Overview
Engini is an enterprise AI agent / workflow automation platform - connectors, agentic workflows, and orchestration across CRM, ERP, HR, and finance systems. As they scaled AI and integration workloads, they needed a faster path from development to production, autoscaling that matched real demand, clear AWS cost visibility, and disaster recovery they did not have before.
The Challenge
Engini’s product velocity was strong, but delivery and resilience were holding the team back. Promotions were slow and console-bound. There was no event-driven autoscaling when AI and workflow demand spiked. Finance could not see a clear AWS run-rate before scale-up. Critically, Engini had no disaster recovery environment - an outage meant extended downtime, not failover.
Engini had no DR environment. Deployments were console-bound with a long path from development to production and no reliable rollback. There was no event-driven autoscaling for AI and workflow peaks. Cost conversations were guesswork. Human access was not SSO-first.
Our Solution
Dcode delivered a governed AWS platform modernization. Terraform reusable modules built the foundation. Workloads moved onto EKS with a controlled cutover path so production stayed safe during validation - not a risky big-bang switch.
Argo CD GitOps replaced console deploys with PR-based promotions and automatic rollback. Helm standardized how services ship across environments. KEDA added event-driven autoscaling for queue-backed agents and workflows. Karpenter provisioned right-sized capacity from real demand instead of fixed fleets sitting idle off-peak. SSO-first access removed long-lived human access keys from the platform path.
Observability landed on centralized logging, Prometheus and Grafana metrics, and alerting to on-call. FinOps work priced the monthly AWS run-rate before uncontrolled growth, so Finance could see spend impact as AI and integration workloads scaled. Where Engini previously had no DR environment, they now run a fully active-active DR environment.
This engagement followed the AWS Well-Architected Cost Optimization pillar. Right-sized compute, scale-to-demand, and deliberate HA choices reduced over-provisioning while keeping performance during AI and workflow peaks.
Results
- Multi-account AWS landing zone live - Organizations, SSO, org CloudTrail, ops/dev/prod isolation
- Cost visibility established - shared Pricing Calculator TCO, Cost Explorer path defined for ongoing tracking
- GCP-only gap closed for AWS production - prod EKS + GitOps catalog, dev + pr envs before prod risk
- Delivery modernized - console deploys replaced by Argo CD, peak 8–11 promotions/week on the AWS GitOps path
- Human IAM users eliminated on the platform path - Identity Center only
- FinOps trade-offs documented - avoided double SQL Server / always-on DR compute until business required it
- No DR before - fully active-active DR now