Simas Razinskas

AI automation consulting

For workflows that cost too much manual time and can't afford an automation that guesses. Find the safest path from a person doing it to a system doing it.

Start here when a manual process is eating senior hours, a no-code chain has hit its ceiling, or an AI idea needs a build plan before it gets budget.

When this fits

  • Manual review, routing, enrichment or reporting is consuming hours your senior people don't have.
  • A no-code automation proved the demand — and now fails on retries, state or volume.
  • You need a build-vs-buy answer before committing budget to an implementation.

What an engagement looks like

  • A workflow audit that ends in a risk map and a first production slice.
  • Architecture for the unglamorous parts: queues, idempotency, approvals, observability.
  • Prototype rescue — keeping the demo that works, rebuilding the backend that doesn't.

What you leave with

  • A system boundary narrow enough to ship and defend.
  • Failure modes named before the code becomes something the business depends on.
  • An automation that survives retries, duplicate events and partial failure — because production sends all three.

Case studies behind this

AI automation

For teams replacing manual or no-code workflows with automation that has to survive retries, duplicates and partial failure.

Open topic

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.