Cognitive Operations: Innovative processes driven by intelligent systems for enhanced efficiency.

Cognitive Operations

 From Insight to Action: Leveraging Cognitive Operations for Business Success 

Business ScenarioOur ApproachBenefits

The gap between the accumulation of data and its effective use represents a fundamental challenge, which requires innovative solutions to bridge the divide between the abundance of information and actionable intelligence.

Business Scenario

Organisations across industries are under constant pressure to cut costs and operate efficiently at scale. To navigate this landscape effectively, enterprises are increasingly recognising the inherent value within their data ecosystems. Leveraging this data potential presents an opportunity for real-time situation detection, empowering swift and precise analyses, facilitating informed decision-making, and supporting automated resolution processes. 

However, despite the widespread collection of massive amounts of data, many organisations face a common hurdle – they lack the necessary tools and expertise to transform raw data into actionable insights that drive business success.  

Consequently, companies are unable to harness the full potential of data to reduce expenses, raise service standards, and increase the overall perceived customer experience, ultimately missing out on interesting business prospects that could increase their revenues. The gap between the accumulation of data and its effective use represents a fundamental challenge, which requires innovative solutions to bridge the divide between the abundance of information and actionable intelligence.

Our Approach

How Celfocus is Helping Enterprises with Cognitive Operations

Celfocus’s Cognitive Operations Offer empowers organisations to maximise the potential of their data, transforming it into a strategic asset.

Our comprehensive end-to-end solutions and services are helping our customers solve several problems, such as:

  • Data Foundations: Preparing or evolving foundational capabilities to collect, process, store, serve, govern, and analyse data at scale. 
  • Data Flows: Designing and implementing data flows from sources using fit-for-purpose strategies that are supported by data lineage techniques to trace data across its lifecycle. 
  • Data Quality: Ensuring data accuracy and consistency for analysis and decision-making that are consistent and reliable for better results. 
  • Insights Generation: Leveraging different strategies to extract meaningful and actionable insights from data patterns. These effective strategies allow the execution of critical operational functions, such as Impact Analysis, Anomaly Detection, Predictive Analysis, Root Cause Analysis, and more. 
  • Insights Automation: Seamlessly integrating insights into operational workflows/automation scripts to implement closed-loop, continuous improvement and achieve Zero-Touch for operational processes. 
  • Machine Learning Model Industrialisation: Creating a streamlined framework for deploying, scaling, and maintaining Machine Learning Models within AI-powered service and network analytics infrastructure. 
  • Leveraging Gen-AI: Introducing state-of-the-art tools and techniques to enhance operational data strategy. It is important to note that no tool is a silver bullet and that implementing these techniques always requires a tangible business case in mind.

To positively transform day-to-day operations through advanced analytics, Celfocus believes that heterogeneous teams with a mix of Data Engineers, Data Scientists, and Domain Subject Matter Experts are imperative. This provides the team with a thorough understanding of the technology and data and/or business processes, which are key to following our transformation approach:

Data Assessment and Road Map

Evaluating existing data landscape, tooling, and data platforms while considering the organisational strategy to identify areas for improvement and develop a feasible roadmap. 

Platform Development and Enhancement

Designing/implementing or enhancing the existing platforms and capabilities that support seamless data collection, storage, and processing, ensuring data is readily available for actionable insights.

Data Quality Enhancement

Implementing data lineage processes and anomaly detection mechanisms to ensure the accuracy, consistency, and reliability of data, providing a solid foundation capable of producing insights.

AI/Machine Learning Use Case Design

Data scientists and SMEs collaborate closely with business stakeholders to design and implement use cases that address operational and engineering challenges, with tangible business outcomes.

Proven Machine Learning Models for Efficiency Enhancement

Leveraging our experience in developing and applying Machine Learning Models to optimise processes, troubleshoot issues, predict equipment failures, streamline field services, and more, driving efficiency and cost savings across your organisation.

Additionally, depending on the nature of the challenge, Celfocus may also require roles that focus on the transformation aspects of the organisation to help with the smooth transition to the future mode of operations.

 Intelligence Intent | Operations | Celfocus


By leveraging Celfocus’s data approach, we can achieve several tangible benefits for our clients’ organisations:

<5 mins anomaly detection in up to 1bn devices down from 40m-
90m MTTD, improving service quality for IoT Enterprise
82% zero-touch automation on 1st line RAN Ops from issue
detection to resolution on network devices;
93% truck roll avoidance by identifying tickets that can be
resolved remotely, saving unnecessary costs;
80% traffic and congestion prediction up to 28 days ahead,
enabling dynamic resource allocation;
>25% revenue increase using optimal policies for 5G network
18% reduction of unnecessary repeated field service
interventions at the customer’s home.
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Use Cases

Network Assurance/Analytics (RAN, FTTH, …)

Process data from Telco ecosystems to perform network root-cause-analysis using data sources such as faults, inventory, incidents, and changes, and more, to automatically flag incidents and problems to operational dashboards and automation frameworks for follow up. 

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Use Cases

Service Assurance/Analytics (IoT, DTV, VoLTE, ...)

Process and report substantial amounts of service usage records, keeping KQI and KPIs under control while detecting service usage patterns, flagging potential customer service issues to operational dashboards and automation frameworks for follow up.

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Use Cases

Intelligent 5G Core Analytics

Vertically monitor core services from NFV applications to the Telco Cloud in use by applying Machine Learning Models that proactively detect anomalies, prevent degradation, reduce downtime, and improve performance, providing insights to operations that can also be automated for further efficiency.

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Use Cases

Customer Support Augmentation

Using Large Language Models to elevate customer experience and automate customer interactions, providing more natural and accurate answers and resolving issues efficiently (sentiment analysis, intent recognition, call summarisation, real-time troubleshooting, and suggestion and deep detractors’ prevention).

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Use Cases

Network Engineer copilots

Provide engineers with organisation-contextual answers to resolve and/or automate problems by answering natural language prompts with actions (automation scripts, service SLAs, etc.), aligned with the organisation’s blueprint, while ensuring that all data remains private.

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Use Cases

Proactive & predictive maintenance

Of network equipment (CPE, routers, switches, and others) by analysing multiple parameters using ensemble algorithm techniques to identify the probability of failure and flag recommendations for operations to follow up.

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