The ELEVATE Framework: Why Poggle Is Built on It

Nick Morgan, Co-founder6 May 2026 · 6 min read

Most engineering teams know they should be improving. Fewer know exactly what to improve, how to measure it, or how to sustain progress over time. Productivity frameworks promise answers, but many are too abstract to operationalise or too narrow to capture the full picture of engineering health.

The ELEVATE Framework takes a different approach. It defines seven pillars of high-performing engineering, each measurable, each actionable, and each connected to real delivery outcomes. Poggle is built on ELEVATE because we believe it's the most complete model for understanding what makes engineering teams effective.

ELEVATE is a structured framework for engineering productivity that goes beyond simple output metrics. Rather than measuring lines of code or story points, it identifies the systemic practices that distinguish high-performing teams from the rest.

The framework defines seven pillars:

• Velocity and Throughput. How quickly work moves through the system. This includes cycle time, deployment frequency, and the pace of iterative delivery.
• Code Quality. The health of the codebase as reflected in review practices, PR size discipline, and the ratio of meaningful changes to churn.
• Resilience. How well a team handles failures, recovers from incidents, and builds systems that degrade gracefully rather than catastrophically.
• Collaboration and Flow. How effectively team members work together. Review responsiveness, knowledge sharing, and cross-team coordination all factor in.
• Focus on Delivering Value. Whether engineering effort aligns with business outcomes. Teams that score well here ship features that matter, not just features that are easy.
• Onboarding and Enablement. How quickly new team members become productive contributors. This measures the team's investment in documentation, mentoring, and accessible systems.
• Progression by Craft. Whether engineers are growing in their skills and taking on increasing levels of technical challenge over time.

Each pillar captures a dimension of engineering effectiveness that, individually, tells you something useful. Together, they provide a comprehensive view of team health.

We evaluated several frameworks before building Poggle. DORA metrics are valuable but narrow, focused primarily on deployment pipeline health. SPACE is comprehensive but designed as a research framework rather than a practical measurement tool. Most internal frameworks we encountered were either too subjective (relying on surveys) or too output-focused (counting PRs without understanding quality).

ELEVATE struck the right balance for three reasons:

It's measurable from observable signals. Most ELEVATE pillars can be assessed from data already flowing through your engineering tools. Pull request activity, review patterns, deployment frequency, and cycle time all feed directly into pillar scores. This means measurement doesn't require surveys, manual reporting, or additional process.

It covers the full picture. A team can have excellent velocity but terrible code quality. They can have strong collaboration but poor resilience. ELEVATE forces you to look at all dimensions simultaneously, preventing the common failure mode of optimising one metric at the expense of everything else.

It's actionable. Each pillar maps to specific practices that teams can adopt, measure, and improve. This isn't an abstract maturity model that tells you to "be better." It's a concrete system where improvement in any pillar corresponds to specific, observable behaviour changes.

A framework on paper is only useful if teams can actually apply it. This is where most productivity initiatives fail. Leaders understand the principles but lack the tooling to measure adoption, track progress, and identify where teams need support.

Poggle bridges this gap by translating ELEVATE pillars into a continuous, automated measurement system.

Automated scoring. Poggle calculates sub-metrics for each pillar based on PR data and engineering activity. These roll up into pillar scores (0 to 100) and an overall ELEVATE score, giving teams and leaders a single view of engineering health.

Goal-setting against pillars. Teams can set goals aligned to specific ELEVATE pillars. Want to improve code quality? Set a target for average PR size or review turnaround time. Want better velocity? Target cycle time reduction. Goals are specific, measurable, and connected to the broader framework.

Trend visibility. A score at a point in time is less useful than a trend. Poggle shows how pillar scores evolve over sprints, quarters, and team changes, so leaders can see whether practices are sticking or slipping.

Team-level and individual insights. ELEVATE pillars apply at different levels. Collaboration is inherently a team metric. Progression by craft is individual. Poggle surfaces insights at the right level, avoiding the trap of measuring individuals with team metrics or vice versa.

Teams that adopt ELEVATE through Poggle typically see measurable improvements within the first quarter. The patterns are consistent across organisations of different sizes and technology stacks.

Cycle time reduction. Teams that focus on the Velocity and Throughput pillar see average PR cycle times drop by 20 to 40% within 8 weeks. The primary drivers are smaller PRs and faster review turnaround, both of which become visible and goal-tracked through Poggle.

Review culture improvement. The Collaboration and Flow pillar makes review responsiveness visible. Teams that set review SLA goals consistently improve time-to-first-review, which has a compounding effect on overall delivery speed.

Reduced quality regressions. The Code Quality pillar tracks PR size, review depth, and churn rates. Teams that actively manage these metrics report fewer production incidents and less time spent on unplanned rework.

Better onboarding. Teams that track the Onboarding and Enablement pillar can measure how quickly new hires reach full productivity. This creates accountability for investment in documentation, pairing, and accessible development environments.

Sustained improvement. The most significant result is sustained improvement rather than temporary spikes. Because ELEVATE measures systemic practices rather than heroic individual effort, improvements tend to compound over time rather than regressing when attention shifts elsewhere.

DORA measures four metrics (deployment frequency, lead time, change failure rate, mean time to recovery) focused on CI/CD pipeline health. ELEVATE includes these concepts within its Velocity and Resilience pillars but adds dimensions like collaboration, code quality, and individual growth that DORA doesn't address.

SPACE (Satisfaction, Performance, Activity, Communication, Efficiency) is a research framework designed to understand developer productivity holistically. It's excellent for framing research questions but wasn't designed as a practical measurement system. ELEVATE is more prescriptive and tool-friendly.

OKRs are a goal-setting mechanism, not a measurement framework. ELEVATE pairs naturally with OKRs: pillars inform which objectives matter, and pillar scores provide the key results.

Adopting ELEVATE doesn't require a transformation programme. Most teams start by understanding their current state across the seven pillars, identifying one or two areas where improvement would have the most impact, and setting specific goals against those pillars.

Poggle makes this process straightforward by calculating your starting scores automatically, highlighting which pillars represent the greatest opportunity, and providing clear goal suggestions based on what high-performing teams in similar contexts have achieved.

The framework meets teams where they are. Whether you're a startup establishing engineering practices for the first time or an enterprise looking to sustain performance at scale, the pillars remain the same. Only the targets change.

Nick MorganCo-founder at Poggle