AI Production Engineering
We productionize AI systems, automate the workflows around them, and operate them reliably in production. From agents and RAG applications to APIs, business systems, release controls and production operations, we engineer the production gap that matters most.
An animation of business operations reaching production. Fragmented systems, records, tickets, approvals, API calls and human tasks move in conflicting directions, retrying, stalling and duplicating. AI orchestration is introduced inside that work and the flow resolves onto one path. An agent takes a bad route and is caught by a control, then a fallback returns it to the healthy flow. Continuous observability comes online, and the system settles into steady, evenly paced production.
30-min call. Written report. No sales script.
What we do
Everything we do sits in one of three places along the same arc. Reliability is what makes each of them hard, and it is what we are actually selling in all three. An engagement can start in one of them and span the others only when the system needs it.
01
Turn an AI capability that works in a demo into a system your product can depend on.
A service with defined behaviour, an evaluation suite that states what correct means, and a deployment path your team can run without us.
A demo works once, on inputs you chose. Production works on whatever arrives, after the model changes underneath it.
Systems: Agents, RAG applications, LLM features, model endpoints, retrieval and data pipelines.
02
Automate the work around the model: the business process it sits inside, and the engineering process that ships it.
Manual work runs on its own, agent actions stay inside defined boundaries, and changes reach production through an explicit ship, hold or escalate decision.
Automation multiplies whatever it is given. Without controls it does the wrong thing faster, and at greater volume.
Systems: Operational workflows, tool use and actions, API and business system integration, CI pipelines, release gates.
03
Run it after launch, watch real behaviour, and operate the production controls as the system changes.
Problems surface as signals to your team instead of as tickets from your customers, and the system keeps working as your product changes around it.
Launch is the start of the work. AI behaviour moves after release, and nothing tells you unless you built the thing that tells you.
Systems: Production monitoring, evaluations against live traffic, drift detection, incident paths, ownership and escalation.
Three motions. One moat. Anyone can demo; reliability is what separates a pilot from a product.
Where it breaks
These are the failures we are called in for. They are specific, they are structural, and none of them are solved by a better model.
01 coverage
The demo convinces the room. Nobody can say what happens on the inputs nobody tried, so it does not ship, or it ships and nobody sleeps.
02 stutter
The AI works. The path between it and your actual systems does not: auth, rate limits, retries, partial failures, schema drift.
03 discontinuity
The model sits beside the business process instead of inside it. Someone copies output from one tool into another, and that person is the integration.
04 silent step
A model version moves, a prompt is edited, embeddings are rebuilt. Behaviour changes and no test catches it because no test describes the behaviour.
05 excursion
An agent can call tools, write records, send messages or spend money. What it may not do was never written down, so it is not enforced.
06 hollow
You can see that a request completed. You cannot see what was retrieved, what was reasoned, or why the answer was wrong.
07 detection lag
Hallucinations, regressions and drift arrive as support tickets. Your team learns about quality from the people paying for it.
08 propping
The system runs, but only because an engineer babysits it. The pilot became a permanent tax on the team that shipped it.
| Demo conditions | Production conditions |
|---|---|
| Inputs you chose | Whatever users send |
| One model version, one prompt | Models, prompts and data that change underneath you |
| The happy path | Adversarial, ambiguous, malformed, and out of scope |
| Someone is watching | 3am, unattended, no one watching |
| A failure is a retry | A failure is a customer, a ticket, or a regulator |
We engineer for the right-hand column.
How we work
The three motions describe what we do. These seven stages describe how we deliver it, whether we are productionizing a new capability, automating a workflow around one, or taking over something already in production. The model spans Discover through Operate, with the depth of each stage matched to the problem, the risk and who owns the system afterwards.
The lifecycle is not divided between the motions. Discovery applies whether the work is productionizing or operating. Hardening applies to an automated workflow as much as to a new service. The motions say what the engagement is for; the stages say how it gets done.
01 measure
Map the workflow, the failure modes that matter, and what working has to mean for the system in scope.
02 declare
Set the architecture, operating boundaries, acceptance criteria and production controls before implementation starts.
