DevOps & AI Infrastructure

Managed DevOps & Operations

An accountable team for your software after launch, not just a handover. We run releases, monitoring, incidents, patching, backup tests and reporting under a support plan agreed with you.

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Launch is where operations begin

Live software needs steady care: dependencies age, certificates expire, traffic changes and backups only matter if they restore. Managed DevOps & Operations gives that work an accountable owner. We run controlled releases, watch monitoring and alerts, handle incidents within agreed hours, apply patches, test recovery, review access and report on performance and cost. It suits software we built and applications we didn't, including apps built with AI tools. Every engagement starts with an onboarding review.

AI-assisted operations, human-approved changes

How AI assists

  • Correlating alerts, logs and recent deploys to suggest likely causes while an engineer investigates.
  • Routine checks on patch levels, dependencies, certificate expiry, backup results and configuration drift.
  • Summarizing dependency release notes to flag breaking changes before updates are scheduled.
  • Drafting incident timelines, change notes and regular reports from monitoring data.

What our experts own

  • Incident decisions: severity, rollback or fix, and what your team and users are told.
  • Approval of every production change. Agents don't get unrestricted production access.
  • Recovery tests: engineers run restores and confirm that data and services actually come back.
  • The support plan: covered hours, response commitments and exclusions, agreed with you up front.

What happens when an alert fires

Typical incident path within covered hours; severity levels, contacts and response commitments come from your support plan.

  1. Alert

    Monitoring picks up a symptom users would notice, such as errors, slow pages or a failed job.

  2. Triage

    An engineer confirms what is affected and how widely, with AI summarizing recent changes and related errors.

    Checkpoint: An engineer sets the severity

  3. Mitigate

    Stop the harm first: roll back, disable a feature flag or add capacity, before the cause is known.

    Checkpoint: An engineer approves every production action

  4. Communicate

    Your named contacts get updates on impact, current actions and when to expect the next one.

    Checkpoint: Wording for your users agreed with you

  5. Fix

    The underlying cause is fixed in code or configuration, tested in staging and released through the pipeline.

  6. Post-incident review

    A blame-free write-up of cause, timeline and what monitoring missed; follow-ups join the improvement list.

    Checkpoint: Follow-up priorities agreed with you

When something fails: If a mitigation doesn't hold or the cause sits with a third party, we escalate as your support plan sets out and keep updates going.

What we manage

Ongoing responsibility, not a one-time handover

  • Controlled releases

    Planned deployments through reviewed pipelines, with release notes, staged rollout where it fits and a rollback path checked before each release.

  • Monitoring and alerting

    Continuous automated monitoring of availability, errors, performance and resources, with alerts routed to the people named in your support plan.

  • Incident handling

    Triage, fix or rollback, and clear updates during covered hours, followed by a written review of the cause and the follow-up work.

  • Patching and dependency updates

    Operating system, runtime, library and certificate updates on a schedule, tested before production, with urgent security fixes prioritized.

  • Backup and recovery tests

    Backups verified regularly and restores rehearsed, so recovery steps are proven in practice before you need them.

  • Reviews and regular reports

    Access reviews, performance and cost visibility, and a regular report on incidents, changes, risks and recommended next steps.

Who hands us their operations

Operations keeps losing to feature work: alerts go unread, updates wait for a quiet week, and an outage turns into a search for whoever still has access.

  • Founders whose launch agency or original developer has since moved on
  • Product teams without a DevOps specialist, where developers handle outages themselves
  • Businesses depending on a revenue-critical web app that nobody actively maintains

Typical operations requests

Typical scenarios we scope, not client case studies.

  • A contractor leaving with the only access

    The contractor who ran the servers is leaving, and the handover is a single call. We would list every login and key they hold, rotate them, confirm backups can be restored and write down what runs where before they leave.

  • A runtime reaching end of support

    The product runs on a language runtime that will soon stop receiving security updates. We would plan the upgrade in stages, test each stage against your key workflows in staging and release during agreed maintenance windows.

  • Alerts everyone has learned to ignore

    The team gets so many alerts that real problems get lost in the noise. We would check which alerts led to action, merge or retire the rest, and route what remains by severity to whoever the support plan names.

How managed operations start

  1. 01

    Onboarding review

    We review code, infrastructure, access, backups, monitoring and known risks, including for software we didn't build, and agree what to fix first.

  2. 02

    Agree the support plan

    Systems covered, support hours, response commitments, escalation contacts, responsibilities on both sides and exclusions, written down.

  3. 03

    Stabilize the essentials

    Missing monitoring, backups, access controls and runbooks are put in place, and urgent risks are fixed before routine operations begin.

  4. 04

    Operate and report

    Releases, monitoring, patching, recovery tests and reviews run on schedule, with regular reports and an agreed improvement list.

Two ways to work with AI tools

AI assists with infrastructure code and diagnostics. Choose where it may process your configuration and logs.

Not sure? We'll recommend one during scoping. Compare the packages in detail

Outside a managed operations plan

  • New features beyond the improvement work your support plan includes are scoped separately, as development projects.
  • Systems we haven't onboarded, such as a vendor's platform or an unreviewed server, stay outside the plan until they are reviewed and added.
  • Forensic investigation of a security breach isn't included. Within the plan we contain the incident, preserve logs and support whoever investigates.
  • Outages at third-party services, such as payment, email or model API providers, are outside our control; we watch them and work around them where the design allows.

How development, QA and AI operations connect

  • Fixes from the same team

    When monitoring or an incident points to a code problem, our engineers can fix it within the engagement or hand a clear diagnosis to your developers.

  • QA regression coverage

    Patches, dependency updates and fixes run through regression tests before release, so routine maintenance is checked against your important workflows.

  • AI agent and model operations

    If your product uses AI features or agents, we add evaluations, prompt and model updates and permission reviews. Hosting private models is covered by Private AI Infrastructure.

  • Continuous improvement

    Reports turn into a prioritized list of performance, cost, security and roadmap work, scheduled with you rather than left in a document.

FAQ

Frequently asked questions

What happens if something breaks outside business hours?

Monitoring and alerting run continuously. Who responds outside business hours, and how quickly, is set in your support plan: covered hours, response commitments by severity, escalation contacts and exclusions. If critical systems need after-hours coverage, we scope and agree it explicitly rather than assume it.

Can you manage an application you didn't build?

Yes, including applications built with AI tools. We start with an onboarding review of the code, infrastructure, access, backups and monitoring, then fix the most urgent risks before taking on routine operations. If something can't be run safely as it is, we tell you and propose the change.

What does a support plan include?

The systems covered, support hours, response commitments by severity, escalation contacts, maintenance windows, reporting schedule, responsibilities on both sides and exclusions. It also sets how much improvement work is included. We agree it after the onboarding review, so it reflects what actually needs running.

Do AI tools see our logs and production data?

Only what you permit. Access follows least privilege, and AI tools work from agreed logs, metrics and configuration with secrets kept out. If that material must stay inside your boundary, Private / Local AI Engineering uses models on infrastructure you control or an agreed isolated environment. Claude Code / OpenAI Codex Engineering uses commercial agents under agreed account and data-retention settings.

Related reading

Give your live software an accountable team

Tell us what is running and what worries you. We'll start with an onboarding review and propose a support plan that fits.

Start a ProjectStart with a review