CloudWizz Intelligence
AI built into every
layer of delivery.
Not a chatbot. Not a wrapper. Purpose-built AI systems integrated into infrastructure auditing, monitoring, security, and CI/CD — reviewed by senior engineers before anything touches production.
Unrestricted S3 bucket in prod-us-east-1
→ Queued for senior engineer review
$2,340 / mo saving — 3 EC2 nodes rightsized
✓ Applied after engineer sign-off
2 pods Pending 18h — autoscaler limit hit
→ Fix drafted, awaiting engineer review
Flaky test root cause found in build #847
✓ Patch approved and deployed
Terraform drift — 4 resources out of sync
→ Remediation plan generated
The Model
AI does the work. Humans own the outcome.
Most consultancies are either pretending AI isn't happening or claiming "AI-first" without explaining what gets delegated. We're explicit about what AI handles and what senior engineers own — every time.
AI Accelerates
What AI handles
- →
Infrastructure audits
Gap analysis, cost breakdowns, and risk identification — automated across your entire environment.
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Terraform & IaC drafts
First-pass IaC written from audit findings. Engineers review, refine, and approve before any apply.
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Runbook generation
Runbooks drafted from observability data and incident history — reviewed and validated by engineers.
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Alert triage
Noise filtered, real signals surfaced. Engineers see context, not raw log dumps.
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Documentation scaffolds
Deliverables, architecture docs, and handoff materials drafted at speed, polished by the team.
Humans Review
What senior engineers own
- →
Every production change
Nothing touches production without a senior engineer reviewing, validating, and signing off.
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Architecture decisions
Trade-offs, design choices, and long-term implications — humans only, every time.
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Live system validation
Real environments, real edge cases. AI can't carry the risk. Engineers do.
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Risk vs. noise calls
What needs acting on and what can wait — judgment that can't be delegated to a model.
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Client relationship & on-call
Accountability, escalations, and the relationship. Senior engineers own the outcome end to end.
Capabilities
Four systems. One intelligence layer.
Each capability is a purpose-built AI system — designed around real DevOps workflows, not generic tools.
Infrastructure
AI-Assisted Infrastructure
Automated infrastructure auditing, Terraform draft generation, and architecture gap detection. What used to take a senior engineer two weeks takes hours — with the same senior engineer reviewing the output.
Monitoring
Intelligent Monitoring & Alerting
AI-powered alert triage that surfaces what's real and suppresses noise. Automated runbook generation from observability data. Engineers get context, not raw logs.
Security
Automated Security & Compliance
Continuous security scanning, misconfiguration detection, and compliance checks across your infrastructure — automated, reported, and remediated with human sign-off.
CI/CD
AI in CI/CD Pipelines
Intelligent pipeline optimisation, failure analysis, and deployment risk scoring. AI identifies what's slowing your releases and what's putting them at risk — before they go out.
How It Works
From audit to production.
AI-accelerated, human-approved.
Every engagement runs through the same intelligence pipeline — no exceptions, no black boxes.
-
Step 01
AI audits your infrastructure
Our systems scan your environment, identify gaps, risks, and opportunities. What used to take a week is ready in hours.
-
Step 02
Senior engineer reviews every finding
Nothing leaves our system without a senior engineer validating it. AI surfaces — humans decide.
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Step 03
You get a prioritised roadmap
Clear deliverables, fixed costs, transparent trade-offs. No surprises. You approve before we build.
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Step 04
Continuous intelligence in production
Once live, our monitoring and alerting systems keep watching — and your dedicated engineer keeps owning the outcome.
227+
Terraform modules in production
40+
Reusable GitHub Actions workflows
64%
Average cloud cost reduction
4.9★
Clutch rating
Clarity
What it's not.
Worth saying plainly, because a lot of "AI-first" consultancies aren't being straight with you.
✕
Not a chatbot you prompt
You don't type questions and hope for good answers. CloudWizz Intelligence is purpose-built tooling wired into your actual environment — not a wrapper around a general-purpose model.
✕
Not a black box making production changes
No AI output touches production without a senior engineer reviewing, validating, and signing off. The model surfaces. The human decides. Every time, no exceptions.
✕
Not a replacement for your engineers
We augment what skilled engineers can do — not replace them. Every engagement has a named senior engineer accountable for the outcome, start to finish.
A Real Example
This is what it looks like in practice.
A Series B SaaS company. AWS. A misconfigured IAM role that had been sitting in production for six weeks.
Step 01
The problem
An IAM role in production had overly broad S3 permissions — read/write across all buckets, not scoped to the service that needed it. The team didn't know. It had been there six weeks.
Step 02
AI flagged it in hours
Intelligence surfaced the misconfiguration during a routine audit scan, three hours after it started. It also mapped which resources were exposed and estimated the blast radius — automatically.
Step 03
Engineer reviewed. Fixed same day.
A senior engineer reviewed the finding, confirmed it was a real exposure (not a false positive), scoped the fix, and had it applied the same day. Zero downtime. Security finding closed before their SOC 2 audit.
Hard Questions
The questions skeptics ask.
How is this different from using AI tools ourselves? +
Most teams experimenting with AI in DevOps are prompting general-purpose models — ChatGPT, Copilot, Gemini. These tools have no context about your infrastructure, your history, or your risk tolerance. They hallucinate. They can't see your environment. And when something goes wrong, there's no one accountable. CloudWizz Intelligence is purpose-built for DevOps workflows, integrated with your actual environment, and every output is reviewed by a senior engineer before anything touches production. The difference isn't the AI — it's the system around it.
Does your AI train on our infrastructure data? +
No. Your infrastructure data, configurations, and logs are never used to train any model. What runs on your environment stays in your environment. We use AI to analyse and accelerate — not to harvest.
What happens if the AI gets something wrong? +
That's exactly why senior engineers review every output before it touches production. The AI surfaces findings and drafts — humans validate, decide, and approve. Nothing goes live on the back of an AI output alone. The engineer carries the accountability, not the model.
Can we use this alongside our existing tools? +
Yes. CloudWizz Intelligence works with what you already have — AWS, Azure, GCP, Terraform, GitHub Actions, Datadog, PagerDuty, and more. We don't replace your stack. We plug into it and make it faster.
"We never train models on your infrastructure data. Your configurations, logs, and environment details stay in your environment — always."
Enis Ozgel
Business Development Manager · CloudWizz
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