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The Problem
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.
Problems
No company context
Zero audit trail
Knowledge stays siloed
Pilot never becomes default
Why AIDLC?
Faster delivery
Built-in compliance
Institutional memory
Faster onboarding
A pipeline that improves
Humans stay in control
OpenTelemetry:
Baked in, not bolted on
Auto-instrumentation of .NET and Java services → ADOT collector sidecars on EKS → AWS X-Ray for distributed tracing → CloudWatch for metrics and logs → custom dashboards and SLO alerting. All traces, metrics, and logs flow through a single OTel pipeline, swap backends without re-instrumenting.
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.
What changes in your architecture
AWS Landing Zone & multi-account governance
AWS Control Tower
Security baseline
Identity & access
Network architecture
FinOps & cost governance
Compliance as code
Measurable results our clients achieve
60-80%
infrastructure cost reduction vs on-prem VMs
10x
faster deployments via GitOps pipelines
99.9%+
availability through EKS self-healing & multi-AZ
<5 min
MTTR with full OTel trace-to-log correlation
Platform Development as a Service
Your dev teams are fast but your platform is holding them back?
We fix that
We listen first
We sit with your engineering teams and understand how they actually work. Not how the org chart says they work. We map what the platform needs to do before we write a line of code.
We build it properly
Everything as code. Open source where it makes sense. We build your platform the way we would build our own. Automated, tested, documented, and ready for the team that inherits it next year.
We keep it running
Platforms are not projects. They are products. We stay involved for scaling, cost optimisation, disaster recovery, and whatever your business throws at it next.
The Golden Path to
Predictable Software Delivery
The most common request we hear from our clients is the need for a "Golden Path" for their engineering teams. They want a platform that makes the right way to deploy the easiest way to deploy. We build these environments to increase developer velocity while maintaining strict security and compliance standards.
Building a platform is a balancing act. We help you make informed choices regarding infrastructure costs and system performance. Our service covers the full lifecycle of platform development, including the design, build, and ongoing optimization phases. We provide the peace of mind that comes with having an expert team standing behind your production systems.
What this covers
If you are not sure which of these applies to you, that is exactly what the first conversation is for.
Platform Engineering Transformation
We assess where your platform engineering practice is today and move it to where it needs to be.
Platform as Product
We treat your platform like a product with users, feedback loops, and a roadmap. Not a one-time project.
Platform Engineering Design and Architecture
We design the architecture from the ground up. Decisions that will still make sense three years from now.
Building Platform Pipeline
Every change to the platform goes through a proper pipeline. Quality, security, and cost checks built in from the start.
Internal Developer Platform (IDP)
We build the platform your developers actually use. Self-service, opinionated, and fast to get started on.
Platform Unit Testing
We write tests for platform code the same way developers write tests for application code. No more merging on trust.
Self Service Platform Capabilities
Teams provision what they need without waiting for ops. We build the guardrails that make self-service safe.
Platform Teams vs Delivery Teams
We help you figure out the right team structure. Who owns the platform, who consumes it, and how they work together.
The principles behind every platform we ship
These are the actual engineering decisions we make on every engagement.
Automate everything
Everything as code
Platform has a pipeline too
Platform changes get tested
Built to scale
You can see everything
What changes when the platform is right
Outcomes your engineering org will actually feel.
Your cloud bill goes down
Kubernetes consolidation reduces the number of servers you are running. Add event-driven scaling, serverless where it fits, and auto-scaling to zero. The savings are real and measurable from day one.
Your services get faster
Decoupled architecture, caching layers, and lean services replace monolith bottlenecks. Your applications perform better without your engineers having to rebuild everything from scratch.
Your infrastructure heals itself
Kubernetes-native, cloud-native, self-healing by design. Incidents that used to wake someone up at 3am start resolving themselves before anyone notices.
Your teams ship faster
When the platform is solid, engineers stop waiting and start building. Standardised deployments, automated pipelines, and a shared foundation means less friction on every team, every day.
Case Studies
Open-Source Observability Transformation on AWS
Nothing else matters
Nothing else matters
Nothing else matters
Nothing else matters
How we can help you?
What makes kloia's platform development approach unique?
We treat your platform as a product, not a one-time project. That means it has users (your developers), a feedback loop, and a roadmap that evolves with your business. Most vendors build a platform and leave. We build one that your teams actually want to use, with self-service capabilities, guardrails, and continuous improvement baked in from day one.
How long does platform development typically take?
Every platform is different, but most engagements follow a phased model: a discovery and design phase (2–4 weeks), an initial platform build with core capabilities (6–12 weeks), and an ongoing iteration cycle. We don’t wait until everything is perfect, we deliver value incrementally so your teams can start benefiting early.
What technologies do you use in platform development?
We’re technology-agnostic, we use what’s right for your context, not what’s easiest for us. Our engineers work across Kubernetes, Terraform, Pulumi, Ansible, and leading cloud providers (AWS, GCP, Azure). For Internal Developer Platforms, we evaluate tools like Backstage, Crossplane, and ArgoCD. We always start with your current stack and build from there.
How do you ensure platform security?
Security isn’t an afterthought, it’s embedded in every layer of our process. We apply quality and security testing to all platform changes, implement infrastructure-as-code so every configuration is auditable, and enforce strict access controls and compliance standards from the start. Shift-left security means issues are caught before they reach production, not after.
What kind of support do you provide after implementation?
Go-live is the beginning, not the end. After implementation, we stay involved to help you scale, optimize costs, and handle disaster recovery planning. We also support your internal teams with training and documentation so they can operate the platform confidently, without being dependent on us forever.