Zovelty Global AI Cloud Operations Platform

Daily operating system for DevOps, SRE, Platform Engineering and Cloud Engineering teams.

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Production-grade tools only

The AI Operations Mesh for Cloud Teams

Zovelty Global connects to the tools engineers already use every day, builds one operational knowledge graph, explains incidents in plain English, and recommends safe fixes for humans to review, approve and implement.

Try the command center View supported tools

What engineers ask every day

What changed before checkout failed?Change graph
Which Kubernetes deployment is unhealthy?Incident
Which Terraform apply changed the load balancer?IaC
Can AI create a safe rollback PR?Action

Demo Application

A realistic operator cockpit combining incident response, service graph, cost, security, observability, and safe remediation.

MTTR forecast18m42% faster than baseline
Impacted users12.8kUS-East checkout traffic
Revenue at risk$42kEstimated from payment funnel
Monthly savings$31.4k8 safe actions found
Critical risks32 fixable by PR

Ask Zovelty AI

Uses graph, telemetry, logs, traces, CI/CD, IaC, identity, security and cost evidence.
SEV-2
I am watching production. Checkout latency is elevated. I correlated Kubernetes events, Prometheus metrics, Loki logs, Tempo traces, ArgoCD syncs, GitHub commits, Terraform state, AWS CloudTrail, RDS health and Cost Explorer impact.
Likely cause: checkout-api v1.47.2 changed Redis timeout from 500ms to 100ms.
Confidence: 87%. Evidence from GitHub PR #8421, ArgoCD sync, Loki timeout logs and Prometheus p95 latency.

Incident INC-2048: checkout-api latency

AI has built a causal timeline across GitHub, ArgoCD, Kubernetes, Redis, Prometheus, Loki, AWS and PagerDuty.

Active
GitHub Actions deployed checkout-api v1.47.2 from PR #8421.
ArgoCD synced Helm release to EKS production namespace payments.
Prometheus p95 latency rose from 180ms to 1.9s. Error budget burn 14x.
Loki and Tempo show Redis token lookup timeout spike.
AI recommends rollback to v1.47.1 and PR with circuit breaker.

Operational Knowledge Graph

Graph connects cloud resources, code, deploys, SLOs, costs, owners and risks.
GitHub PR#8421
ArgoCDv1.47.2
checkout-apip95 1.9s
Redistimeouts
RDS ordershealthy
AWS ALB5xx rising
Users12.8k impacted
OwnerPayments SRE

Production Tool Intelligence

Only important tools used daily by DevOps, SRE, Platform Engineers and Cloud Engineers. Each connector tells AI what to collect, how to reason, and which safe actions it can perform.

AI Actions That Save Time Every Day

The platform should automate investigation and generate reviewed fixes, while keeping high-risk production actions behind approvals.

Generated Remediation PR

Terraform, Kubernetes and Helm changes with policy checks.
Terraform fixRestrict public admin security group, update tags and owner metadata.
Checkov pass
Kubernetes fixRestore Redis timeout, add circuit breaker and timeout-rate alert.
OPA pass
Helm fixPatch values.yaml and generate a safe ArgoCD rollback path.
Needs SRE
- redis_timeout_ms: 100
+ redis_timeout_ms: 500
+ circuitBreaker:
+   enabled: true
+ alerts:
+   redisTimeoutRate: "2%"

Operational Workflows

From signal to action with auditability.
1. SenseIngest metrics, logs, traces, events, cloud audit logs, costs, CI/CD and tickets.
2. LinkBuild graph edges across services, resources, owners, commits and deployments.
3. ReasonFind likely cause, blast radius, users impacted, cost impact and risk.
4. ActCreate PRs, rollback plans, Slack updates, Jira tickets and PagerDuty notes.
5. LearnStore incident memory, runbooks, rejected actions and successful fixes.

Website Sections for Go-To-Market

Product website content built into the demo, focused on daily engineering value rather than vague enterprise promises.

For SRE

Shorten incidents

Correlate alerts, deploys, traces, logs, cloud events, database health and service ownership in one place.

For DevOps

Fix through PRs

Generate Terraform, Kubernetes and Helm changes with context, policy checks, CI results and human approval.

For FinOps

Cut waste safely

Connect spend to teams, services, Kubernetes workloads, idle resources and deployment changes.

For Security

Prioritize real risk

Combine CVEs, IAM, public exposure, secrets and production blast radius to focus on what matters.

For Platform

Reduce tickets

Give teams a self-service AI interface that understands golden paths, runbooks, ownership and policies.

For Cloud

Understand multi-cloud

Map AWS, Azure and Google Cloud resources into one operational graph with identity, network and cost context.

How to Implement Zovelty Global Live for a Business

Start read-only, prove daily value, then add approved write actions through pull requests, tickets and existing CI/CD. The platform should track day-to-day engineering work across AWS, Azure, GCP and DevOps tools without replacing them.

Phase 1

Connect read-only data

Connect AWS, Azure, GCP, Kubernetes, GitHub/GitLab, CI/CD, Prometheus, Grafana, Loki, PagerDuty, Slack and Jira with read-only permissions. Build inventory, ownership, service maps, deployment history and incident timelines.

Phase 2

Build the daily work graph

Link every service to repos, commits, pipelines, deployments, cloud resources, Kubernetes workloads, dashboards, alerts, incidents, owners, tickets, costs, vulnerabilities and identity changes.

Phase 3

Make AI useful every morning

Generate daily briefs: overnight incidents, failed builds, risky deployments, noisy alerts, expiring certificates, slow databases, cloud cost spikes, security risks and open approvals by team.

Phase 4

Add safe action workflows

Let AI create Terraform, Kubernetes, Helm and runbook PRs. Let it open Jira/ServiceNow tickets, update Slack and PagerDuty, request approvals and recommend rollbacks. Keep production mutation behind human approval.

Phase 5

Operationalize for teams

Create team workspaces for SRE, DevOps, Cloud, Security, FinOps and Platform teams. Each workspace shows only relevant services, incidents, costs, risks, deploys, runbooks and recommended actions.

Phase 6

Measure business value

Track MTTR reduction, fewer repeated incidents, cloud savings, faster deployment recovery, reduced alert noise, security risk closure, failed pipeline time saved and engineer hours returned.

Minimum Live Architecture

Production-ready but practical for first customers.
CollectorsCloud account connectors, Kubernetes agent, Git provider app, CI/CD webhooks and observability adapters.
Ingest
Event pipelineNormalize CloudTrail, Azure Activity, GCP Audit Logs, Kubernetes events, deploys, alerts and tickets.
Stream
Knowledge graphService, owner, repo, deployment, resource, identity, cost, alert, ticket and vulnerability relationships.
Reason
AI action layerRAG over runbooks, graph queries, tool calls, PR generation, approval gates and audit logs.
Act

Daily Engineering Operating Loop

What teams should use every day.
MorningShow overnight incidents, failed deploys, cost spikes, expired certs and urgent risks.
During deploysWatch pipelines, ArgoCD syncs, Kubernetes rollouts, metrics, logs and user impact.
During incidentsFind what changed, blast radius, owner, rollback and plain-English status updates.
During reviewsExplain Terraform plans, security findings, cost impact and risky Kubernetes changes.
End of daySummarize open risks, unresolved tickets, noisy alerts and recommended PRs.