Your best senior engineer just left. Exit interview: "I wanted to build things, not spend half my time answering support questions." You had no idea support was consuming that much time.
The Problem
Your engineering team handles customer support escalations. It's part of the culture—engineers own their products. But how much time does support actually consume? You don't know. Some weeks feel heavy with support. Other weeks feel like pure development. But you have no data. You can't tell if support is 10% of engineering time or 40%. You can't tell which engineers are carrying the support load. You can't identify recurring issues that should be fixed in the product. The reality is support work is invisible. A Slack thread helping a customer doesn't show up in Jira velocity. An urgent bug fix triggered by a support ticket looks like any other bug fix. A late-night deployment to fix a customer issue doesn't register as "support" in any system. Meanwhile, some engineers feel overloaded with support while others coast on feature work. The burden isn't distributed fairly, but you can't see who's carrying what. Product asks: "Why is feature velocity down this quarter?" You suspect support load increased, but you can't prove it.
How It Cascades
Engineers burn out on invisible support work. Your best people leave for companies where they can focus on building. The ones who stay resent carrying the support burden.
You can't make informed staffing decisions. Should you hire dedicated support engineers? Expand the team? Change the support model? Without data on current support workload, any decision is a guess.
Product improvements get missed. Maybe the same integration issue comes up 50 times. You could fix it once and save months of support time. But you don't see the pattern, so the issue persists.
Support costs stay hidden. Leadership sees headcount and salaries. They don't see that 30% of that cost is actually going to support, not development. Resource allocation becomes disconnected from reality.
The support burden grows quietly. More customers mean more support. But it happens gradually, so nobody notices until engineers are drowning and velocity has crashed.
The Insight
The issue isn't that engineers shouldn't do support—many companies make that model work. The issue is that support work is invisible, unmeasured, and unoptimized. Without visibility, you can't balance support vs development, identify recurring issues to eliminate, distribute load fairly, or make informed decisions about support structure. What's needed is comprehensive tracking of support activity across all channels where it happens.
"One of the main reasons I am particularly attentive to this conversation is the developer experience. We have some homebrew thing to track incidents. Understanding our support patterns and optimizing our support structure is another goal for us."
The Solution
Maestro tracks support activity automatically: support-tagged Jira tickets (time to resolve, volume, patterns), Slack conversations in support channels (who's responding? how often? how long?), customer-facing bug fixes (which engineers handle escalations?), weekend and after-hours support work (who's taking the burden?), recurring issues (which problems keep coming up?). You pull up the support dashboard and finally see the truth. Your engineering team spent 32% of their time on support last quarter. But it's not distributed evenly. Three senior engineers carry 60% of the support load. Two engineers do almost no support. The imbalance is stark. More importantly: one API integration issue has generated 47 support tickets this quarter. If you fixed that integration properly, you'd eliminate a major support drain. You take action: Rotate support duties more fairly to prevent burnout on the three carrying the load. Prioritize fixing that recurring API integration issue. Hire one dedicated support engineer to handle tier-1 issues, freeing senior engineers for complex escalations only. Three months later: overall support load down to 22% as the API fix eliminates recurring issues, support distribution more even across the team, feature velocity up 25% because engineers have more focused time, engineer satisfaction up 30 points. One leader said: "Understanding our support patterns and optimizing our support structure was a major goal. Maestro gave us visibility we never had before."
The Outcome
Engineering organizations quantify support workload accurately, identify and eliminate recurring support drains, distribute support burden fairly to prevent burnout, make informed decisions about support staffing and structure, and optimize the balance between support and development work.