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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
Case Study - N11
How N11 Thrives with
Event Sourcing
130
Microservices in production
8
CQRS services
Client: N11
Project type: Microservices
Website: www.n11.com
Situation
N11.com is a major Turkish e-commerce marketplace and a joint venture between Dogus Group and Korean SK Group. The platform serves a wide product range at high traffic.
N11's software architecture had grown enormously over the years. The monolith carried high unit-test coverage, which reflected the team's strong quality culture. But the same monolith had become the main constraint on scale.
Task
The N11 team faced repeated scaling challenges as the codebase and traffic grew. After several joint sessions, Kloia and N11 agreed that a decoupled architecture was needed for long-term growth.
Before any build, both teams aligned on the architectural conventions that would govern the transition:
-
Domains and bounded contexts
-
Strong-consistency requirements
-
Event-Driven vs Event-Sourcing / CQRS use-cases
-
Underlying platform requirements
Action
Kloia and N11 combined expertise from both sides. Kloia brought the microservices depth. N11 brought the domain knowledge. Together the teams began a structured modernisation journey.
From these sessions, the teams built an internal microservices framework to make the developer experience easier. The framework leveraged Spring and Netflix libraries, plus tools such as SQL, NoSQL and EventBus, to stay developer-friendly.
The framework offers the following capabilities:
- REST and Data Retrieval / Aggregation. Ensures REST services comply with maturity level 3.
- Stateless authentication / authorisation. Auth is handled without server-side session state.
- Decoupled Cache Management. Distinct caching policies on separate entities, each editable independently:
- CacheKey
- JSON Contract
- Eviction & Index policy
- Circuit Breaker
- Automated Auditing. Handled automatically by the framework.
- API / Swagger Documentation. Generated automatically.
- Structured Logging. Logs are captured as JSON objects and persisted on Elastic. Developers write one-line logs and the framework handles the transformations.
- Microservices Exception Handling. The framework returns the exception to the microservice that initiated the chain. Instead of TRY-CATCH-THROW in every service, the framework owns exception handling.
- Centralized Configuration. Spring keeps environment configuration in a single file. The framework splits it into separate files and folders.
- Resource Discovery / Registry. Each service can expose more than one endpoint and resource. On top of the service discovery Kubernetes already provides, the framework maintains the registry.
- Automated Dependency Registration. Extra fields feed the Spring Actuator health check with dependency data sources such as Couchbase.
A central config service gathers dependencies from each service. This standardises library-level dependencies and encapsulates access to the data sources. These non-functional principles define the framework, alongside industry best practices such as Feign Client.
The deeper challenge in decoupling was Event-Driven vs Event-Sourcing. Every business has weak-consistency requirements, yet most software still applies strong-consistency everywhere, even where it is not needed.
Kloia worked with the N11 software teams to shift from a monolith mindset to an event sourcing mindset. The teams used Kloia's open-source library Splitet to handle snapshots and rollbacks, which also drove the cultural shift from monolith to event sourcing.
Results
The N11 microservices transition started in 2017. After working through several challenges, the N11 team now has a high level of confidence to progress with CQRS and Event Sourcing architectures.
Case Studies
Open-Source Observability Transformation on AWS
Open-Source Observability Transformation on AWS