.svg "kloia.png"){kind=logo}
Migration & Modernization
Built for the cloud-native era
We don't just move your workloads, we transform them. Containers, microservices, managed services, and full observability from day one.
VMs → Containers Microservices Database modernization AWS Landing Zone OpenTelemetry AWS MAP
Modernization-first, not lift & shift
Every migration engagement is an opportunity to eliminate technical debt, unlock developer velocity, and build for scale. Click any pillar to explore.
VM → Container replatforming
Move off bare VMs onto EKS with proper orchestration, autoscaling, and workload isolation.
App modernization
.NET & Java apps restructured into distributed microservices not just updated versions.
Data & database modernization
Schema redesign, managed engines, and data model transformation via AWS MAP.
Managed messaging & events
Message queues migrated to Amazon SQS, SNS, EventBridge, and MSK.
Observability & OTel
Full OpenTelemetry instrumentation traces, metrics, logs wired to AWS-native tooling.
Enterprise landing zones
Multi-account AWS structure, security baselines, and compliance guardrails at scale.
AWS MAP-aligned delivery phases
Our engagement follows AWS Migration Acceleration Program phases, extended with modernization sprints.
01
Assess
- Portfolio discovery
- TCO analysis
- App dependency mapping
- Modernization scoring
02
Mobilize
- Landing zone setup
- CI/CD foundations
- OTel baseline
- Migration backlog
03
Migrate & Modernize
- Containerization
- Microservice decomp
- DB migration & redesign
- Messaging replatform
04
Optimize
- Cost optimization
- Performance tuning
- SLO/SLA wiring
- FinOps dashboards
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
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
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
What the pipeline shipped
Eight features moved from spec to merged, reviewed code with no human writing application code. People specified, clarified, and approved. The label-driven state machine never deadlocked or double-launched a run.
What the pipeline surfaced
A consolidated multi-agent review of the merged work raised 14 critical and 81 high-severity findings before any production cutover. The defects concentrated in two areas: legacy adapter contract fidelity,
and the parity harness itself.
FAQ
How is AIDLC different from existing AI coding tools?
Existing tools paste AI on top of the developer. AIDLC builds AI into the pipeline. The agents act on GitHub events, run in your sandbox, and answer to your spec. They never merge.
Can the agents merge code on their own?
No. Agents propose, a human approves. The strongest action an agent takes is opening a pull request and labelling it for review. Every merge is human-gated by construction, not by policy.
How do you prevent an agent from doing something dangerous?
The agent runs in a non-root container with a one-hour scoped token. All outbound network traffic goes through an egress allowlist enforced outside the agent process. In production, a DNS firewall and AWS Network Firewall sit in front of that.
What does one delivery cycle actually cost?
In a recent engagement, a single feature cost between $30 and $60 of model spend, keyed to its originating GitHub issue. A full cycle of eight features came in under $750. Context caching keeps the unit cost stable as scope grows.
How long does it take to get started?
One to two weeks to build the knowledge base. Then the first cycle runs. We do not need a six-month integration.
Will AIDLC work for regulated sectors?
It's designed for them. Every action is logged, every model run is auditable, every dollar is attributed. We bring the compliance layer with us.
Do you support custom LLMs?
Yes. The platform is model-agnostic by manifest. We default to Claude on Bedrock in the EU, but routes can point to any compatible endpoint, including internal models.
Is AIDLC viable for smaller teams?
We have a focused starter package for teams under 50 engineers. Less compliance scaffolding, same pipeline, faster start.