Case Studies
A curated list of case studies to illustrate CTS's range of capabilities and resources.
Client: A major Telecommunications company
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The Challenge: The Telco was looking to optimize its public cloud expenditures on AWS and GCP in mid-2019. The challenge was to identify usage patterns and optimize spending without compromising performance or scalability.
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The Solution: An ML-based recommendation system was developed to analyze usage patterns and optimize cloud spending. Using an early version of a tool called CloudBolt, baseline cloud usage patterns were extracted, which were then used by custom developed ML algorithms to generate usage forecasts.
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Implementation and Results: The deployed system learned the "normal" usage and behavior patterns of the Telco’s cloud resources. By recognizing these patterns, the system could provide demand suggestions for right-sizing the environment.
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Cost Savings: Over the course of a year, the telco saved 15% on its AWS and GCP cloud migration costs. This was achieved through automated elasticity, reduction of wastage, and reclamation of temporary resources.
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Enhanced Efficiency: The system's automated recommendations and adjustments ensured that the cloud environment was always optimally sized, reducing unnecessary expenditures and improving overall efficiency.
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Impact: Implemented in 2021/2022, the ML-based cloud cost optimization system delivered significant cost savings and operational efficiency.
Client: Confidential
Challenge: In the late 2000s, this client sought an advanced cybersecurity solution to detect and respond to anomalies in their network. The goal was to develop a system that could establish normal behavior baselines and identify significant deviations in real-time, akin to modern Security Information and Event Management (SIEM) systems.
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Solution: A sophisticated ML-based baselining and threat-hunting system was developed. The solution leveraged logs from multiple sources including firewalls, IDS/IPS systems, Routers, Servers, and Netflow traffic. The system established baselines representing "normal" behavior and monitored these baselines in real time to detect anomalies.
Implementation and Results:
Baseline Establishment: The system analyzed logs from various sources to establish normal behavior baselines, which would be then used for comparison later on to detect anomalous behavior.
Real-Time Monitoring: The system continuously monitored network activity against the established baselines. Significant deviations were detected in real-time, enabling prompt identification of anomalies.
Rapid Alerts and Automated Responses: Administrators were alerted within 30 minutes of detecting an anomaly via both email and SMS gateway integration. Additionally, automated threat responses were configured based on past experiences and threat intelligence, ensuring swift action against potential threats.
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Impact: The implementation of this ML-based cybersecurity solution significantly enhanced the client's ability to detect and respond to threats. By automating threat detection and response, the system reduced the time to identify and mitigate potential security incidents, thereby strengthening the overall security posture.
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This case study demonstrates the effectiveness of early ML-based threat-hunting systems in cybersecurity, highlighting the ability to proactively identify and respond to anomalies, much like modern SIEM solutions.
Client: A National Carrier
Challenge: The Airline faced difficulties in accurately predicting their on-premises infrastructure requirements. They needed to manage growing application workloads while considering various external factors such as socio-political-economic conditions, macroeconomic growth, and industry trends. The goal was to forecast infrastructure needs over the next three years to refine procurement strategies and ensure scalability.
Solution: A comprehensive analysis was conducted, examining historical usage patterns and the growth of application workloads. This analysis was cross-referenced with the broader business environment, where common growth factors were identified and used to predict future infrastructure requirements with a high degree of accuracy.
Implementation and Results:
Accurate Forecasting: By identifying and leveraging common growth factors, the predicted on-premises infrastructure requirements were successfully forecasted for over three years.
Optimized Procurement: The accurate forecasts allowed the Airline to refine its procurement strategies. Investments were staggered and aligned with infrastructure design and scalability needs, preventing over-provisioning and ensuring cost-effective scaling.
Enhanced Resource Profiling: Detailed resource profiling and visibility into usage patterns enabled the implementation of a chargeback function from IT. This transformation allowed the IT department to operate as a profit center, attributing costs more effectively to different business units.
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Impact: The solution not only optimized the client's infrastructure investments but also transformed the IT department into a profit center, enhancing overall operational efficiency and financial management.