The ELEVATE Framework: Why Poggle Is Built on It

Nick Morgan, Co-founder20 May 2026 · 6 min read

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Most engineering teams know they should be improving. Far fewer know exactly what to improve, how to measure it, or how to sustain progress over time.

The ELEVATE Framework answers that. It defines six 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 six pillars:

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.

• 20 to 40% faster cycle time (Velocity & Throughput). Average PR cycle times drop within 8 weeks, driven by smaller PRs and faster review turnaround that Poggle makes visible and goal-tracked.
• Faster, fairer reviews (Collaboration & Flow). Teams that set review SLA goals consistently improve time-to-first-review, which compounds into faster overall delivery.
• Fewer quality regressions (Code Quality). Tracking PR size, review depth, and churn leads to fewer production incidents and less unplanned rework.
• Quicker ramp-up (Onboarding & Enablement). Measuring time-to-productivity creates accountability for documentation, pairing, and accessible development environments.
• Improvement that sticks. Because ELEVATE measures systemic practices rather than heroic individual effort, gains compound over time instead of regressing once attention shifts.

ELEVATE covers the dimensions other measurement frameworks touch, then adds the ones they leave out.

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 & Throughput and Operational 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 six 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