From intention to behavior.
Momentum is a contextual agent, not a chatbot. It turns a goal like “work on my project for 90 minutes” into a structured focus session — then watches your real working context, catches drift the moment it happens, and intervenes before the moment is lost.
No install for this preview — the live demo below simulates a real session end-to-end.
Focus session
42:18
remaining · 90 min planned
Goal progress
68%
Current context
github.com
Focused
Every productivity app still assumes you have the discipline it's meant to give you.
Set goals, build tasks, track behavior, read the dashboard, decide what to do next — the tool only helps if you're already disciplined enough to use it correctly. Momentum inverts that: it understands the context you're working in and acts inside it.
Traditional approach
Momentum
“You should focus more.” — generic advice with no awareness of what you're actually doing right now.
“You planned to finish auth today. You've been on YouTube for 8 minutes.” — then it offers to act.
Meet the agent — not a chatbot.
No chat window, no prompt box. Under the hood it's a small, boring loop: look at what's happening, decide if it's worth saying anything, and — rarely — ask to act.
Reads context, not conversation.
It never waits for you to type — it watches your goal and your environment.
Silence is the default.
Most sessions get zero messages. It only speaks when a rule actually fires.
Never acts without you.
Every intervention ends in a choice you make, never a command it runs alone.
how the agent actually thinks →
One loop, closed end to end.
This is the entire MVP surface area: a single loop, demonstrated completely, rather than a dozen half-built features.
Goal
“Finish my project MVP in the next 90 minutes.” Momentum parses intent, target date, and daily focus target.
Context
The Context Engine turns raw events — tab changes, idle time, elapsed minutes — into a structured state the agent can reason over.
Agent
A decision engine evaluates that context against the active session and decides whether an intervention is actually warranted.
Intervention
A specific, human message — never a generic nudge — proposing a concrete next action.
Action
With your approval, Momentum acts: returns to your workspace, starts a break, or ends the session cleanly.
Measurement
Every session produces analytics that feed back into the model of how you actually work.
Run a focus session, right here.
This widget compresses a full session into about 30 seconds. Pick a goal, start the session, then switch into YouTube or Instagram to see Momentum notice and intervene.
Work balance
Duration
Work / activity balance
Intervenes sooner — a short grace period before it speaks up.
Today's progress
2 sessions so far
The dashboard is the control center. Two lightweight companions feed it.
The live demo above simulates context in the page. In real use, that same context — and the same intervention loop — is produced by a desktop app and a browser extension talking to this dashboard.
Desktop app
macOS & Windows
Runs quietly in the background to read system-level signals — active window, idle time — the context a browser alone can't see.
Start nowBrowser extension
Chrome, Edge & Brave
Watches tab and domain changes in real time and is what powers the distraction detection you just tried above.
Get the extensionBoth companions are early access — join now and we'll notify you the moment your platform is ready.
Raw events in. A reasoning-ready state out.
The agent never sees a firehose of tab-switch events — it sees a compact, structured snapshot of what's actually happening.
Raw events
{
"goal": "Build MVP",
"session_duration": 90,
"elapsed": 32,
"current_domain": "youtube.com",
"productive": false,
"distraction_duration": 420,
"last_productive_activity": 180
}The agent decides whether to speak at all.
Most of the time, the right move is silence. An intervention only fires when the rule actually matches — and the message is generated from the live context, not a template.
the whole pipeline, in one breath →
Trigger rule
IF focus_session = active AND current_domain = distraction AND distraction_duration > threshold THEN generate intervention
Generated intervention
“You planned to finish the authentication module today. You've been on YouTube for 8 minutes. Want to head back?”
Real actions, not just messages.
The MVP demonstrates four concrete actions the agent can take in your environment — each one reversible, each one visible.
Return to work
Hides the distraction and brings back the last productive site.
Open workspace
Opens the project, its docs, and the relevant GitHub repo in one move.
Pause
The user can always ask for a break — the agent never overrides that.
End session
Closes the session cleanly and saves everything for analytics.
Momentum should never feel like it's controlling you.
Sensitive actions always require your explicit approval. Every capability the agent has can be scoped, reviewed, and revoked — permissions are a first-class part of the product, not an afterthought.
Momentum wants to:
- Close YouTube
- Return to GitHub
- Continue focus session
Every session ends with a real answer, not a streak counter.
Planned vs. actual, where the time actually went, and when you were at your best today.
