Agile Machine Learning


Understanding the potential

Machine Learning (ML) is here. It’s here today and it’s here to stay. Already, more and more organisations are getting to grips with its huge potential and starting to reap major benefits.

The benefits of Machine Learning include higher-accuracy predictions and more actionable insights, lower costs and higher turnovers, increased flexibility and improved agility, as well as far greater convenience and efficiency.

Data-driven organisations are discovering new and improved ways to solve problems and challenges. They’re analysing data to extend their competitive edge still further and are well on their way towards digital transformation.

They’re in essence leveraging the fact that machines are far better and faster at identifying and extracting patterns in data than human beings are. Using these patterns, you’ll gain invaluable knowledge and insights that previously lay buried and unused.

Unlocking the potential.

The knowledge and insights that Machine Learning unlocks can help your organisation in a wide range of situations including knowledge retention, decision making, prediction, sentiment analysis, anomaly detection and much more.

ML systems can learn how humans responded in similar situations in the past – invaluable if these decision makers are no longer available or have no time, or the number of similar decisions is now much greater. Moreover, the systems can analyse complex datasets with multiple variables that no human would ever be able to handle, let alone use to provide accurate recommendations (not to mention the associated time and cost).

Commonly implemented ML solutions involve personalising and refining online recommendations to boost e-commerce sales, analysing sentiment in articles, sifting through huge volumes of transactional data to detect fraud or implementing chatbots to provide first-line helpdesk support.

Once your organisation starts to see results, you’ll truly start to grasp machine learning’s full potential and want to leverage this on many other projects.

implementation is key

Trifork Machine learning

The Trifork model of Agile Machine Learning


Taking your first steps towards implementing Machine Learning isn’t as difficult as you might think, especially with Trifork at your side! The organisations we’ve helped so far all started where you are today.

Start the way they did by generating curiosity and awareness about ML’s potential and its value to your business and staff alike.

Next, create awareness of the huge volume of siloed and unused data available inside and outside your organisation and how you could be using it to your advantage.

Educate colleagues about the types of problem that ML can already solve based on past business cases and spot any similarities within your own organisation. And above all, involve as many people and parts of your organisation as possible to get fresh perspectives and new ideas.

Start identifying specific areas of your organisation that could benefit from ML’s core strengths – anything that involves knowledge retention, decision-making, prediction, sentiment analysis, anomaly detection…

-> Take one of Trifork’s ML workshops to discover where and how ML could apply to your business. (fill in the form to receive information about the discovery)

-> Start embedding ML skills into your organisation to prepare for future demand.

-> Monitor and report your progress on an ongoing basis using KPIs and dashboards.


Trifork wholeheartedly believes that business comes before technology.

This is why we take the time to understand your business properly and why we listen intently to your needs, concerns and challenges.

It’s crucial that we get to the crux of any issue affecting your business before anything else.

When implementing ML, we work in sprints, as we do for our Enterprise Application Development. Sprints that rapidly generate tangible results with a measurable impact on your business. We repeat this process until we get you the results you’re looking for.

Working in this way means that Trifork can respond rapidly and adapt accordingly – constantly focusing on making iterative improvements.

Something we never lose sight of are actionable results. We don’t stop at theory, we always help you put theory, or a model, into practice – directly improving your processes and creating true added value.

Our approach is a closed loop. We’ll be at your side to identify the problem at hand, develop an ML solution and ultimately scale this solution to your production environment.

We regularly organise meet-ups and accelerator workshops to guide your business swiftly from awareness through solutions to scalable systems.

Of course, we’re always available to assist and coach you through the process of implementing ML within your organisation. We’re here to lead the way.

Predictive Approach to Machine Learning Functionality

WEBINAR (recording)

Listen to the Trifork webinar below and learn how to apply Machine Learning to deliver new functionality for a complex application given limited time.

This is a recording to explain the basics of Machine Learning to you. If you would like to be introduced to Machine Learning for your organisation, leave your details and we will contact you!

Fill in your details and receive the information about how to get started with Machine Learning/Artificial Intelligence:

Trifork always treats your personal details with the utmost respect and use them to respond to all your requests. Please notify us if you wish to be excluded from our database.

Predictive approach to Machine Learning functionality


For example
… a service centre to customise and optimise its decision-making processes, increasing its customer satisfaction levels?

… a service company to predict machinery maintenance requirements, improving its bottom line?

… a manufacturing facility to check its production process and finished product quality levels, slashing its reject rate?

… a law firm to extract information from its contracts and identify changes, reducing the need for human input?

… a call centre to implement a virtual assistant to help its new staff make better decisions?

… a healthcare provider to evaluate and recommend courses of medical treatment based on past cases, improving its patients’ quality of life?