After years of hyper-growth and several acquisitions, Sunrun reached an inflection point where its engineering team was struggling to ship. Leadership sensed the problem was technical debt — but ‘we have technical debt’ isn't a management decision. Without a quantified view of how time was actually being spent across a large engineering organization, there was no basis for conviction, no way to weigh tradeoffs, and no clear case for what needed to change.
Parable built a contextual knowledge graph across Sunrun's core systems — pulling from M365, Salesforce, Jira, Git, and other functional tools — into a dedicated virtual private cloud. Rather than just aggregating data, the graph built semantic context around every unit of work: who was in a meeting and why, what a task was actually related to, how it connected to the broader business. The result was a precise, dollar-quantified view of how much of the engineering org's capacity was going to technical debt versus the product roadmap. That quantification drove immediate action — organizational redesign, AI transformation investments, and a roadmap for both — with Parable measuring the impact of each initiative against the original baseline.
Parable's approach began with the fundamental problem: leadership had a hypothesis but no data. The goal of the engagement was to generate a quantified, evidence-based view of how the engineering organization was actually spending its time — not how it reported spending its time.
Data was ingested from Sunrun's core systems — M365, Salesforce, Jira, Git — into a dedicated virtual private cloud. Parable's proprietary context graph processed this data not as raw records but as semantic units: classifying each meeting, task, and commit by its actual business purpose, connecting units of work to each other and to the strategic initiatives they served.
The output was a dollar-quantified breakdown of engineering capacity allocation — specifically, how much was going to technical debt maintenance versus product roadmap execution. This was the first time Sunrun had a precise, defensible basis for a management decision on the engineering structure.
With that quantification in hand, Parable measured the impact of each subsequent initiative — organizational redesign, AI transformation investments — against the original baseline, establishing a feedback loop between investment decisions and observed outcomes.
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