03 implement
Implement the production system and the controls required by its scope, with integrations, evaluation and observability built where needed.
04 bind
Wire the system into the client's data, services, permissions, CI/CD and operating environment.
05 stress
Exercise realistic failure conditions, validate boundaries and recovery paths, and make release decisions from evidence.
06 admit
Release through the appropriate controls, with explicit ship, hold or escalate decisions and a defined recovery path.
07 watch
Watch real production behaviour, detect material departures from agreed thresholds, and route them into defined response and recovery paths.
Evidence
Reliability has to be verifiable by your team without us in the room. Every engagement leaves behind the evidence appropriate to its scope, from evaluations and traces to release controls, observability and operating policies, so your team can inspect what was built and how it is meant to run.
01from Design, Build
Assertions your engineers can read, run and extend.
Written against the behaviour your business depends on, not against a benchmark that resembles it.
02from Harden, Operate
Reproducible records of what broke, with the inputs, the retrieved context, the model response and the point of divergence.
A failure your team cannot reproduce is a failure they cannot fix.
03from Harden, Deploy
The rules that decide whether a change is allowed to ship. Thresholds are explicit, versioned, and reviewed like any other code.
When a release is blocked, the reason is a number, not an opinion.
04from Design, Operate
Thresholds, escalation paths and ownership, written down.
What counts as degraded, who is paged, and what happens next.
05from Build, Operate
Tracing through the whole path: request, retrieval, tool calls, model response, outcome.
Enough signal to answer why, not only whether.
06from Design, Harden
What the system may do, what it may access, and what it is prevented from doing.
Tool permissions, data boundaries and action limits, defined in code rather than assumed in review.
07from Design
The targets themselves, agreed in Design and written down: what the system must achieve, how it is measured, and what happens when it does not.
These are the mechanism, not the marketing. Release gates are one artifact among several, never the whole practice.
Engagement paths
Entry points sit under the same three motions. Most work begins with a single production problem. If the right starting point is not obvious, the AI Production Audit provides a focused front door. Scope widens only where the system justifies it, and ongoing operation is an option rather than a default.
Front door
AI Production AuditA structured read of where your AI work actually stands against production, and what stands between it and shipping.
Not sure which of the below applies? Start here.
broader scope
Production Readiness FoundationArchitecture, evaluation strategy and reliability targets for AI features heading toward production for the first time.
focused scope
Production RescueFor AI systems already in production that are failing, drifting, or consuming more engineering time than they return.
focused scope
Automated Release GatesRelease controls that turn relevant evidence into an explicit ship, hold or escalate decision before a change reaches production.
broader scope
Agentic Workflow AutomationAutomation and control for agents that take real actions: tool use, permissions, and the boundaries they must not cross.
ongoing, optional
Continuous Production OperationsOngoing monitoring of production behaviour, drift detection, and maintenance of the gates and evaluations as your product changes.
Across all three
ongoing, optional
Embedded Reliability EngineersSenior production engineers embedded inside your delivery team to own defined production workstreams when sustained engineering ownership is needed.
Ongoing operation is available for teams that want it. It is not a requirement of the work above.
The practice
GatekeeperOps is a specialist AI Production Engineering firm focused on turning AI pilots and critical workflows into reliable production systems.
Our work draws on AI engineering, workflow automation, reliability engineering, evaluation, release controls, observability and production operations.
Engagements are led by senior engineers and built around direct technical ownership. The people defining the architecture stay close to implementation, integration, hardening and release.
We work with a focused number of teams at a time so delivery stays senior-led, technically rigorous and close to the system being built.
No generic transformation programmes. No handoff-heavy delivery model. Just focused engineering work tied to a production outcome.
Writing
Working notes on getting AI systems to production standard.
Most engineering teams ship AI features the way they ship everything else. That is the problem.
Teams can build sophisticated eval suites that never influence a release decision. The result is evidence with no defined consequence.
Adding AI to an unreliable delivery system does not fix the underlying weaknesses. It gives them more places to surface.
A 30 minute call, then a written report on where your AI work sits against production and what the path looks like. If the answer is that you do not need us, the report will say so.
30-min call. Written report. No sales script.