Go-live should be boring. That takes engineering.
Most AI dies at go-live because deployment was treated as a ceremony instead of a system. One heroic push into production, no rollback, no rehearsed failure, and the first incident becomes the last one.
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We build the machinery that makes releases uneventful: automated pipelines from model change to production, rollbacks measured in minutes, and reliability targets someone is actually paged for. The tenth release should feel like the hundredth.
FOR LEADERS WHO
want AI releases as routine as software releases.
FOR TEAMS WITH
a system that works but ships by hand and hope.
FOR PROGRAMS WHERE
the first incident nearly ended the program.
01 What you get
01
The release pipeline.
Model and system changes flowing to production through automated testing, staging, and approval, the same way every time.
02
The rollback plan.
Reversal rehearsed and measured in minutes, so a bad release is an inconvenience rather than an incident.
03
The reliability envelope.
Availability and quality targets defined, monitored, and alerting the right people, with paging that means something.
04
The runbook.
What to do when things go wrong, written for the person on call at 2am rather than for the steering committee.
02 How it runs
Four phases, until releases are routine.
Reliability is a practice, not a purchase. We build it into how you ship.
PHASE 01
Stabilize.
The current path to production mapped and its sharp edges removed: manual steps, snowflake configs, single points of failure.
PHASE 02
Automate.
The release pipeline built: testing, staging, approval, and deploy as one automated flow, with humans at the decisions that matter.
PHASE 03
Rehearse failure.
Rollbacks executed, incidents simulated, alerts tuned, before production traffic makes it an emergency.
PHASE 04
Hand over the routine.
Your team running releases with us alongside, then without us, with the runbook proven in use.
03 Questions we answer
How does a model change reach production safely?
What happens when a release goes wrong at 2am?
How do we test AI behavior before users see it?
What's our rollback, and how fast is it?
How do we ship weekly without breaking what's live?
Who gets paged, and for what?
04 Continue the arc