CASE STUDY · 2025 · SAAS
SATELLITE
Observability for long-running jobs, built for teams that ship at night.
- ROLE
- Founding Engineer
- YEAR
- 2025
- STACK
- Next.js · Prisma · Postgres · GraphQL
PROBLEM
When a nightly pipeline drops a single batch, the on-call engineer learns about it from a customer ticket — usually three hours after the failure, often because a downstream report shipped wrong.
Existing observability tools optimize for request-response systems. They do not understand a job that takes nine hours, retries on a non-idempotent step, and emits zero structured signal between stage boundaries.
APPROACH
Modelled the domain around the run, not the request — a typed timeline of stages, retries, and side-effects with first-class anomaly notation.
Built the agent thin: ten kilobytes, no daemon, no required config — just a function call that wraps any block and ships its envelope to the bus.
Designed the control surface as a single dense screen — every run on a vertical, every failure annotated in line, no drilling into modal pop-ups to find the why.
OUTCOME
Median time-to-detection on critical pipelines moved from 2h47m to 6 minutes across the first three pilot teams.
Two of those teams replaced their internal Slack-bot watchdog stack entirely.
Currently in private beta with eleven companies; first paid customers Q1 2026.
CREDITS
- Engineering
- João M. Mota
- Design
- João M. Mota
- Brand
- Estúdio Linha
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