Productivity
PeakTrack
How it works
Habits-as-graph system where automation behaves politely and integrations stay deterministic.
What we designed
Productivity tools either nag users into churn or bury operators under opaque background jobs nobody can replay.
PeakTrack exposes the habit graph to users and builders alike—schedulers announce intent, backoff policies respect focus windows, exports stay diffable.
Declarative workflows
Every nudge declares trigger, throttle, escalation, and quiet hours—the UI mirrors the YAML contract powering workers.
Concurrency with receipts
Jobs produce signed receipts consumed by dashboards so client teams know who saw what ping and when retries occurred.
Hybrid human + automation
Escalations open human-in-loop tasks instead of spawning duplicate automations—a single queue drains both.
- 01
Graph modeling session
We capture entities (habits, cohorts, devices) plus SLAs—the graph schema ships as migrations with migration-time validation.
- 02
Dry-run rehearsal
Synthetic users march through timelines; golden fixtures assert notification copy, locales, and token spend.
- 03
Fleet rollout lanes
Tenant rings graduate from synthetic → pilot → GA with automated rollback thresholds on error budgets.
- 04
Operational theater
Live boards track queue saturation, suppressed notifications, and manual overrides with immutable audit stubs.
Under the hood
GraphQL façade over relational habit store, Temporal-style durable timers, SSE channels for desktops, JWT-scoped integrations.
Stack highlights
Illustrative composite based on recurring studio patterns—not a testimonial tied to any single deployed client.