About Kloia | DevOps, Cloud & Generative AI Consultancy

No company context
AI tools write code without knowing your codebase, your specs, your stored procedures, your standards. Generic in, generic out.

Zero audit trail
Nobody knows what changed, by whom, against which spec, at what cost. When something breaks, the log is empty.

Knowledge stays siloed
The senior engineer's head is still the source of truth. The pipeline learns nothing. That person leaves, the knowledge walks out.

Pilot never becomes default
The shiny demo works. Then adoption stalls. The new way of working never replaces the old one.

Why AIDLC?

Faster delivery
Tens of dollars per feature, hours of elapsed time. Not weeks.

Built-in compliance
Every action audited. Every dollar attributed to an issue.

Institutional memory
The pipeline learns. The org's knowledge stops walking out the door.

Faster onboarding
New engineers ramp on a pipeline that already knows the codebase.

A pipeline that improves
Each cycle's retro feeds the next cycle's configuration.

Humans stay in control
Agents propose. Humans approve. Every merge is gated.

No company context

Zero audit trail

Knowledge stays siloed

Pilot never becomes default

Faster delivery

Built-in compliance

Institutional memory

Faster onboarding

A pipeline that improves

Humans stay in control

VM → Container replatforming

Move off bare VMs onto EKS with proper orchestration, autoscaling, and workload isolation.

Custom metrics

Business and technical KPIs exported via OTel metrics SDK to CloudWatch and Managed Prometheus.

Structured logging

JSON-structured logs with trace correlation IDs, shipped to CloudWatch Logs Insights.

SLO alerting

Composite alarms and SLO burn-rate alerts wired from day one not as an afterthought.

Before

Monolithic .NET / Java app on VMs

After

Containerized microservices on EKS

Before

Self-managed relational DB on EC2

After

Amazon Aurora / RDS with redesigned schema

Before

On-prem RabbitMQ / ActiveMQ

After

Amazon SQS / MSK / EventBridge

Before

No distributed tracing or correlation

After

Full OTel traces, metrics, and logs

Before

Single AWS account, manual IAM

After

AWS Control Tower multi-account with guardrails

Before

Manual deployments, no CI/CD

After

GitOps pipelines with ArgoCD / CodePipeline

AWS Control Tower

Automated landing zone with account vending, OU hierarchy, and built-in guardrails.

Security baseline

AWS Security Hub, GuardDuty, Config rules, and CIS/NIST/PCI benchmarks enforced via SCPs.

Identity & access

IAM Identity Center (SSO), ABAC policies, least-privilege roles across all accounts.

Network architecture

Hub-spoke VPC with AWS Transit Gateway, PrivateLink, and Network Firewall for zero-trust connectivity.

FinOps & cost governance

Tagging strategy, AWS Cost Explorer, Budgets alerts, and Savings Plans from day one.

Compliance as code

AWS Config conformance packs, automated remediation, and audit-ready reporting.

Since 2015, we've worked with organizations to eliminate tech debt, clear delivery friction, and modernize legacy stacks, without sacrificing stability or blowing the budget.

That work spans five core practice areas:

Cloud and DevOps
Application Modernization
Software Testing
Observability and AIOps
AI

Our focus is delivering measurable outcomes, including accelerated release cycles, seamless migrations, and high-availability infrastructure.

We do it for 200+ customers across 19 countries (over 70% of them enterprise) through consulting, fixed-scope projects, or managed services built around what each team actually needs.

Slow Release Cycles & Delayed Time-to-Market

The Pain:
Release cycles stretching over weeks or months, thanks to manual testing bottlenecks, complex deployments, and constant context switching.

Our Solution :
Shifting to daily deployments through CI/CD maturity, test automation, and AI-assisted delivery (AIDLC).

Uncontrolled Cloud Costs & Resource Waste

The Pain:
Complex cloud and multi-cloud environments make it hard to see what's actually happening, so bills balloon and legacy license overhead piles up.

Our Solution :
Comprehensive FinOps strategies, cloud-native architecture redesign, and license optimization.

High Downtime & Incident Recovery Overhead (High MTTR)

The Pain:
Fragmented microservices make root cause analysis slow, which means a worse customer experience and lost revenue.

Our Solution :
Full-stack observability (Instana, Datadog) and AIOps that cut mean-time-to-resolution (MTTR) from hours down to minutes, sometimes seconds.