%20--%3e%3cstyle%20type='text/css'%3e.cls-0%20{fill:%23FF9900;}%3c/style%3e%3cpath%20class='cls-0'%20d='m117.3%2084h-14.1l39.2-79h-24.8l-20.3%2041.7-20.5-41.7h-3.8l-20.3%2041.7-20.8-41.7h-24.3l41%2083.3h8.2l18.2-37.7%2018.3%2037.8h23.7v2.8h10.7v-10.5h-10.6v3.3h0.2zm3.3-74.7h15.2l-28.9%2057.1-7.2-14.9%2020.6-42.2h0.3zm-69.3%2074.9-37-75h15.1l20.8%2042.4-5.8%2011%204.1%201.6%2016.9-33.9%207.2%2015.7-18.4%2038.1-2.9%200.1zm44.5-0.1-28.2-58.9%207.4-14.8%2029.6%2060.8-6.1%2012.9c0%200.1-2.7%200-2.7%200zm27.9%200.6v2.8h-3.1v-2.8h3.1z'/%3e%3c/svg%3e)
Continuous engineering assurance
One place to see whether your cloud, clusters, pipelines and AI systems are actually in the state you think they are — and to close the gap when they are not.
Five modules run today
Connect a Google Cloud project and Security, Cloud, Kubernetes, DevOps and FinOps assurance read it — read-only, and each run reports what it examined and what it could not, so an empty result is never mistaken for a clean one. It reads Google Cloud only, not AWS or Azure, and it does not track remediation. AI & AgentOps, Compliance and Integrations are still on the roadmap and each page says so. The wider reviews are available now as engineering engagements, delivered by the people building this.
Eight modules, one picture
Assurance split by the systems teams actually own, so findings land with the people who can act on them.
Cloud Assurance
Continuous architecture review against the Well-Architected pillars, instead of a slide deck once a year.
- Workload and architecture assessment
- Security, reliability, performance, cost, operational excellence, sustainability
- Risk prioritisation and remediation tracking
- Audit-ready reporting
Kubernetes Assurance
Cluster posture across EKS, AKS, GKE and OpenShift — security, reliability and the configuration drift nobody notices until it bites.
- Cluster inventory and configuration hygiene
- RBAC, network policy and security posture
- Resource limits, probes and reliability signals
- Upgrade readiness and cost optimisation
DevOps Assurance
How delivery actually works, measured: branch protection, security gates, release practice and deployment maturity.
- CI/CD maturity across GitHub, GitLab, Azure DevOps and Jenkins
- GitOps and Terraform practice
- Branch protection and security gates
- Engineering delivery metrics
Security Assurance
Security posture where engineering decisions are made — in the cloud account, the cluster, the pipeline and the code.
- Cloud and identity security posture
- Infrastructure-as-code and pipeline security
- Secrets and software supply chain
- Control mapping for compliance work
FinOps
Where the money goes, what is wasted, and which commitments are worth making.
- Spend visibility and forecasting
- Waste, idle resources and rightsizing
- Kubernetes cost allocation
- Data transfer, NAT and commitment optimisation
AI & AgentOps
Inventory, permissions, cost and risk for the models and agents now running in production.
- Model and agent inventory
- Agent permissions and tool/MCP governance
- AI security posture and architecture review
- Token usage, latency, errors and spend
Compliance
Map what your systems do to what your auditors ask, without a spreadsheet marathon.
- Control mapping across frameworks
- Evidence collection from live systems
- Gap tracking and ownership
- Audit-ready reporting
Integrations
Read-only connections to the systems you already run, so assurance reflects reality rather than a questionnaire.
- AWS, Azure and Google Cloud
- Kubernetes clusters
- CI/CD and source control
- Observability and ticketing
A loop, not a report
An annual review tells you where you stood in March. This is meant to keep telling you, and to close what it finds.
Discover
Connect read-only and build an accurate picture of what is actually running.
Assess
Evaluate it against architecture, security, reliability and cost criteria.
Prioritise
Rank findings by real risk and effort, not by severity label alone.
Remediate
Generate the fix, and route it through review — never straight into production.
Verify
Confirm the change landed and the finding is genuinely closed.
Improve
Feed what was learned back in, so the same gap does not reappear.
Nothing changes without a human saying so
Automated remediation that pushes straight to production is how you turn one bad finding into an outage. Fixes are proposed, reviewed and merged the same way any other change is.
- Step 1
Detect
A finding is raised with the evidence behind it.
- Step 2
Explain
What it means, why it matters, what it affects.
- Step 3
Propose
A concrete fix, as a pull request against your repository.
- Step 4
Approve
Your engineer reviews and merges. Then we verify it landed.
Tell us what you would need it to catch
We are building this against real environments. If you run cloud, Kubernetes or AI systems at scale, the fastest way to shape it is a conversation with the engineers designing it.
Talk to an Engineer