Gain a competitive edge in the market!

Generative AI & Machine Learning

Most people know Artificial Intelligence (AI) from killer robots and ChatGPT. However, AI can do a whole lot more! By strategically implementing AI, companies can significantly enhance their operational efficiency, improve customer satisfaction, and gain a competitive edge in the market.

Work faster, better, and more aligned with your customer’s needs

Get ahead of the competition!

GenAI & Mendix, the way to give your business a competitive edge.

Increase operational efficiency

AI can automate long and repetitive tasks. This not only helps save time and money, it also increases employee productivity and job satisfaction. AI relieves employees from repetitive tasks and provides customers with answers faster.

Improve customer experience

Through AI businesses can provide customers with faster support for example using chat bots. AI can also analyze customer behavior and predict consumer needs in real-time. This allows businesses to alter their tools and services.

Enhance security & fraud detection

Based on data, AI can identify risks and understand them through Machine Learning. It can then help you choose safe passwords and even verify credit card-holders by detecting unusual devices and transactions, helping it to prevent credit card fraud.

Enable data-driven decision-making

AI provides real-time insights such as trends and correlations by analyzing vast amounts of data at high speeds. This supports faster, more efficient decision-making. Additionally, AI can help identify and analyze risk factors and provide mitigation strategies.

“LCAPs also provide opportunities for organizations to stay up to date with the most recent practices – for example, platform engineering and technology innovation like AI-assisted application development and, specifically, the recent trend of generative AI.”

Gartner Research 2023

How can you implement AI into your business?

There are many sub-types of Generative AI that have proven to be useful in various use cases. Here are some examples of sub-types and their uses.

Machine
Learning

Allows AI to be trained based on data which allows it to learn to identify patterns or groupings, make predictions & classifications, or achieve other goals by interacting with its environment. This can be useful in almost any sector.

  • Supervised Learning: Training a model on labeled data. This can be used for predictions and classifications.
  • Unsupervised Learning: Uses unlabeled data to identify patterns or groupings.
  • Reinforcement Learning: Learns by interacting with its environment to achieve a goal.

Natural Language
Processing

Builds on AI’s capabilities to analyze text data for insights, automate conversational agents, and translate text between languages. This can, for example, be very useful in the customer service sector.

  • Text Analytics: Analyzing text data for insights. Used in sentiment analysis, customer feedback analysis, etc.
  • Chatbots: Automated conversational agents. Improve customer service and reduce response time.
  • Machine Translation: Translating text between languages. Useful for international business communications.

Computer
Vision

Trains AI to identify objects, features, or people both in images and videos as well as facial recognition. This can, for example, be very useful in the security, inventory management, and content moderation sectors.

  • Image Recognition: Identifying objects, features, or people in images. Useful in quality inspection, inventory management, and security.
  • Facial Recognition: Identifying or verifying individuals. Enhances security and personalized customer experiences.
  • Video Analysis: Analyzing video content for specific events. Used in surveillance, sports analysis, and content moderation.

AI Driven
Analytics

Based on the analyzed data, AI can forecast future trends and recommend actions based on these trends. This can, for example, be very useful in the supply chain operations, logistics, and sales sectors.

  • Predictive Analytics: Forecasting future trends based on historical data. Applied in sales forecasting, risk management, and supply chain optimization.
  • Prescriptive Analytics: Recommending actions based on predictions. Enhances decision-making in logistics, marketing strategies, and operations.

Stay ahead of your competition!

Looking for more AI use cases or advice on how to implement AI into your business?

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