Built in Berlin for Kubernetes teams
Kubernetes operations your team can trust.
Deploy faster, understand incidents with real evidence, and reduce wasted resources—without giving an AI permission to change your cluster.
Read-focused agent
Review before change
Built in Berlin
Cluster connectedEvidence arriving now
Evidence before answersEvents · metrics · YAML
You stay in controlReview before change
External pilotSoftKAI DevOps team
Read-focused by defaultOutbound-only agent connection
Environment awareKubernetes · EKS · OpenShift
Review before changeGitOps-ready recommendations
One human-guided operating path
Choose where your team needs help.
Each capability stays connected to the same real cluster, application and evidence—without hiding the decisions from your engineers.
Deploy · guided configuration
From idea to review-ready manifests.
KrevoPilot detects the connected environment, asks only for required inputs, writes the manifests, and checks the result before your team reviews it.
1Understand the cluster2Guide the configuration3Check with Krevo AI4Send to Git or download
Observe · application-first context
See the application, not a wall of disconnected metrics.
Health, topology, changes, dashboards, metrics, events and logs remain attached to the real application, namespace and cluster your team is operating.
1Open an application2Follow its topology3Inspect live evidence4Drill into the exact resource
Investigate · evidence before answers
Move from a symptom to a defensible root cause.
Krevo AI correlates status, events, current and previous logs, metrics, manifests, networking and recent changes. Supported and ruled-out hypotheses remain visible.
1Select the exact pod2Correlate evidence3Explain the cause4Review the safe fix
Optimize · history before recommendation
Right-size with evidence your team can inspect.
Compare requests with observed CPU and memory behavior, distinguish learning from mature recommendations, estimate savings, and review the proposed GitOps change.
1Collect real history2Measure maturity3Review the recommendation4Observe after change
Founder-led · built in Berlin
Kubernetes operations should earn your trust.
KrevoPilot is built by Abdul Rahman through KreateRevo UG—combining hands-on Kubernetes and DevOps experience with a simple rule: show the evidence and keep the customer in control.
Read-focused agent
Evidence before answers
Review before change
Abdul Rahman
Founder · KreateRevo UG
Built for operational outcomes
Less time searching. More confidence acting.
Give platform engineers and developers the context they need without asking them to assemble five separate tools.
Shorten incident investigation
Move from an unhealthy application to the affected pod, supporting evidence, probable cause and a reviewable fix.
See the benchmark →Reduce resource waste
Compare requests with observed usage, separate preliminary from mature recommendations, and estimate monthly opportunity.
See optimization →Operate with guardrails
Keep Kubernetes access read-focused, scope users to teams and clusters, and record attributed administrative activity.
Review security →EARLY-ACCESS DESIGN PARTNERSoftKAICloud · DevOps · Security · AIExternal pilot in progress
Tested outside our own lab
A DevOps services team installed KrevoPilot against a realistic 11-service environment.
SoftKAI independently validated agent installation, read-focused access, application inventory and evidence-backed diagnosis of deliberately broken workloads.
11 servicesReal failure scenariosRead-only behavior verified
Read the pilot notes and disclosure →
Fits the platform you already run
Operational depth without owning all your data.
Use KrevoPilot as the decision layer while Git, Loki and your collaboration tools remain systems of record.
Security designed for technical review
Know what is installed, what it can read, and what leaves the cluster.
The standard outbound agent uses Kubernetes get and list. It has no create, update, patch or delete verbs. Optional collectors remain explicit.
Technical pilots are reviewed before activation
Bring one cluster. Leave with evidence.
We will review your environment, expected cluster size and success criteria before enabling a guided pilot.