Bounded operating service · LLMOps & AI Observability

Managed AI Operations & Continuous Improvement

Managed operations only work when incidents, evaluations, costs, and proposed changes enter one bounded service record with named owners and review points.

We maintain the service record across monitoring, incidents, evaluation findings, cost movements, and proposed changes. Each item keeps its evidence, severity, owner, next action, and review point. Your service owner sets priorities and accepts residual risk.

You leave with a current service record and improvement backlog that show what changed, who owns it, and when it returns for review.

Discuss this workView the parent service
Illustration of Managed AI Operations & Continuous Improvement: a team monitoring and operating an AI system in production

Some of the 500+ brands we've worked with

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  • PayTR
  • AVVA
  • Cheetos
  • Jumbo
  • Quick Sigorta
  • Ekol

Steps, gates, and who decides

How we work

The managed service begins with a charter. Inside that boundary, every material signal enters the same operating record and leaves with an owner, a decision, or a dated reason it remains open.

  1. Set the operating charter

    The charter names the services, environments, signals, queues, owners, escalation routes, decision cadence, and excluded work. That prevents an incident queue from quietly becoming unlimited production support.

    AI assist
    Tools draft the candidate service boundary from current systems and queues.
    Human gate
    Are the service boundary, response ownership, and decision authority clear? Your service owner confirms which systems and decisions sit inside scope.

  2. Run monitoring and triage

    Production signals, incidents, evaluation findings, cost movements, user feedback, and open exceptions enter triage together. Each material item receives evidence, severity, an owner, and a next action.

    AI assist
    Tools route incoming signals and draft severity for triage review.
    Human gate
    Does each material issue have evidence, severity, an owner, and a next action? Your operations lead confirms severity and the owner for each material issue.

  3. Test proposed changes

    Backlog items are selected by the client priority. We inspect dependencies and failure cases, then test representative behavior before a production change reaches the decision owner.

    AI assist
    Tools run representative cases and flag failures against the acceptance conditions.
    Human gate
    Does the change meet its quality, cost, reliability, and rollback conditions? Your service owner decides whether a change meets the rollback and quality conditions.

  4. Review and improve

    Accepted and rejected changes both update the service record. We assign the next measurement, keep unresolved conditions visible, and bring them back at the agreed review point.

    AI assist
    Tools draft the updated service record and backlog from the review.
    Human gate
    What is accepted now, what stays open, and when will it be reviewed again? Your service owner accepts what's closed and what stays open.

Named artifacts you keep

What you get

The outputs let an owner open one record and see what happened, why it matters, who is acting, which change was tested, what the client accepted, and what is still waiting.

  • Roadmap

    Managed-operations service record and improvement backlog

    The current service boundary, incidents, findings, priorities, dependencies, owners, accepted changes, and open improvement items.

  • Risk register

    Monitoring sources and open-dependency inventory

    The monitoring sources, evaluation evidence, assumptions, system dependencies, constraints, and unresolved questions behind the backlog.

  • Report

    Proposed-change test readout and rollback exceptions

    Test results for proposed changes, including important slices, failures, exceptions, and rollback conditions.

  • Decision record

    Accepted changes, owners, and next-review log

    The accepted action, conditions, residual risk, owner, implementation handoff, next measurement, and review point.

Scope and honest limits

When to bring us in

Use this service when production work arrives through several queues and the team needs one bounded place to triage issues, order improvements, test changes, and record client decisions.

A good fit when

  • Your incidents, quality findings, cost movements, and user feedback enter separate queues, so no service owner can set one order.
  • Your team changes production behavior, but the reason, evidence, result, and next review point are scattered across the service record.
  • Support requests keep displacing improvement work, so backlog items reach review without an agreed priority or acceptance path.
  • Production signals arrive through monitoring and incident triage, but nobody can see quality, cost, and service status in one report.
  • The improvement backlog names dependencies, but owners cannot tell which exception should move first.
  • A proposed change looks ready from one result, but representative tests have not checked its failure cases or rollback conditions.
  • Accepted changes leave the review meeting, while ownership, handoff, next measurement, and review date remain split across records.

Better handled as other work when

  • You need one team to absorb unlimited production support, because the operating charter only covers the services and queues it names.
  • You want Zeo to accept a business, policy, legal, or residual-risk decision, but those calls remain with your service owner and authorities.
  • You expect incidents, cost changes, or quality failures to disappear, while this service only manages how each one is handled and closed.

If one of these is closer to your situation, start here instead: View the parent service

We operate the systems we test

It's hard to test a system well if you've never had to keep one running. We operate production AI ourselves, so our evaluation, security testing, and LLMOps work starts from what actually breaks. The people on it are senior engineers, and Zeo has been doing client work since 2011.

Content we've produced on this topic

Articleby Burak PehlivanWhat is WebMCP? Agentic Web and Websites Interacting with AI AgentsArticleby Can MutioğluOpenAI Models & APIs UsageArticleby Yiğit KonurMCP Server Observability: Monitoring, Testing & Performance MetricsArticleby Yiğit KonurMCP Server Economics: TCO Analysis, Business Models & ROI

Tools we use

Tools behind this work

  • Airtable

    maintains the service record from triage through accepted change

  • Datadog

    runs the shared operational view across quality, cost, and incidents

  • Langfuse

    turns each quality issue into an inspectable trace and version record

  • Confident AI / DeepEval

    reruns regression gates before proposed improvements enter release

  • Helicone

    tracks request cost, latency, tokens, retries, and cache movement

Next step

Give production issues one accountable operating record

Share the service boundary, the open incidents or findings, and the person who sets priorities. We will structure the first managed triage and review record.
Talk to Zeo

Before you decide

Questions we hear a lot

We need the production service boundary, monitoring and incident sources, evaluation results, cost views, current backlog, known constraints, operating and engineering owners, escalation routes, and the authority who can prioritize or accept a change. Access stays limited to the agreed operating work.