If you're reading this, you already know your product and engineering teams can't deliver on commitments. Sprint after sprint, releases keep slipping. You've pushed the team harder, tried new frameworks, and maybe even hired new people. Nothing works.
The friction between product and engineering, the reorgs that don't stick, the feeling that you can't quite put your finger on what's actually broken—these are patterns I see across growth-stage companies. You're not crazy to think something's fundamentally wrong. You're right.
Here's what's happening: The root causes of delivery failure are systemic patterns that are invisible when you're operating inside the organization. Even exceptional leaders can't diagnose their own team's dysfunction—you're too close to see the patterns you're living in every day.
A two-week forensic analysis that identifies why your product and engineering teams can't deliver on commitments—then gives you the blueprint to fix it.
This diagnostic combines meeting observation, communication analysis, work system investigation, data analysis, and team interviews to find the root causes invisible from inside your organization. Then I translate those findings into specific, actionable steps you can take to get back on track.
Meeting Observation I attend your sprint planning, standups, backlog grooming, retrospectives, and other relevant meetings. I'm looking for patterns in decision-making, communication breakdowns, and gaps between what's being said and what's actually happening.
Communication Analysis I analyze your team's actual communication patterns—code review comments, Slack channels, work management threads. Not to judge individuals, but to identify systemic issues: where teams get blocked without speaking up, where information doesn't flow, where friction appears.
Work Management System Investigation I investigate your Jira, Linear, or whatever system you use to track work. I'm analyzing how work actually flows versus how it's supposed to flow—ticket aging, cycle time patterns, bottlenecks, where work gets stuck, and why.
Data Analysis I analyze your actual metrics: