KeyKibotos - Enterprise Infrastructure That Works

Real Results
For Real Companies

EDF

Energy & Utilities • 38M customers across Europe

Company Overview

Leading European energy company serving 38 million customers across multiple countries with critical infrastructure requirements.

Business Challenge

EDF's energy operations generated massive volumes of sensitive internal data from nuclear plants, renewable sources, and smart grid infrastructure across European markets - all requiring strict security and on-premises processing.

Key Requirements

Real-time energy production decisions needed for 38M customers
Regulatory compliance across multiple EU markets (France, UK, Belgium)
Critical infrastructure monitoring for nuclear and renewable plants
Secure processing of internal operational data on private infrastructure

Technical Constraints

Data Volume: 500GB daily from 200+ SCADA systems, smart meters, production databases
Processing Delays: 8-12 hour batch processing preventing real-time decisions
System Performance: Monolithic architecture on-premises with 85% CPU utilization
Manual Processes: 40+ hours monthly manual reporting per EU market

Our Solution

We built a modern containerized data platform on private cloud infrastructure, replacing their legacy on-premises system while keeping sensitive internal EDF data secure.

What We Built

Data Ingestion Layer: Custom Airflow providers for multiple SCADA/smart meter sources processing internal EDF data
Data Lineage Platform: OpenMetadata deployment tracking data flow from sensors to dashboards
Container Platform: Multi-region Kubernetes clusters on private cloud infrastructure
Monitoring Stack: Real-time alerting for critical metrics

Technical Implementation

Infrastructure: Private cloud across multiple data centers, auto-scaling Kubernetes nodes
Data Lineage: Complete data lineage with OpenMetadata platform
Dashboards: Custom Grafana dashboards for energy operations tracking internal data flows

System Integration

SCADA Systems: Read-only integration with 200+ operational control systems
Oracle Databases: Change Data Capture (CDC) pipeline for real-time sync
Regulatory Systems: API integration with EU compliance reporting platforms
Security compliance: Secure data extraction with safety protocol compliance

Tech Stack: Apache Airflow • OpenMetadata • Kubernetes • Docker • ArgoCD • Helm • Prometheus • Grafana • Python

Results & Outcomes

Before (Legacy System)

8-12 hour processing delays
85% CPU utilization at peak
40+ hours monthly manual reporting
Weekly system maintenance downtime

After (Our Platform)

15-minute processing window
45% average CPU (auto-scaling)
2 hours automated reporting
99.2% uptime (zero maintenance downtime)

Business Impact

95% Faster Processing

35% Less Outages

95% Report Automation

Groupe SNCF

Transportation & Logistics • 15M daily passengers

Company Overview

France's national railway company serving 15 million passengers daily with complex logistics and real-time operational requirements.

Business Challenge

SNCF's railway operations needed to scale for 15M daily passengers across France's extensive rail network.

Key Requirements

Passenger service quality declining during peak travel times
Operational efficiency gaps affecting on-time performance
Real-time decision making needed for train scheduling and delays
Competitive pressure from other transportation modes

Technical Constraints

API Response Time: 3-8 seconds for passenger queries during peak hours
Database Capacity: Single PostgreSQL instance at 95% capacity (500GB daily writes)
Deployment Cycles: 3-week releases due to tightly coupled monolithic services
Real-time Gaps: 30-minute delays in operational dashboards affecting passenger experience

Our Solution

We decomposed the monolith into 12 domain-specific microservices with event-driven architecture.

Core Microservices Architecture

Domain Separation: 8 bounded contexts (passenger, freight, operations, maintenance)
Event-Driven Design: Real-time communication between services via Kafka
API Gateway Pattern: Centralized routing and authentication for passenger services
Database Per Service: Independent data stores with read replicas

Technical Implementation

Passenger API Gateway: Scala service handling 1M+ daily requests (sub-500ms response time)
Real-time Event Streams: Kafka cluster processing 50K events/second (train positions, delays)
Caching Layer: Redis implementation achieving 99.9% cache hit rate
Database Strategy: PostgreSQL read replicas + write separation

Legacy Systems Integration

Railway Scheduling System: Event-sourced integration with core operational systems
Mainframe Billing: Asynchronous messaging for financial transactions
Station Management: API gateway pattern for distributed station systems
Train Control Systems: Real-time position and status data integration

Tech Stack: Scala • Apache Kafka • AWS MSK • Lambda • PostgreSQL • Redis • Hexagonal Architecture

Results & Outcomes

Before (Monolith)

3-8 second API response times
3-week deployment cycles
30-minute operational delays
95% database capacity

After (Microservices)

Sub-500ms API responses
2-hour deployment cycles
Real-time operational data
60% database utilization

Business Impact

85% Faster APIs

95% Faster Deployments

€150K Annual Savings

Stellantis

Automotive & Manufacturing • 500K+ connected vehicles

Company Overview

Global automotive manufacturer with 500K+ connected vehicles requiring real-time analytics and predictive maintenance across multiple continents.

