Team Operations framework

Deep dive into the 27-dimension, 5-stage Team Operations assessment.

Team Operations is the primary framework. We score your team across 27 dimensions in 6 functions, each 1-5, for a composite of 27-135. The composite places you on the 5-stage Team Operations ladder: Foundation, Building, Scaling, Leading, Compounding.

Team Operations framework

Team Operations is the primary scoring framework. It reads a team’s operational capability across 27 dimensions in 6 functions, each scored 1-5, for a composite of 27-135. It answers the question: how capable is your team at building and shipping?

Five maturity stages

1. Foundation (27-48)

   Basics missing across most dimensions. Team hasn’t yet established consistent practice. Most work is manual, ad hoc, or driven by individual contributors rather than shared systems.

2. Building (49-70)

   Practice exists but lands unevenly. Some functions are stronger than others. Team is experimenting with new workflows but lacks repeatability and measurement.

3. Scaling (71-91)

   Systematic process with measurable outcomes. Team ships features repeatedly and reliably. Data loops are starting to compound.

4. Leading (92-113)

   Deeply integrated practice across all functions. Capability is embedded in the team’s ways of working, not just the product. Distance from Building-stage peers is meaningful and widening.

5. Compounding (114-135)

   Self-improving systems. Every function reinforces every other. Team gets better with every cycle, creating advantage that’s hard to copy.

Six functions, 27 dimensions

Dimensions live in six functions that map to the full product lifecycle.

Strategy

How well the team reads its market and makes evidence-based calls.

Dimension	What we measure	Score 1	Score 5
Market intelligence	Quality of market signal collection and synthesis	No systematic market read	Continuous competitive intelligence with real-time synthesis
Decision quality	Evidence behind product and strategic decisions	Gut-feel decisions, undocumented	Structured decision frameworks with measured outcome tracking
Roadmap discipline	How well the roadmap reflects strategic priorities	Roadmap driven by stakeholder requests	Outcome-based roadmap with clear OKR linkage and regular pruning
Competitive positioning	Clarity and defensibility of position	No differentiated position	Compounding position that deepens with scale and is hard to replicate

Design

How effectively the team turns user insight into shipped experience.

Dimension	What we measure	Score 1	Score 5
Research and discovery	Depth and consistency of user research	No research practice	Continuous discovery with rigorous synthesis and opportunity trees
Prototyping speed	How fast the team goes from idea to testable artifact	Weeks to a prototype	Same-day prototypes with real user feedback loops
Experience design	Quality of interaction patterns and UX craft	No coherent interaction model	Interactions feel native, adaptive, and refined
Design-dev handoff	Efficiency and fidelity of the design-to-build handoff	Manual specs with high loss-in-translation	Automated handoff with design system coverage and zero spec debt

Development

How efficiently and consistently the team builds and ships.

Dimension	What we measure	Score 1	Score 5
Architecture and systems	Depth of capability integration in the technical stack	No capability in the stack	Models, pipelines, and inference are core to the architecture
Spec and context quality	Quality of PRDs, tickets, and context handed to builders	Vague specs, high ambiguity	Rich context, acceptance criteria, examples
Build vs buy	Decision rigor on model and infrastructure choices	No framework for build vs buy	Principled model with clear criteria, regular review, measured outcomes
Delivery velocity	Speed and consistency of shipping improvements	Quarterly releases	Continuous deployment with assisted review, testing, and rollout

Intelligence

How well the team captures, organizes, and uses customer and product signal.

Dimension	What we measure	Score 1	Score 5
Customer signal synthesis	Quality of feedback collection and synthesis	No systematic feedback collection	Synthesis of all customer signal into actionable intelligence
Product analytics	Depth and use of usage data	No analytics instrumentation	Real-time anomaly detection with surfaced insight
Data flywheel	Whether data creates a defensible compounding advantage	No data strategy	Proprietary flywheel: usage generates data that improves the product
Feedback loop quality	Whether usage data flows back to improve the product	No feedback mechanism	Real-time signal loop from user to model to product improvement
Knowledge management	How well institutional knowledge is captured, organized, surfaced	No systematic capture	Knowledge captured, organized, and distributed automatically

GTM

How effectively the team takes products to market and drives adoption.

Dimension	What we measure	Score 1	Score 5
Positioning and messaging	Clarity and resonance of market messaging	Generic or feature-based positioning	Outcome-focused positioning with clear differentiation
Launch execution	Consistency and quality of launches	Ad hoc launches with no playbook	Repeatable launch system with pre/post analytics and clear success metrics
Adoption and expansion	How effectively the product drives growth and expansion	No adoption strategy	Onboarding, expansion loops, and retention flywheel
Pricing and packaging	Whether pricing reflects value and supports growth	Traditional seat-based pricing	Usage or outcome-based pricing that scales with value delivered

Operations

How well the team learns, adapts, and optimizes its own ways of working.

Dimension	What we measure	Score 1	Score 5
Quality and experimentation	Rigor of testing, evaluation, and quality processes	Manual QA with no eval pipeline	Automated eval pipelines with continuous quality monitoring
Team orchestration	How the team coordinates work across people and systems	Manual planning with poor visibility	Real-time capacity and dependency tracking
Process iteration	How fast the team improves its own ways of working	Processes are fixed until broken	Continuous process improvement with data-driven retrospectives
Cost and token economics	How the team manages inference and infrastructure costs	No awareness of cost	Active token budget management with cost-per-feature analysis
Security and compliance	How the team manages security and compliance risk	No security practice	Threats detected, vulnerabilities patched, compliance maintained continuously
Reliability and resilience	How features handle failure and maintain availability	No specific monitoring	Failures predicted before they occur and infrastructure adjusted preemptively

How dimensions interact

Dimensions compound within and across functions.

• Data flywheel + feedback loop quality = the compounding engine. Strong data feeds strong models, which generate more useful data.
• Architecture and systems + delivery velocity = the delivery engine. Deep integration enables fast iteration.
• Market intelligence + competitive positioning = the positioning engine. Clear market read produces defensible differentiation.
• Customer signal synthesis + product analytics = the intelligence layer. Strong collection plus strong analysis produces actionable read.

Scoring criteria

We score each dimension 1-5 based on observable signal:

1. Public evidence - what the product shows, says, and does externally
2. Technical signals - architecture patterns, API design, infrastructure choices
3. Business model signals - pricing structure, packaging, monetization
4. Team signals - job postings, engineering blog, conference talks
5. Adapter signals - operational ground truth from connected tools (GitHub, Linear, PostHog)

Using your scores

For individual contributors

Pick the function most relevant to your role and focus on its 4 dimensions. A PM leans on Strategy and Operations. An engineer leans on Development and Intelligence. Your function score is your primary growth lever.

For product leaders

Use the dimension heatmap across your portfolio to surface systemic weakness. A dimension that scores low across multiple products is an org-level capability gap, not a team problem. Invest in org-wide training and tooling, not individual coaching.

For founders and executives

Compare your Team Operations score against your Lifecycle and Product Assessment scores to surface the biggest tension. A high product score with a low team score means your product has more capability than your team can sustain. That’s a scaling risk, not a strength.

Related

Three frameworks

How the three frameworks fit together.

Score your product

Run an assessment and read your 27-dimension breakdown.

Read your report

Walk through the maturity report.