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Artificial Intelligence

The AI revolution is upon us, and it's never too late - or too early - to integrate AI into your software platform.

Technology Services


How your company can benefit from AI

Artificial Intelligence and Machine Learning are transformational technologies that allow for business efficiencies and insights that were previously unobtainable.

Do you want to eliminate the manual review and analysis of large documents? Detect anomalies in images, financial transactions, or transportation networks? Recommend products or services based on a customer's habits and data? Leverage AI for these - not traditional algorithms.

Convergence AI Labs can model, engineer, train and deploy AI solutions - bespoke or off-the-shelf - that enable game-changing efficiencies and business models.


AI technologies

Businesses commonly use AI to predict trends and outcomes through pattern recognition, anomaly detection and sentiment analysis. Stay ahead of the curve by introducing Artificial intelligence into your business model. It's never too late or too early to do so.


We turn your data into

intuitive AI tools

that anyone can use.


Official AWS Partner

AWS Select Consulting Partner Badge

Behind all business-grade platforms is a robust cloud-based infrastructure. Convergence is both an AWS Select Consulting Partner and an AWS Public Sector Partner - we have deep knowledge in the architecture, development and management of the systems that your software ecosystem requires in order to scale.


AI development lifecycle

Our AI development team will guide you where can benefit from artificial intelligence solutions. From manufacturing to real estate, social media to medical, we've worked in many sectors and have developed AI-powered components for a wide variety of applications. The AI revolution is upon us, and it's never too late - or too early - to integrate AI into your business.


Define the problem

Ask yourself this simple - yet critically important - initial question: is there a pattern? Define the type of AI problem, and how solving it achieves your business goals.


Collect data

The lifeblood of AI is data. Without data, there is nothing to train AI models with. However, data is a challenge for AI projects; 80% of the work in an AI project, on average, is collecting and preparing data.


Prepare data

In order to properly prepare the dataset for AI projects, the data stage is broken into three parts: data requirements, data collection, and exploratory data analysis (EDA).


Train the AI model

Data is like the crude oil of machine learning, meaning that it has to be refined into features - predictor variables - in order to be useful for training a model.



No measurement? No improvement. To determine how well an AI model is performing, start by establishing different metrics to compare and contrast the model's predictions against.


Deploy & improve

Allow users to interact with the AI models. The models should be deployed in such a way that they can be used for inference as well - and they should be updated regularly.


Custom vs ready-made AI

Should your business build custom AI, or use an Artificial Intelligence as a Service (AIaaS)? For smaller projects, such as prototypes, including AI may seem far-fetched. This is not true. By leveraging a host of cloud-based AI-powered services - such as AWS SageMaker or Google's Cloud AI suite of services - we are able to integrate a wide variety of AI functionality within the footprint of a smaller budget.

There are, however, trade-offs to consider when deciding between custom and ready-made AI engines. Together let's see how Convergence and the power of artificial intelligence can accelerate change in your organization.

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