Business Challenge

Stellantis needed to transform 500K+ connected vehicles into a competitive advantage through data-driven insights.

Key Requirements

Predictive maintenance to reduce warranty costs and improve vehicle reliability
Fleet optimization across multiple automotive brands and global markets
Real-time vehicle safety monitoring and emergency response coordination
Compliance with varying data privacy regulations across continents

Technical Constraints

Data Volume: 2TB daily from 500K+ connected vehicles across multiple continents
Processing Speed: Near real-time processing required for vehicle safety alerts
Global Deployment: Multi-region infrastructure with data residency compliance
Algorithm Complexity: Predictive maintenance models processing engine diagnostics and environmental data

Our Solution

We created a comprehensive connected vehicle platform that transforms raw telemetry into business intelligence.

Core Platform Components

Streaming Analytics: Real-time processing of vehicle telemetry and sensor data
ML Pipeline: Predictive maintenance models for component failure prediction
Event Processing: Critical alert handling and emergency response coordination
Multi-Region Architecture: Global deployment with data residency compliance

Data Processing Infrastructure

Spark Streaming: AWS EMR platform with dynamic auto-scaling based on vehicle data patterns
ML Pipeline: Processing engine diagnostics, driving patterns, environmental data
Prediction Accuracy: Component failure prediction 2-4 weeks in advance (87% accuracy)
Event Processing: 10M+ vehicle events per hour with real-time alerting

Multi-Region Deployment

Data Residency: GDPR compliance and local data protection requirements
Cost Optimization: Intelligent data tiering and archive strategies (35% cost reduction)
Geographic Distribution: Vehicle data processing across multiple continents
Emergency Response: Real-time accident detection and coordination systems

Tech Stack: Apache Spark • AWS EMR • Kinesis • MLflow • Python • Scala

Results & Outcomes

Before (Legacy System)

Reactive maintenance only
High warranty claim costs
Limited vehicle data insights
Manual emergency response

After (Our Platform)

2-4 week failure prediction
23% reduction in warranty claims
10M+ events/hour processing
Real-time emergency coordination

Business Impact

87% Prediction Accuracy

35% Cost Reduction

$50M Annual Savings

Groupe SeLoger

Real Estate & PropTech • 50M+ property listings

Company Overview

France's leading real estate platform processing 50M+ property listings with complex ML-driven valuations and real-time market analytics.

Business Challenge

SeLoger needed to modernize their data infrastructure to maintain market leadership in French real estate.

Key Requirements

Competitive pressure requiring faster property listing updates
User experience impacted by slow search and valuation features
Rising cloud costs affecting profitability margins
Data quality issues affecting property valuations and customer trust

Technical Constraints

Data Volume: 50M+ property listings requiring real-time processing
Processing Delays: 8-hour batch processing delays impacting user experience
Cost Efficiency: High AWS costs with inefficient resource utilization
Data Integration: Hundreds of real estate agencies with different data formats and quality standards

Our Solution

We built a scalable data platform with FinOps cost optimization and real-time processing.

Core Platform Components

Data Lake Architecture: AWS S3-based platform with real-time ingestion
FinOps Implementation: Auto-scaling and cost monitoring for resource optimization
ML Pipeline: Property valuation models with improved accuracy
Data Integration: Unified platform for real estate agency onboarding
Streaming Ingestion: Kafka-based real-time data pipeline for property updates
Spark Processing: Distributed processing for 50M+ property listings
ML Valuation: Scikit-learn models achieving 94% accuracy in property recommendations

Cost Optimization & Integration

Auto-scaling: Dynamic resource allocation based on listing volume patterns
Cost Monitoring: Real-time AWS cost tracking and optimization (40% reduction)
Data Tiering: Intelligent archiving strategies for historical property data
Resource Optimization: Spot instances and reserved capacity planning

Tech Stack: Apache Spark • Kafka • MLflow • Airflow • Python • Scikit-learn • AWS CloudFormation • AWS S3 • AWS EMR

Results & Outcomes

Before (Legacy System)

8-hour batch processing delays
High AWS infrastructure costs
Manual data quality processes
Limited property valuation accuracy

After (Our Platform)

15-minute listing publication
40% reduction in cloud costs
Automated data quality pipeline
94% property valuation accuracy

Business Impact

98.5% Platform Uptime

35% User Engagement

25% Premium Growth

Le Groupe La Poste

Postal & Logistics • 60M users, 300M+ daily events

Company Overview

France's postal service managing data for 60M users and processing 300M+ daily package events with strict privacy compliance requirements across multiple countries.

Business Challenge

Le Groupe La Poste needed to modernize data infrastructure to support 60M users across multiple countries while maintaining strict privacy compliance.

