Bring AI into code review, pipeline troubleshooting and incident investigation. Keep deployment gates, access controls and verified recovery at the center of production delivery.
An example of what we can scope with your team. The tools and automation boundaries follow your workflow.
Summarize build logs, deployment changes and service symptoms.
Draft a fix or runbook action with the supporting evidence.
Run tests, apply the agreed approval gate and check recovery.
A delivery pipeline, AI-assisted investigation and tested runbooks.
Depending on fit: GitHub Actions · Azure DevOps · AI coding tools · telemetry
Choose a defined project or ongoing delivery. Engineering Capacity is one option when its scope fits. Scope and price are agreed first.
Organizations we’ve worked with
DevOps work for teams that need releases, monitoring, and incident response to become predictable operating routines.
We combine AI-assisted code review and failed-build analysis with CI/CD tests, approval gates and deployment visibility.
We define rollback plans, release notes, smoke checks, and recovery paths so failures are contained instead of improvised.
We connect logs, metrics and release history so AI-assisted investigation can summarize evidence and suggest checks for the responsible engineer.
We build AI-assisted incident summaries and runbook suggestions, with approved response actions, escalation and recovery checks.
The delivery layer is built around safe change, visible failure, and repeatable recovery.
Builds, tests, deploys, and checks are explicit so production changes are not dependent on tribal knowledge.
Teams can see what shipped, who approved it, which checks passed, and where a release is in the rollout path.
Alerts are tied to user-facing symptoms, likely causes, and owners instead of noisy dashboards nobody trusts.
Rollback, escalation, and incident communication are defined before the next production issue happens.
See how release pipelines and recovery practices support production teams.
50x
deployment frequency
Biomark
CI/CD, infrastructure, and monitoring were rebuilt to support safe releases across a multi-app healthcare platform.
KlaroReliability Engineering
50,000 users supported, 10,000+ sensors connected, and 99.2% uptime.
Echo Drive LogisticsReliability Engineering
90% automation coverage, 70% less manual testing, and 85% fewer production issues.
A clear starting point
Begin with one delivery path and its production risks. Agree the pipeline, release checks and recovery work first; ongoing delivery capacity and on-call support are separate choices.
A reviewed pipeline baseline, agreed checks and a tested release and recovery path.
AI can draft and summarise evidence. Production actions follow explicit permissions and approval gates.
Review my delivery pipelineSomething already broken? Explore System Rescue.
DevOps becomes useful when it reduces production risk and gives teams confidence to keep improving the system.
Teams ship with clearer checks, safer rollout paths, and faster ways to reverse a bad change.
Logs, metrics, traces, and deployment history make failures easier to locate and explain.
Incidents route to the right people with context, reducing confusion during high-pressure moments.
Post-incident learning becomes backlog work that reduces repeat failures instead of another meeting with no system change.
By industry
Explore the systems and operating needs that shape delivery in your sector.
Explore all industriesClient feedback from delivery engagements — see all reviews
"We outsourced our ERP development to Octacer, and they delivered a perfect solution that integrated smoothly with our existing systems. Their team is excellent."
Imran Rashid
CEO, My Construction
Scope, delivery and what happens next. The details that help you decide.
Usually the starting point is your existing delivery system. We review GitHub Actions, Azure DevOps or other current tooling before recommending a migration.
An agreed pipeline baseline, prioritised risks, required checks and a release/recovery path. Implementation scope follows the applications and environments being covered.
AI may summarise logs, draft changes or recommend actions. Execution stays within agreed permissions, tests and approval gates; sensitive production changes need the agreed reviewer.
We review credential storage, least-privilege access, environment separation and audit records. Approved data boundaries also apply to logs or code sent to AI tools.
No. Data migrations and external side effects may require forward fixes or a recovery procedure. We agree and test the applicable recovery strategy rather than assuming a code rollback is enough.
Only if explicitly agreed. Response hours, alert routing, escalation and the production owner must be defined before support starts.
We agree repository access, configuration handover and operating responsibilities. Future changes and ongoing support are scoped separately from the initial delivery.
We review your pipeline, deploy safety, and incident history, then outline the first fixes that make shipping safe.
20–30 minutes · No preparation needed · Free
