AI Production Audit
A 30 minute call and a written report on what stands between your AI work and a reliable production outcome.
Learn moreEngagements
Seven ways in. One production engineering practice.
GatekeeperOps takes AI from pilot to production outcome, and keeps it there. Each engagement below is a different way into that one practice, entered from a production problem a team already has. Most begin against a single system rather than a programme: full system capability, modular engagement scope.
Where to start
Start with the problem you have now.
Nobody buys seven engagements. You start where the work is actually stuck, and scope widens later only if the system in front of it justifies the widening. If none of these is obviously yours, the audit exists to answer that question first.
The situation
Where it starts
A pilot works in a demo and nobody can say what it would take to put it in front of customers.
Production Readiness Foundation
Builds the production foundation around the AI capability: architecture, acceptance criteria, release controls, observability, failure handling and ownership appropriate to the system.
The path to production has stalled. Releases are slow, the signal is not trusted, and every change feels like a risk.
Production Rescue
Finds what is blocking the system from dependable production across architecture, integrations, workflows, controls and operating paths, then repairs what actually matters.
A business workflow crosses several systems, needs judgement at some steps, and should be doing real work rather than sitting in a demo.
Agentic Workflow Automation
Designs and productionizes the workflow across agents, tools, APIs, state, approvals and recovery paths so it can perform real business work safely.
AI changes ship on judgement. There is no point in the pipeline where an unsafe change is stopped by evidence.
Automated Release Gates
Turns release evidence and policy into a repeatable ship, hold or escalate decision inside the delivery workflow.
A system is live, the product and the models keep moving, and nobody would know if its behaviour had drifted.
Continuous Production Operations
Operates the observability, thresholds, drift detection, release controls and incident paths around live AI systems as they change.
Your team could do this work but needs sustained engineering ownership on it, and internal capacity is constrained.
Embedded Reliability Engineers
Embeds senior production engineers inside your repositories, CI/CD and delivery model to own defined production workstreams alongside your team.
You are not sure which of these you have.
AI Production Audit
A 30 minute call and a written report naming what stands between the work and production, ranked. No commitment attached.
The engagements
Three motions. Seven engagements.
The three motions describe what an engagement is for. Every engagement sits under one of them, and all three are delivered through the same seven stages rather than through three separate models.
The way in
Before any of the three, and free. It is how most engagements get scoped, and it commits you to nothing that follows it.
01Productionize AI
Take an AI capability from pilot or prototype to something that holds up in production.
Production Readiness Foundation
The production foundation around an AI capability: architecture, acceptance criteria, release controls, observability, failure handling and ownership appropriate to the system.
Learn moreProduction Rescue
An AI system that stalled on the way to production, or is failing once there: the architecture, integrations, workflows, controls or operating paths blocking it diagnosed and repaired.
Learn more02Automate real workflows
Put AI inside a workflow that takes real actions, with the controls that implies.
Agentic Workflow Automation
AI-driven business workflows designed, integrated and productionized across tools, APIs, systems, state, approvals and recovery paths so they can perform real work safely.
Learn moreAutomated Release Gates
Evidence and release policy turned into a repeatable ship, hold or escalate decision inside the delivery workflow.
Learn more03Operate reliably
Operate the production controls around a live AI system as the product, the models and usage change.
Continuous Production Operations
Operate the production controls around live AI systems as models, data, workflows and usage change: observability, thresholds, drift, release controls, incident paths and recovery.
Learn moreAcross all three
Not a fourth motion. Senior production engineers embedded directly inside your team when the work needs sustained ownership rather than a separately scoped delivery model.
Embedded Reliability Engineers
Senior production engineers working inside your repositories, CI/CD, delivery workflow and operating model to own defined production workstreams alongside your team.
Learn moreQA Automation
Need traditional QA automation?
For teams looking specifically for Playwright, Selenium, automation modernization, release reliability or automation rescue, GatekeeperOps runs a dedicated QA Automation practice.
Explore QA Automation Services(opens in a new tab)How we deliver
Every engagement runs on the same seven stages.
Discover, Design, Build, Integrate, Harden, Deploy, Operate. The motions say what an engagement is for; the stages say how it gets done, and they are not divided between the motions. The depth of each stage is matched to the problem, the risk and who owns the system afterwards, so not every engagement runs all seven.
Read how we deliverScope and pricing
Why there are no rates on this page.
Scope varies more than a rate table survives. A team whose pilot has never been near a release pipeline needs a different engagement from one running a live system that drifts, and both differ again from a team that needs a single workflow made safe enough to take real actions. A published tier would misdescribe most of them.
The audit gives both sides a technical basis for defining scope before commercial terms are proposed. After it, both sides hold the same view of the problem, the scope that matches it, and what sits outside that scope. Price is discussed against that view rather than against a tier.
The only number on this site is the audit, which is free.
What happens before scope is discussed
- ›A 30 minute call led by a senior engineer close to the work
- ›A written report on what stands between the work and production
- ›The gaps that matter, ranked
- ›A recommended starting engagement and its scope
- ›What sits outside that scope unless separately agreed
What you can expect
- ›No charge for the audit
- ›A technical conversation, not a generic sales presentation
- ›Written findings after the call
- ›No commitment to a follow-on engagement
- ›If GKO is not the right next step, the report will say so
FAQ
Common questions.
Getting an AI capability into production and keeping it there. That spans three motions: productionizing AI, so a pilot becomes something that holds up in front of customers; automating real workflows, so AI can take actions against real systems inside sensible boundaries; and operating reliably, so the controls around a live system keep operating as the product and the models change. The engineering underneath is architecture, evaluation, integration, release control, observability and recovery.
No. The delivery model runs Discover through Operate, but an engagement is scoped to the problem rather than to the model. Some work starts where discovery already exists. Some needs deep hardening and light design. Some stops at deployment, with operations handed back to your own team. Full system capability, modular engagement scope.
That is the normal case. Most engagements begin narrow, against a single system or a single workflow, and widen only when the work in front of them justifies it. Buying all seven engagements is not a path anyone is expected to take.
Only where you want that. Continuous Production Operations exists for teams who would rather have a live system operated than staff the function themselves, and it is an option rather than the end of a funnel. Every engagement before it can finish with operations handed back to your team, and the work is written to be owned by your engineers either way.
An agency is generally bought to build a feature. This practice is bought for the part after the demo works: what the system is not allowed to do, how you would know it had drifted, what blocks an unsafe change from reaching users, what happens when the system is wrong, and who is paged. Where we do build, the evaluations and the instrumentation ship as part of the deliverable rather than as a later phase.
The system, its evaluations, its instrumentation and its documentation, in your repositories and your pipelines. The work is done inside your stack as it actually exists, so nothing depends on us continuing to be there.
Start with the production problem you have now.
The AI Production Audit is a 30 minute call and a written report on what stands between your AI work and a reliable production outcome. If the answer is that you do not need us, the report will say so.
Book the AI Production AuditNo charge. 30-min call. Written report. No sales script.