From workplace signals to a better digital workday
Deploy through your existing IT process, set rules that reflect each team's work, and bring work patterns, device health, and AI adoption into one view. Use the trends to find friction and decide what to improve.
Install a lightweight agent on every desktop
One signed installer per platform — Windows, macOS, Linux — run by hand or pushed silently through your MDM. The agent works in the background at under 2% CPU, capturing locally and reporting to the central dashboard. No network? It buffers the day on-device and defer-syncs the moment the connection returns.
No machine reports until an admin says so
After install, each machine requests registration and lands in a Pending queue. Admins approve or reject every device — or switch on automatic approval for trusted rollouts. The same lifecycle controls handle the exit: remotely uninstall an agent, or disable its login, without ever touching the machine.
Teach Momentum what productive means — per department
Global App Rules mark every application Productive, Non-Productive, or Unmarked. Then departments override where reality differs: a design tool is productive for Design and unmarked for HR; a social app is productive for the Marketing team that runs your accounts. Scores are only as fair as the rules — so the rules are yours.
Set expectations that reflect how each team works
Before the first report means anything, you define what a normal day looks like: workday hours, expected desk hours, target productive hours, office start and end times, and the productivity thresholds that separate green from red. A configured target is a comparison point for trends, not a measure of work quality — it's how you answer 'what changed this week'.
An honest chronological record from first login
Once approved, the agent builds each person's Timeline from their first login of the day: green blocks for active work, red for idle stretches, in chronological order. Alongside it sit the Time Summary pie, App Usage — including offline applications — and Input Statistics: counts of mouse clicks and keystrokes with a per-app distribution. Counts, never content.
Three altitudes of insight, one source of truth
Start with team trends; explore the relevant detail. Timeline answers 'how did this day unfold'. Productivity patterns show focus and app-switching alongside device health and AI adoption metrics, so you can see what changed together. Authorized users can inspect an individual's Timeline to answer 'what's happening on this team'. The Workforce Dashboard answers the organisational question: a Workforce Health Score rolling up productivity, attendance, security, and AI adoption, plus top and bottom performers and departments.
A team's focus blocks shorten over several weeks. Its lead reviews app switching and workload with the team, adjusts meeting routines, then checks the next period's trend.
This describes a pattern you noticed, a change you made, and a way to measure whether it helped — not an outcome Momentum detected for you.
Agent → sync → classification → dashboards
A simple pipeline with one deliberate property: nothing is lost offline, and nothing is scored until it passes through rules you control.
Captures active/idle time, app usage, and input counts locally on Windows, macOS, and Linux.
Online, data streams to your tenant; offline, it queues on-device and back-fills on reconnect.
Every app-minute is weighed against Global App Rules and department overrides, then measured against your baseline settings.
Timeline, Activity, and the org Workforce Dashboard render the same signal at three altitudes.
Exactly what is captured — and what is not
Momentum measures activity, not content. The boundary is technical, not just policy: the agent records counts and categories, never what anyone typed or read.
Built for the people who run the fleet
Every agent reports its own operational telemetry back to the dashboard. Logs stream Info and Error entries per machine, tagged by component — Daemon, Installer, Task Scheduler, Upgrade — so a stuck rollout is diagnosed from your desk, not a desk visit. Resource Usage shows CPU and memory per device, proving the agent stays out of your users' way.
How-it-works FAQ
Does anything get captured before a machine is approved?+
No. After install, the machine requests registration and sits in the Pending state. Only once an admin marks it Approved (or your automatic-approval policy admits it) does the agent begin reporting time, app usage, and input statistics.
What happens when someone works offline?+
The agent keeps capturing locally — active/idle time, app usage including offline apps, and input counts — and performs a deferred sync when the machine reconnects. Timelines back-fill automatically, so a day on a plane still shows up complete.
Can the same app be productive for one team and not another?+
Yes — that is exactly what per-department overrides are for. A Global App Rule sets the default (Productive, Non-Productive, or Unmarked), and each department can override it. Figma can be productive for Design while staying Unmarked for HR.
How is the productivity score calculated?+
Against your own baseline. You define workday hours, expected desk hours, target productive hours, and productivity thresholds in settings; the score compares each person's actual active time in productive-classified apps to those targets. It's one of four pillars — alongside attendance, security, and AI adoption — rolled into the org-wide Workforce Health Score.
What does 'input statistics' actually mean?+
Counts only: how many mouse clicks and keystrokes occurred, with a per-app distribution. Momentum never records which keys were pressed or the content of anything typed — it is an activity signal, not a keylogger.
How do we retire a machine or off-board a person?+
From the dashboard: admins can remotely uninstall the agent from any machine, or disable agent login so the device can no longer report. Historical data stays intact for reporting.