Every team change has second-order effects. Now you can see them.
Hiring someone, losing someone, splitting a team, merging two teams — these decisions affect velocity, knowledge distribution, review capacity, onboarding load, and a dozen other dimensions. Gitrevio simulates the impact using your actual team data.
Eight scenario types
Deep dive: departure simulation
A departure simulation maps the full blast radius — not just the person's direct output, but everything that depends on them. Review load redistribution, knowledge gaps, mentorship disruption, and the cascading effects that compound over weeks.
The recovery path turns a crisis into a project plan. Instead of scrambling when someone gives notice, you have a prioritized list of mitigations with time and cost estimates for each.
Run departure simulations proactively for your highest-risk team members. Combined with attrition risk scoring, you can have recovery plans drafted before a resignation even happens.
Hiring simulation: the other direction
Adding someone to a team does not immediately make it faster. There is an onboarding drag period where the new hire consumes senior engineers' time while ramping up. The simulator models this honestly.
You see the break-even point — the week where net velocity turns positive. For a senior hire on a well-documented codebase, that might be week 6. For a junior hire on a complex monolith, it could be week 14.
The simulator also recommends the optimal hire profile. Based on current knowledge gaps, review bottlenecks, and technology stack, it tells you what skills to prioritize in your next hire.
Built on your real data, not generic benchmarks
Every simulation runs against your actual team data — commit history, review patterns, knowledge graph, sprint performance. The model knows who reviews what, who owns which services, and how your specific team dynamics work.
The knowledge graph computes dependency and blast radius for every engineer. When you simulate a departure, the model traces every service, repository, and workflow that person touches — including indirect dependencies other tools miss.
Simulations use Monte Carlo sampling to give probability distributions, not single-point estimates. You get P50, P75, and P90 outcomes because the future is uncertain and pretending otherwise is dishonest.
Results improve over time as Gitrevio accumulates more data about your organization. The first simulation after setup uses 90 days of history. After a year, the model has seen seasonal patterns, team evolution, and real departure outcomes to calibrate against.