AI automation
Systems that took over work people used to do by hand — job queues, webhook ingestion, localization pipelines — and kept doing it when inputs got ugly.
For teams replacing manual or no-code workflows with automation that has to survive retries, duplicates and partial failure.
Case studies behind this
A durable job queue that replaced a wall of no-code automations
A growing pile of no-code workflows became hard to observe, hard to retry, and unsafe once external API work started failing mid-run.
Go · Postgres leases · gocron · bearer + HMAC authIdempotent webhook ingestion at scale
Third-party webhooks retried, duplicated, and arrived out of order; silent loss would only surface later as broken reports.
Go · Postgres (SKIP LOCKED, matviews) · HMAC · advisory locksFrame-accurate video localization
Translated speech rarely matches the original duration, so naive AI dubbing drifts away from the picture within minutes.
WhisperX · XTTS voice cloning · Demucs · FFmpeg · CTranslate2Round-trip translation of structured documents without breaking them
Packaged document formats scatter translatable text through XML runs where careless extraction corrupts formatting or loses the path back.
Python XML/ZIP tooling · multi-format CAT interchange
Questions worth asking first
Which parts of this workflow should a human still review?
Where does the system need queues, retries or idempotency?
What is the smallest production slice that proves the business case?
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