevelopment Lifecycle34 tasks · 6 stages
03 · ProductProduct Assessment27 dims · 6 attributes

A strong team doesn't guarantee a strong product. A disciplined build doesn't guarantee what you ship is AI-native. An AI-native product doesn't guarantee the team can sustain it. The diagnostic is the gap.

One team. Three questions.

Each framework answers one question.

The answer to each is a stage between 1 (Foundation) and 5 (Compounding). The three answers together are the diagnostic.

Team Operations
Is your team operating at the level it needs to?
Development Lifecycle
Is your team building AI products the way they should be built?
Product Assessment
Is your product actually AI-native, or is it AI theater?
◆ Team Operations

Is your team operating at the level it needs to?

27 dimensions · 6 functions
Strategy4 dims
Market Intelligence. Decision Quality. Roadmap Discipline. Competitive Positioning.
Design4 dims
Research & Discovery. Prototyping Speed. Experience Design. Design-to-Dev Handoff.
Development4 dims
Architecture & Systems. Spec & Context Quality. Build vs Buy. Delivery Velocity.
Intelligence4 dims
Customer Signal Synthesis. Product Analytics. Data Strategy & Flywheel. Feedback Loop Quality.
Operations7 dims
Knowledge Management. Quality & Experimentation. Team Orchestration. Process & Iteration. Unit Economics. Security & Compliance. Reliability & Resilience.
Go-to-Market4 dims
Positioning & Messaging. Launch Execution. Adoption & Expansion. Pricing & Packaging.
Stages: Foundation · Building · Scaling · Leading · Compounding.
◆ Development Lifecycle

Is your team building AI products the way they should be built?

34 tasks · 6 stages
Stage 1Specify & Constrain
The spec IS the implementation instruction.
Structured spec templates, harness constraints, measurable acceptance criteria, anti-examples for critical paths.
Stage 2Build the System of Context
Your context is your moat.
Context hierarchy, indexing pipeline, multi-model routing, architectural constraints as context.
Stage 3Orchestrate & Generate
Type less. Think more.
Parallel agent delegation, mission control pattern, scope boundaries, token budgets per task type.
Stage 4Validate, Eval & Craft
Truth metrics over vanity metrics.
Eval pipeline before generation pipeline, truth metrics per feature, counter-metric patterns, security scanning, craft review.
Stage 5Ship & Manage Economics
Token budgets alongside sprint budgets.
Cost-per-action tracking, inference cost dashboards, tiered model routing, pricing alignment, pinned model versions.
Stage 6Learn & Compound
Every cycle makes the next one faster.
Post-cycle retros for AI workflow, emergence rate measurement, spec template libraries, cognitive debt tracking, context pruning.
Stages: Specify · Context · Orchestrate · Validate · Ship · Compound.
◆ Product Assessment

Is your product actually AI-native, or is it AI theater?

27 dimensions · 6 attributes
Product Architecture4 dims
Core Integration Depth. Model Strategy. Context Architecture. Agentic Capability.
Adaptive Experience5 dims
Interaction Model. Progressive Disclosure. Adaptive Interface. Confidence Transparency. Human-Product Collaboration.
Learning Systems4 dims
Learning Flywheel. Personalization Depth. Knowledge Architecture. Data Quality & Freshness.
Product Economics4 dims
Cost Per Outcome. Inference Economics. Pricing-Cost Alignment. Value Attribution.
Trust & Reliability5 dims
Hallucination Management. Security Posture. Privacy & Data Governance. Ethical Guardrails. Reliability & Graceful Degradation.
Compound Mechanics5 dims
Network Intelligence. Switching Cost Depth. Expansion Surface. Platform Leverage. Benchmark Community.
Stages: Wrapper · Augmented · Integrated · Native · Compounding.
◆ The tension engine

One framework is a scorecard.
Three frameworks, read together,
become a diagnostic.

The tension engine reads all three frameworks and names the pattern. Every scored team lands in one of three patterns, or in an aligned state below Stage 3.

Translation Gap

Your team is more mature than your product. Capability isn't translating into outcomes.

Triggered when Team Operations stage is higher than Product Assessment stage by one or more stages. The Development Lifecycle axis tells you whether the gap sits in how you build or in what you decide to build.

Fragility Signal

Your product is more AI-native than your team can sustain. Ship velocity may exceed operational maturity.

Triggered when Product Assessment stage is higher than Team Operations stage. The Development Lifecycle axis tells you whether you're shipping fast with discipline, or fast without it.

Compound Ready

Your team, your build, and your product are all above Stage 3 and aligned. Compounding advantage available.

Triggered when all three frameworks score Stage 3 or higher and no gap exceeds one stage. The rarest pattern. The one every team is trying to reach.

The pattern is the diagnosis.

The ranked gap list is the prescription.

The next improvement is the action.

◆ Methodology

How scoring works.

Signals

Dacard reads signals from 50+ tools your team already uses. GitHub commits, Linear issues, Slack channels, Figma files, Notion docs, PostHog events, Sentry errors, website content. Each signal maps to one or more framework units via a calibrated inference map.

Scoring

Each of the 88 units is scored 1 to 5 by an LLM reading the signals against a rubric. The rubric for each unit is public. Every score surfaces the evidence it's based on and the confidence level. Low-confidence units prompt the user to add context.

Calibration

Scoring is anchored by five named archetypes per framework. The archetypes are benchmark teams (AI-First Studio, Eng-Heavy Series A, Pre-AI SaaS, AI Wrapper, Compounding Ops) that define what each stage looks like in practice. New scores calibrate against the archetype distribution.

Theater Detection

Each framework has theater patterns that cap scores when claims exceed evidence. Eval theater, context theater, emergence theater, cost theater, spec theater, harness theater. Twelve patterns across three frameworks. The theater check is automatic. You can't score your way into Stage 4 by claiming what the code doesn't show.

Benchmarks

An initial benchmark cohort of hand-scored companies per framework anchors the distribution. Known AI-native companies, traditional SaaS adopting AI, and design-partner cohort teams. Your score is positioned against the distribution, not against an arbitrary 100-point scale.

Version Lock

Framework v1.0 (April 2026) is locked through Q2 2027. Rubrics and stage definitions don't change during v1.0. Signals underneath evolve continuously. Your Stage 2 in April and your Stage 3 in October are comparable because the rubric didn't change underneath them.

Why three

Why three frameworks.

Most measurement tools cover one framework. Jellyfish measures engineering (one of six Team Operations functions). LinearB measures dev workflow. Swarmia measures engineering effectiveness. Amplitude measures product usage. Each answers one question.

Three frameworks answer a different question: where's the divergence?

Team at Stage 4 and product at Stage 2 isn't the same diagnosis as team at Stage 2 and product at Stage 2. Same product score, different prescription. That's what a single-framework tool can't tell you.

Development Lifecycle is the newest framework. It's what turns two-framework tension analysis (team vs product) into three-framework diagnosis (team vs build vs product). Whether the gap is in what you decide or how you build is a different problem with a different fix.

Three frameworks is the minimum you need to name the pattern correctly.

Two tell you there's a problem.

Three tell you which problem.

See your pattern.

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