Today's session
79%
Planned
90 min
Productive
71 min
Distraction
12 min
Idle
7 min
Most productive period: 09:15–10:05 & 11:00–11:45
Main distraction
YouTube
Interruptions
3
Task progress
Authentication → 70%
Next recommended action: finish the OAuth callback tomorrow morning.
Tracking data becomes behavioral intelligence.
At the end of the day, Momentum turns raw session data into an actual observation and a recommendation — the beginning of a real behavior model, not just a report.
You planned 3 hours of focused work. You completed 2h17.
Your strongest session: 10:00–11:30
Your biggest distraction: YouTube
Observation
Your focus decreased significantly after 16:00.
Recommendation
Schedule difficult tasks before 15:00 tomorrow.
Small surface area, real feedback loop.
Six layers, one direction of data flow, and a store that turns every session into training signal for the next one.
User
Sets a goal, starts a session, approves actions
Web app
Next.js dashboard, goals, live session view
Context engine
Events, session state, user goals
AI agent
Reasoning, decision, intervention
Action layer
Browser, timer, notifications
Event / data store
Everything the model learns from
Same loop, different intentions.
Goal, context, agent, intervention, action, measurement — the loop doesn't change. What changes is what counts as drift.
Deep work
“I want two uninterrupted hours.” Momentum sets up the workspace, watches context, and intervenes on drift.
Learning
“I want to learn Python.” Momentum clarifies the goal, finds resources, and builds a path with real sessions.
Procrastination
15 minutes into a session, the agent notices you're on social media and names exactly how long you've been gone.
Stuck, not distracted
No tab switch, no progress for 18 minutes. Momentum asks if you're stuck and offers to look at the problem with you.
End-of-day reflection
Completed goals, abandoned ones, distractions, and the periods where you actually did your best work.
Weekly review
Focus +18%, best day Tuesday, 6/8 goals completed, and a pattern: you perform better before 14:00.
This is behavioral data. It has to be treated like it.
The user owns the data. Full stop. That principle is a design constraint, not a footnote.
Transparency
You always know what's collected and why.
Consent
Every capability is explicitly granted, never assumed.
Control
Disable any context source — browser, calendar, anything — at any time.
Data minimization
Only what the loop actually needs, nothing collected on speculation.
Local-first
Raw events can stay on-device; only summarized context reaches the model.
No hidden surveillance
A personal tool for you — never a monitoring layer aimed at you.
The MVP proves one loop. The vision is bigger.
From a focus agent to a system that optimizes the gap between the life you want and the behavior you actually have.
Focus agent
- Goals
- Focus sessions
- Browser context
- Distraction detection
- Interventions
- Analytics
Personal productivity agent
- Calendar
- Tasks
- Slack / Teams
- Notifications
- Documents
Personal learning agent
- Research
- Learning paths
- Adaptive learning
- Spaced repetition
- Resource recommendations
Life operating system
- Calendar
- Tasks
- Browser
- Work apps
- Learning
- Personal goals — one behavior model
The same loop works inside a company, not just for one person.
Momentum's core mechanic — watch real activity instead of trusting what people self-report — happens to solve one of HR's oldest problems: nobody has real visibility, so promotion, training, and mobility decisions ride on one manager's judgment, once a year.
75%
of employees leave without ever getting a single promotion
ADP Research Institute
$35M/yr
spent on review cycles at a 10,000-person company
CEB / Washington Post
14% → 77%
feel inspired to improve after an annual review vs. after continuous feedback
CEB, SHRM
69%
of companies use no AI at all in their evaluation process
industry survey
Activity optimization
The agent reads real workload, task complexity, and project contribution — not a status update someone typed at 5pm.
Talent management
That activity data feeds reviews, targets training at real skill gaps, and surfaces internal mobility before someone quits to get it elsewhere.
The whole thing lives or dies on trust.
42% of monitored employees are job-hunting within a year, vs. 23% of those who aren't. But 90% accept the exact same data collection when it's tied to a concrete career benefit. Same principle as the personal product: transparency and a real benefit for the person being observed come first — never bolted on afterward as a compliance checkbox.
| Issue | Today | With the agent |
|---|---|---|
| Talent detection | One manager's yearly judgment call | Continuous, objective tracking |
| Training | Generic, based on stated wishes | Targeted, based on real activity |
| Internal mobility | Barely visible, decisions come late | Detected proactively, before someone leaves |
| Avoidable turnover cost | Lost talent that was never promoted (75% of exits) | Reduced through real visibility |
Stop measuring intentions.
Start acting on them.
Momentum from intention to behavior — one focus session at a time.