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One-time engagement

Release gate

A two to three week engagement that sets up the checks every release of your AI system has to clear before customers see it: the evals, the guardrails, the regression set and the rollback, running in your pipeline and left with your team.

Releases go out when they pass, not when they feel ready.

  • 2 to 3 weeks
  • Priced on the call
  • The gate running in your pipeline
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Is this for you?

  • Releases go out when they feel ready

    There is no test that says a change made the agent worse, so every release is a judgement call.

  • Something good got broken

    A prompt change or a model update made an answer worse for a customer, and nobody saw it until they did.

  • A buyer asked how you test

    Procurement wants to know what runs before a release, and the honest answer is a few people trying it.

Not this? The production readiness audit measures the system once; the gate keeps measuring it on every change; the AI security review tests what the system can be made to do, not what each release changes.

What you get

  1. 01

    The gate

    The checks that run on every change, in your pipeline, with the bar each one has to clear.

  2. 02

    The checks, written down

    What each check tests, where it runs and what a fail means, so the team keeps it going without me.

  3. 03

    The runbook

    What to do when a check fails, how to roll back and who decides, in writing.

Illustrative pages. Yours carry your system’s scores, numbers and fixes.

The runbook

What runs, and when

  1. 1CostFix
  2. 2CostFix
  3. 3CostFix
  4. 4CostFix
  5. 5CostFix

The release gate

Five checks, on every change

  • The eval set
  • The guardrails
  • The regression set
  • The gate itself
  • The rollback

What the gate checks

Five checks a change to an AI system has to clear before customers see it. I set each one up in your pipeline and leave it running.

  1. 01The eval set

    I build the eval set from your real use cases and wire it to run on every change, so a worse answer fails the build.

    I measurePass or fail on every change, against real use cases

  2. 02The guardrails

    I add the guardrail checks to the gate: injection, data crossing and tool scope tested on every release, not only once.

    I measureEach guardrail, pass or fail, per release

  3. 03The regression set

    I keep every answer that once went wrong as a test, so the same mistake cannot ship twice.

    I measureEvery past failure, run again on every change

  4. 04The gate itself

    I put the checks in your pipeline where a release has to pass them, with the bar set and a fail stopping the release.

    I measureThe gate, running, with the bar it holds

  5. 05The rollback

    I set up the rollback and the alert, so when something gets through anyway the customer sees it for minutes, not days.

    I measureRollback and alerts, tried on one real release

2 to 3 weeks, the gate running in your pipeline

The three weeks

  1. Week 1

    Read the pipeline

    Access in place, the pipeline and the release path read, the real use cases collected for the eval set.

  2. Week 2

    Build the checks

    The eval set, the guardrail checks and the regression set built and wired into the pipeline, the bar set with your team.

  3. Week 3

    Run it and hand over

    Real releases through the gate with your team, the rollback tried once, the runbook written. A smaller pipeline finishes at the end of week 2.

The next step

Start with a
20-minute call.

Tell me how releases go out today and what went wrong last time. You leave knowing whether the gate fits, how long it would take and what it would cost.

Bring

  • How a release goes out today
  • The last change that made something worse
  • Your pipeline, in a sentence
Book a 20-minute call Audio, at a time you pick. No deck, no preparation.

Why me

I run releases for a living. At TWINii I introduced spec-driven development, AI-assisted engineering standards and mobile release governance across a distributed team, and I own what ships and when. At Devnagri AI the LLM workflows went out across 22 languages with LLMOps behind them. The gate is the bar I hold my own releases to.

The track record
“The guy thinks 5 steps ahead. He bridges DevOps rigor with AI workflows like it's second nature. Clean infra, predictable deploys, zero drama.”
Kamaljit SinghFounder, IndraOps Technologies

Asked on most first calls

The questions.

How long does it take?

Two to three weeks from the day access is in place. One pipeline and one agent is usually two; several services or several platforms usually three.

What access do you need?

Your pipeline, your test data, a sample of real use cases and your engineers for a few sessions, since they keep the gate running after I leave.

We have no evals yet. Is that a problem?

No. Most teams I meet do not. The gate starts with the first eval set, built from your real use cases during the engagement.

Will it slow releases down?

The checks run in minutes in your pipeline. What they stop is the release that would have cost you a customer, and the week spent finding out why.

Do you sign NDAs?

Yes, yours or mine, before I see anything.

What does it cost?

It depends on the pipeline and the system, so it is fixed on the first call, in writing, before anything starts.

How is this different from the production readiness audit?

The audit measures the system once and hands you a scorecard. The gate keeps measuring it on every change, in your pipeline, after I have gone.

Every way I help

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