.svg "kloia.png"){kind=logo}
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 - LUMO
How Lumo Uses Generative AI to Automate Irrigation Decisions
50%
Reduced support dependency
99%
Real-time data access
Client: Lumo
Project type: AI
Website: www.lumo.ag
Situation
Lumo is an agricultural technology company that optimizes irrigation through intelligent automation. Traditional irrigation systems were inefficient. Farmers wasted water, missed yield targets, and planned without real-time data.
The existing platform could not adapt irrigation strategies to real-time weather or flow anomalies. This led to suboptimal water usage and delayed responses. Farmers relied on support teams to access irrigation logs, clarify anomalies, and confirm upcoming schedules. Most routine questions required manual intervention.
Task
Lumo needed a business-led transformation of irrigation management using generative AI. Two needs were clear:
- Modernization. Modernize the architecture to be modular, observable, and scalable.
- Access. Introduce a natural language interface so farmers could access irrigation data and system status in real time.
Delivering both required decomposing the application into modern microservices and integrating AWS-native services. Lumo engaged kloia for this expertise.
Action
kloia built a generative AI chatbot on Amazon Bedrock to handle natural language queries such as “Show me my last 4 irrigations.” The system was designed for real-time analytics, self-service access, and cloud-native scale.
Technologies used:
AWS Bedrock
Core generative AI models for the chatbot's natural language understanding and response generation.
AWS Amplify
Deployment and hosting layer for the React-based chatbot frontend.
AWS CloudFront
Hosting for the static web assets of the frontend.
AWS Lambda
Hosting for the SQL Agent and the connection to the databases. The SQL Agent lets the chatbot query Lumo's databases directly for robust reporting and streamlined farmer data management.
AWS CloudWatch
Monitoring for application performance, logging, and system health.
AWS Relational Databases
Storage for the customer databases.
Results
The generative AI assistant now handles routine queries in real time. Farmers retrieve irrigation insights within seconds using natural language, without navigating complex dashboards or contacting support.
Sample interaction
“Show me my last 4 irrigations.” → Tabular data + AI-generated summary + variance detection.
Business impact:
- Support. Support dependency reduced by over 50% through self-service.
- Access. Detailed irrigation data now accessible in real time via natural language.
- Operations. Reduced manual intervention and faster response to irrigation anomalies.
Case Studies
Open-Source Observability Transformation on AWS
Open-Source Observability Transformation on AWS