Simas Razinskas

Production reliability

What keeps a system standing when something goes wrong: health-gated deploys that roll themselves back, ingestion that survives duplicate events, isolation that holds between tenants.

For teams whose AI or platform systems need deployment safety, data integrity under retries, and structural isolation between tenants.

Case studies behind this

Questions worth asking first

  • What happens to production traffic when a release goes wrong?

  • Which data or event paths must survive retries without duplication or loss?

  • Where does tenant isolation need to be structural, not a convention?

Related services

LLM productionization

Start here when the prototype is convincing but nobody can say what it costs at scale, why it fails, or whether yesterday's prompt change made it worse.

Open service

Have a system that has to work?

Book a call and we'll map it out: what it should do, where it will break, and the smallest version worth building first.