Understand how interruptions, work patterns, and digital tools affect engineers' ability to focus—then give teams more room for deep work.
Engineering work depends on sustained focus. Momentum shows patterns in app use, active time, and context switching so teams can investigate what fragments the workday.
Identify when meetings and interruptions are consuming engineering focus.
See productive hours and expected-vs-actual patterns across engineering teams.
Identify fragmented workdays and recurring interruption patterns.
Identify managed devices that need IT attention alongside team work patterns.
Illustrative outcome, modeled from typical rollouts — not a specific customer result. See real customer results →
Engineering output depends on long uninterrupted blocks, and most engineering days don't have any. Standups, pings, and "quick calls" slice the day into twenty-minute fragments; velocity charts show the slowdown but not the cause. Worse, generic monitoring insults engineers — a terminal, a local VM, or Stack Overflow reads as "browsing" to a tool that doesn't understand the work, and the team rejects it on day one.
In Momentum, the Engineering department gets its own app productivity rules: IDEs, terminals, Git clients, and docs are Productive for engineers even where the global default says otherwise — the same override logic that marks an HR tool productive for HR and not for Engineering. The Timeline then tells the honest story: green active blocks against red idle, App Usage that captures offline tools like local IDEs, and Input Statistics per app. The Activity view scores each day against the target productive hours you set — no screenshots, no keylogging content, just classified time.
After a thirty-day baseline, patterns become visible on the Workforce Dashboard. If focus blocks shorten during a meeting-heavy sprint, review the team's schedule, try protected mornings, and compare the pattern next sprint. The data shows whether the intervention actually worked — and the same evidence goes into planning the next cycle.
Momentum never asks you to judge on day one. Collect a month of Timeline data, then set targets from your own expected-vs-actual trend — and watch it move.
Every one of these lives on the Momentum Workforce Dashboard — no exports, no spreadsheets. Set your workday, expected desk hours, and target productive hours in General settings, and watch the expected-vs-actual gap close week over week.
A proven rollout sequence using real Momentum modules — agents, machine approval, department app rules, and the dashboard.
Momentum brings the signals that matter into one fair, real-time view — so leaders can act on evidence and teams can trust the numbers.
Illustrative dashboard mockup — figures are not from a specific customer.
Illustrative dashboard mockup — figures are not from a specific customer.
See which engineering teams are adopting AI assistants and where training or approved-tool guidance may help.
Spot adoption patterns and target enablement where it matters most.
Explore AI Adoption Analytics →No screenshots. No code capture. No keylogging content. Momentum measures classified work activity so leaders can understand focus and workload without inspecting what engineers actually write.
Yes. The agent tracks App Usage including offline applications, and department rules let you mark any engineering tool Productive — so a day deep in a terminal scores like the real work it is.
No. Input Statistics record aggregate activity counts — clicks and keystrokes as numbers, overall and per app — which distinguishes active work from idle time without ever reading a single line of code or keystroke content.
Yes. Team leads see analytics for their own team only, and each engineer sees only their own Timeline and score — nothing is broadcast to peers.
The agent is deliberately lightweight, and the Resource Usage module shows its CPU and memory footprint per device — so you can verify the overhead on your own fleet instead of taking our word.
