AI & Machine Learning

AI is the ability of machines to show human-like intelligence through the collection and analysis of enormous digital information that enables these machines to perceive, self-learn, reason, and make decisions like humans.

AI & Machine Learning

AI is the ability of machines to show human-like intelligence through the collection and analysis of enormous digital information that enables these machines to perceive, self-learn, reason, and make decisions like humans.

The opportunity for AI in business stems from the availability of big data, high-powered computing tools, cloud compounding, advances in algorithms, and the need for advancement in the areas of machine translation, object perception, and object recognition causes. Through a multi-skilled lens, Convergenc3 AI consultants can take on different roles at various points in your organisation’s AI journey. Our technical engineers understand the detail in the context of a wider strategy and business ecosystem. We aim to guide you in driving true business value instead of focusing on vanity use cases for AI.  Through careful insight into your business environment and level of data quality, we will craft solutions tailored to your needs.

Artificial intelligence (AI) and machine learning are two terms that are often used interchangeably, but they are two distinct concepts. AI is a broad umbrella term that refers to any type of computer system that can perform tasks that typically require human intelligence, such as visual perception, natural language processing, and decision making. Machine learning, on the other hand, is a subset of AI that refers to the ability of a computer system to improve its performance on a given task through experience.

There are many challenges businesses are currently facing when it comes to AI & Machine Learning. One challenge is the lack of data. In order to train a machine learning algorithm, businesses need a large amount of data that is high quality and labelled. Another challenge is the lack of expertise. Machine learning is a complex field and businesses often do not have the in-house expertise to develop and deploy machine learning models. Finally, machine learning models are often opaque, which makes it difficult for businesses to understand how and why the model is making certain predictions.

Why AI

Why AI?

The availability of big data, high-powered computing tools, cloud compounding, and advances in algorithms and the need for advancement in the areas of machine translation, object perception, and object recognition causes.

Artificial Intelligence encompasses all the capabilities to reshape the future fundamentally.

It will have a positive impact on global problem areas such as mobility, healthcare, education, agriculture, security, poverty, and resource management.

Investments in AI are growing fast, especially by big tech giants globally.

AI has been used as an enabler for organisations that are leveraging artificial intelligence to extract valuable insights from a large set of data for providing innovative products and improving customer experience.

Why AI?

Why AI

The availability of big data, high-powered computing tools, cloud compounding, and advances in algorithms and the need for advancement in the areas of machine translation, object perception, and object recognition causes.

Artificial Intelligence encompasses all the capabilities to reshape the future fundamentally.

It will have a positive impact on global problem areas such as mobility, healthcare, education, agriculture, security, poverty, and resource management.

Investments in AI are growing fast, especially by big tech giants globally.

AI has been used as an enabler for organisations that are leveraging artificial intelligence to extract valuable insights from a large set of data for providing innovative products and improving customer experience.

Why C3 AI?

Convergenc3 business engineers form part of an engineering team backed by experience in different industries most prominently financial services.

Through this multi skilled lens C3 AI consultants can take on different roles at various points in your organization’s AI projects. Our technical engineers understand the detail in the context of a wider strategy and business ecosystem

As a specialty, we leverage this experience and understanding to ask the important questions, and to recommend and implement initiatives that take your business technology to the next level.

No matter which project phase (discovery, design, implementation), or which area of expertise (architect, design, develop, deploy), our C3 AI consultants, who are trained on the latest AI and ML tools, can create laser focus and step in and out of the business roles associated with different types of AI projects.

Why C3 AI

Why C3 AI?

Why C3 AI

Convergenc3 business engineers form part of an engineering team backed by experience in different industries most prominently financial services.

Through this multi skilled lens C3 AI consultants can take on different roles at various points in your organization’s AI projects. Our technical engineers understand the detail in the context of a wider strategy and business ecosystem

As a specialty, we leverage this experience and understanding to ask the important questions, and to recommend and implement initiatives that take your business technology to the next level.

No matter which project phase (discovery, design, implementation), or which area of expertise (architect, design, develop, deploy), our C3 AI consultants, who are trained on the latest AI and ML tools, can create laser focus and step in and out of the business roles associated with different types of AI projects.

Benefits of Implementing AI/ML in business

  • Improved Customer Experience Personalisation

  • Effective Work Processes Automation

  • Powerful Predictive Ability

  • Reasonable Resource Planning

  • Easy Changes within Company

  • Fast Adaptation to Market Changes

  • Advanced Customer Support

  • Increased Data Security

  • More Productive Staff Training

  • Efficient Data Management

C3 AI/ML Approach

Documents/Updates

Update Progress Report On Data Analysis

Update Progress Report On Model Selection

Update Progress Report On Model Analysis

Close Out Documentation

C3 AI/ML Approach

Case Study – Email Classification

Convergenc3 AI has built an email classification solution for one of the top 3 largest short-term insurers in South-Africa.

The Problem

Case study The Problem

The department identified that the culture must be improved and there exists demotivation due to various factors such as weak communication, workload, understaffing, overall happiness, and accountability to name a few.

The problem highlighted by the key stakeholder was that culture audits are overlooked and there is no method to analyse culture concerns with a quick turn-around time.

The Outcome

Case Study The outcome
Before-and-After-AI-v2

Final Comments

Case study final comments

Though this model is promising showing a 78% accuracy, accuracy could be increased by exploring and researching alternative classification models or different
multiclass classification tools. For the time being, the client was very satisfied with the outcome.

Case Study – Culture Survey

Case-study-Culture-Survey

Client: One of the top 3 largest short-term insurers in South Africa

This project is constructed from the feedback received from the individuals inside a specific department within an insurance company where they were tasked to provide their honest opinions and concerns regarding the culture to the Convergenc3 team.

The reason for this project is to form a culture to promote motivation and teamwork. Creating an efficient culture will enhance talent use and employee productivity, and ultimately create a shared vision of greatness. The project relied on the use of AI to analyze employee emails to identify key concerns.

The Problem

Case study The Problem

The department identified that the culture must be improved and there exists demotivation due to various factors such as weak communication, workload, understaffing, overall happiness, and Accountability to name a few. The problem highligvhted by the key stakeholder was that culture audits are overlooked and there is no method to analyze culture concerns with a quick turn-around time.

The Outcome

Case Study The outcome

C3AI proposed using Microsoft Azure machine learning studio to easily and quickly analyze feedback. Key phrase extraction is one of the features offered by Azure Cognitive Service for Language, a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. C3AI built the model, to extract the most prevalent issues, and developed a model that easily reports the result in five key dimensions i.e. Performance, Leadership, Culture, Foundation, Clarity. The model can be re-used with ease and eliminates the need for timely manual work.

Final Comments

Case study final comments

The model shows the overall team health with ease and provides the ability to clearly indicate in which areas management should focus on to create an improved culture and productive workforce. The model can be re-used, with ease, and provides the ability to identify growth. The model provided management the ability to drill down into key issues and provides the base for change management and focus areas.