Constant attention, prepared evidence.
Agents correlate alerts, traces and recent deployments, draft an incident summary and suggest likely causes, so a person starts with the facts instead of a blank page.
Services / Run
Production reliability,
on tap.
SRE as a Service for your applications, cloud infrastructure and AI systems. AI agents keep watch and prepare the evidence. Experienced engineers stay accountable for what happens next.
Illustrative workflow
From a night alert to a recorded recovery.
Agents connect three alerts to one incident, line it up with a recent deploy and prepare the evidence. An engineer approves the rollback, and the agents verify recovery and write the record.
From reacting to improving
Better operations start
before the alert.
Recurring incidents, unclear ownership and rising cloud bills pull attention away from the work your business needs to do.
We understand the system, define who is responsible and fix the causes behind the symptoms. Monitoring and incident response sit alongside regular maintenance and a longer-term roadmap.
People own the outcome.
Experienced engineers review the diagnosis, approve changes to production and lead response when it matters. Agents propose. Accountable people act.
Each incident improves the system.
Post-incident review feeds back into alerts, runbooks and the application, so the same problem becomes less likely.
How it fits together
A managed outcome, not a tool to staff.
Agents, connections and accountable engineers come as one service.
Intake
Where an issue enters
- Alerts from your monitoring
- Chat in Slack or Teams
- A voice line, where you want one, limited to intake and handoff
Agents
Where the thinking happens
- Orchestrated diagnostic workflows
- Your runbooks, retrieved at the moment they are needed
- Decision rules with approval gates
Connections
How agents reach your systems
- Connectors to your observability tools
- Cloud and cluster access, read-only first
- Reusable integrations, so each new environment is faster to connect
People
Where accountability sits
- Senior engineers for complex incidents
- Approval for consequential changes
- A dashboard with the full audit trail
Graduated autonomy
Autonomy is earned, not assumed.
We begin in shadow mode, where the agents investigate every alert and execute nothing. Trust grows only as the evidence supports it, and you control the pace.
| Situation | Kind of action | What happens |
|---|---|---|
| Understand an alert | Always | Investigates in parallel and writes up the likely cause |
| Restart a crashed service | Safe | Recommends the fix. A person carries it out |
| Roll back a release | Needs care | Recommends the change. A person carries it out |
| Change a security rule or delete data | Never alone | Escalates to an engineer with the evidence |
Your role. You review accuracy data, side by side with your own team.
In every phase: a complete audit trail of each step and decision, read-only access to begin with, and a kill switch that you control.
The systems behind your service
Care across the production stack.
An agreed operational scope, supported by the engineering skills to act on what we find.
Cloud infrastructure
Monitor and manage AWS, Azure and Google Cloud environments. Review capacity, auto-scaling, cost and disaster-recovery arrangements.
DevOps & CI/CD
Maintain deployment pipelines, automated checks and rollback paths so releases remain repeatable as the application evolves.
Security & compliance support
Vulnerability scanning, patching, threat monitoring and evidence for your compliance program, including SOC 2 or HIPAA requirements where applicable.
Database administration
Tune queries, plan backups and recovery, and manage availability for systems using PostgreSQL, MongoDB, Redis and related databases.
AI model stewardship
Track model changes, output quality and API costs. Define evaluation thresholds and human escalation for your production AI workflows.
Application performance
Use tracing and application monitoring to investigate slow requests, expensive queries and the bottlenecks affecting users.
Clarity before coverage
Your infrastructure.
A shared operating plan.
You retain control of your environments. We agree the access, coverage and decisions needed to look after them.
- Service coverage
- Systems in scope, monitoring hours, on-call arrangements and named responsibilities.
- Incident response
- Severity definitions, P1 escalation, communication and response targets specified in your SLA.
- Access & control
- Shared responsibility, your administrative access, and the ability to revoke our permissions.
- Commercial terms
- A defined monthly scope, a change process and cancellation terms agreed before onboarding.
Operational tooling
Use the right signals.
We work with established cloud, observability and delivery tools, including the systems your team already relies on.
Cloud & edge
- AWS
- Microsoft Azure
- Google Cloud
- Cloudflare
Infrastructure & delivery
- Terraform
- Kubernetes
- Docker
- GitHub Actions
Observability & response
- Datadog
- Sentry
- PagerDuty
- Prometheus
Data & AI operations
- PostgreSQL
- MongoDB
- Redis
- LLM pipeline monitoring
- Evaluation & cost tracking
The engagement lifecycle
Understand. Stabilize. Keep improving.
Audit & onboard
Review architecture, security and access. Establish monitoring and document the operational baseline.
Stabilize
Prioritize vulnerabilities, backup and recovery gaps, and infrastructure documentation in focused initial sprints.
Manage proactively
Monitor, patch and investigate issues within the agreed coverage. Keep incident response and maintenance accountable.
Evolve quarterly
Review reliability, cost and business needs. Plan the next improvements to infrastructure, applications and AI systems.
Questions
Common questions.
Why not just hire another full-time DevOps engineer?
SRE as a Service can give you access to cloud, security and database specialists under a defined service agreement. We can complement your internal team or own an agreed operational scope. The right choice depends on your coverage needs and the skills you already have.
Will AI agents change my production systems on their own?
Not at first. We begin in shadow mode, where the agents investigate and recommend but execute nothing, and you review their accuracy against your own team. Low-risk actions are automated only after that, moderate ones need approval, and destructive changes stay with a person. Every step is recorded in an audit trail and you hold the kill switch.
What access do the agents need?
We start with read-only access to your monitoring and infrastructure. Any wider permission is agreed explicitly, scoped to specific actions and can be revoked by you at any time.
What if my system goes down at 3 AM?
We agree monitoring coverage, on-call responsibilities and escalation paths during onboarding. Critical alerts trigger the P1 response process defined in your SLA, including triage, communication and recovery responsibilities.
Do I lose access to my own servers?
No. It's your infrastructure. You keep administrative access, and you can revoke ours at any time. We work in a shared-responsibility model.
How does 'AI Model Stewardship' actually work?
We instrument your LLM pipelines to track output quality, model changes and API costs. Prompt and response logging follows your data-handling requirements. We agree alert thresholds, human review and any pause or rollback actions before enabling them.
Can you help lower our cloud bill?
Yes. We review reserved-instance options, unused resources and infrastructure sizing, then prioritize changes against reliability and performance requirements. Savings depend on your starting environment and usage; we measure the effect of changes rather than promise a fixed percentage.
Is there a long-term lock-in contract?
No long-term lock-in. Our standard agreements can be cancelled with 30 days' notice.
Start with the system that worries you most.
Tell us what you run, where the pressure is building and what support should look like. We’ll work through it together.