Generative AI

Business Scenario

In recent years, Artificial Intelligence (AI) has been continuously evolving and Generative AI stands out as one of its most captivating applications. This groundbreaking technology has ushered in a new era of creative possibilities, where machines can not only comprehend data but also generate new content autonomously.

Generative AI is a subset of AI that involves training algorithms to produce data that resembles a given dataset. However, unlike traditional AI models that rely on pre-defined rules, Generative AI leverages complex neural networks to learn patterns and generate new content organically.

It is this ability to create new and innovative content that has opened a multitude of opportunities across various domains. From generating art and music to producing human-like text and images, Generative AI holds the potential to revolutionise several industries:

Content Generation

Creating personalised movie recommendations, generating music, or crafting interactive storytelling experiences are some examples of generating compelling content through Generative AI;

Simulation and Training

In scientific research and engineering, Generative AI plays a crucial role in simulating complex systems, enabling researchers to explore scenarios that might be challenging to achieve experimentally;

Healthcare and Drug Discovery

Generative AI has the potential to revolutionise healthcare by assisting in drug discovery, predicting patient outcomes, and generating synthetic medical images for training medical professionals;

Natural Language Processing

Language models like GPT-3 have demonstrated impressive capabilities in generating human-like text, which can be harnessed for chatbots, language translation, and code generation.

As researchers and developers continue to innovate, Generative AI is set to redefine human-machine interactions and transform industries across the globe.

Our Approach

How is Celfocus using Generative AI?

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Powered by Generative AI, Celfocus Smart Customer Support Framework is revolutionising customer service and personalising experiences. It enriches data through a set of “Generative Skills” via REST API, which can be queried in real time to fulfil different use cases.

“Generative Skills” are also developed with the principle of minimising the impact of an ever-changing Large Language Model (LLM) (the same prompt may have one answer today, and a different answer tomorrow), that is, leverage new/different LLM versions, while maintaining response coherence.

Celfocus’s framework does not stop with Generative AI – it leverages strategically enriched information to facilitate the application of advanced insight generation and state-of-the-art AI models for tasks like prediction or root cause analysis.

Highlights:

Leveraging Open AI technologies to elevate customer experience;
Using advanced analytics for more robust and complex use cases;
One smart framework for multi-channel interactions in Customer Support.

image example: Generative AI Smart Customer Care Framework

Celfocus Smart Customer Support Framework Use Cases

Below are some examples of use cases where Generative AI can be used to deliver measurable benefits and improve customer experience.

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Case Study

Augmented Customer Support

The client, a Tier 1 CSP, aimed to improve:

Customer satisfaction and TNPS by reducing deep detraction and churn rate;
Contact center efficiency with minimal wait and response times and quick problem resolution.

The solution delivered an augmented customer support framework. This framework gathers data from all the interactions from all the channels and uses GenAI and ML capabilities to generate insights based on real-time transcripts, seamlessly integrated within the existing technology stack across various channels.

The results included a 5% increase in customer satisfaction, a 2.5% reduction in churn rate, a 75% increase in call deflection rate, and a 50% reduction in call audit time.

Case Study

Invoice Comparison

A Tier 1 Communications Service Provider (CSP) faced a challenge with 24% of customer calls being related to billing enquiries.

To address this, a GenAI-powered invoice analytics solution was implemented, providing real-time, natural language explanations of billing discrepancies. Initially deployed for contact center agents, the solution significantly reduced call handling times by enabling agents to quickly resolve billing issues.

After a phase of human validation, the feature will be made available in non-assisted channels, allowing customers to resolve billing enquiries on their own. This approach is expected to provide a 10% reduction in the total number of contact center interactions due to the deflection of billing-related calls.

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Case Study

Field Service Recommendations

A Tier-1 Communications Service Provider (CSP) aimed to reduce the number of second site visits, which were at 6% and had a negative impact on customer satisfaction and costs.

The solution leverages GenAI to categorize historical work order information to train a recommendation algorithm using deep learning. This algorithm provides recommendations to technicians in the field based on the issue description.

The initial results were very promising, demonstrating real benefits. Using work orders as the sole data source, the solution achieved a 28% reduction in second site visits, which corresponds to a 2-5% reduction in Field Service costs.

The initial solution is now being enhanced by correlating additional data sources, such as network data, to improve the success rate of recommendations even further, with the expectation of increasing the reduction to 30-35%.