Key Requirements

Operational efficiency for postal and logistics services across Europe
Customer experience improvements for package tracking and delivery
Regulatory compliance across multiple jurisdictions (GDPR, local laws)
Cost reduction in legacy infrastructure and storage systems

Technical Constraints

Event Volume: 300M+ daily package events requiring real-time processing
User Scale: 60M users with strict privacy-by-design requirements
Storage Costs: High costs with legacy systems impacting profitability
Ingestion Speed: Slow data processing impacting operational efficiency

Our Solution

We developed a privacy-by-design data platform for 60M users with high-throughput processing.

Core Platform

Privacy-First Design: Built-in data protection and compliance from the ground up
High-Throughput Streaming: Real-time event processing for package logistics
Apache Kafka: High-throughput event streaming for 300M+ daily package events
Spark Processing: Distributed Scala-based processing with Mesos orchestration
Nested Data Handling: Matryoshka library for complex data structure processing
Storage Strategy: HDFS for historical data, Cassandra for real-time access

Privacy & Compliance

Data Minimization: Collect and process only necessary package and user data
Encryption: End-to-end encryption for all personal and logistics data
Access Controls: Role-based access with audit trails for compliance
Right to be Forgotten: Automated data deletion for GDPR compliance

Tech Stack: Apache Kafka • Spark • Mesos • Scala • HDFS • Cassandra

Results & Outcomes

Before (Legacy System)

Slow data ingestion speeds
High storage costs
Manual privacy compliance
Limited scalability

After (Our Platform)

2x faster ingestion speed
25% storage cost reduction
Automated GDPR compliance
300M+ daily events capacity

Business Impact

60M Users Managed

99.2% System Uptime

100% Privacy Compliance

BeIN Sports

Media & Entertainment • Millions of concurrent users

Company Overview

Global sports media company requiring high-performance backend systems for digital platforms serving millions across Europe and MENA regions.

Business Challenge

BeIN Sports needed to modernize digital infrastructure to compete in streaming sports media.

Key Requirements & Constraints

Handle massive traffic spikes during major live sports events
Improve user experience across web and mobile platforms
Enable real-time content delivery and engagement tracking
Scale infrastructure for global audience across Europe and MENA regions

Our Solution

We built a scalable, high-performance web infrastructure designed for sports media workloads.

Core Architecture

Microservices Design: Domain-driven services for sports content management
Container Platform: Docker-based deployment with auto-scaling capabilities
PHP & Symfony: Microservices for live scores, schedules, user management
WebSocket Services: Real-time live score updates and match notifications
Redis Caching: High-performance caching layer for live sports data

Tech Stack: PHP • Symfony • WebSocket • Docker • AWS ECS • PostgreSQL

Results

Before

System failures
Slow APIs
Limited capacity

After

99.8% uptime
95% faster APIs
500K+ concurrent users

Business Impact

500K+Concurrent Users

99.8%Uptime

95%Performance Gain

We BuildEnterprise InfrastructureThat Actually Works

How We TransformYour Infrastructure

Trusted by Global Enterprises

Our Solutions

Starter Package(Fast Wins)

Includes (pick 3–4 services):

Growth Package(Data & Security Ready)

Includes (5–7 services):

Enterprise Package(Full Transformation)

Includes (8–10 services):

Real ResultsFor Real Companies

EDF

Company Overview

Business Challenge

Key Requirements

Technical Constraints

Our Solution

What We Built

Technical Implementation

System Integration

Results & Outcomes

Before (Legacy System)

After (Our Platform)

Business Impact

Groupe SNCF

Company Overview

Business Challenge

Key Requirements

Technical Constraints

Our Solution

Core Microservices Architecture

Technical Implementation

Legacy Systems Integration

Results & Outcomes

Before (Monolith)

After (Microservices)

Business Impact

Stellantis

Company Overview

Business Challenge

Key Requirements

Technical Constraints

Our Solution

Core Platform Components

Data Processing Infrastructure

Multi-Region Deployment

Results & Outcomes

Before (Legacy System)

After (Our Platform)

Business Impact

Groupe SeLoger

Company Overview

Business Challenge

Key Requirements

Technical Constraints

Our Solution

Core Platform Components

Cost Optimization & Integration

Results & Outcomes

Before (Legacy System)

After (Our Platform)

Business Impact

Le Groupe La Poste

Company Overview

Business Challenge

Key Requirements

Technical Constraints

Our Solution

Core Platform

Privacy & Compliance

Results & Outcomes

Before (Legacy System)

After (Our Platform)

Business Impact

BeIN Sports

Company Overview

Business Challenge

Key Requirements & Constraints

Our Solution

Core Architecture

We Build
Enterprise Infrastructure
That Actually Works

How We Transform
Your Infrastructure

Starter Package
(Fast Wins)

Growth Package
(Data & Security Ready)

Enterprise Package
(Full Transformation)

Real Results
For Real Companies