AI engineering

How our AI Engineering supports your digital products

At Yukawa, we harness the power of artificial intelligence to drive innovation and create smarter, more efficient solutions for our partners. Our AI-driven technologies enable businesses to transform their workflows, optimize operations, and unlock new growth opportunities.

LEARN MORE ABOUT OUR AI COMPETENCES

We integrate AI into the digital products we are building with our partners. Amongst others, such AI driven functionalities automate service processes for the users of those products. It personalizes the user experience and with its machine learning-powered insights, our AI-driven features empower our partners to grow their revenues.

LEARN MORE ABOUT OUR AI USE CASES

AI isn't just something we build for our partners—we also use it internally to improve our own development processes. From AI-assisted coding to intelligent quality assurance and automated testing, we leverage AI tools to enhance efficiency, accelerate innovation, and ensure the highest quality in our software solutions.

Our AI Use Cases

Conversational Bot as User Support for Connected Devices

An AI-powered chatbot streamlined first-level support for a market leading consumer electronics company, assisting users with onboarding, troubleshooting, and device configurations. This reduced support workload, improved response times, and enhanced customer experience, driving higher engagement and efficiency.

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Decision Making in Raw Material Trade

An AI-driven data science solution helped a chemical trading company aggregate, standardize, and analyze complex global trade data, uncovering market trends and sales opportunities. This improved decision-making, enhanced competitiveness, and increased operational efficiency

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Our AI Competencies

Machine Learning

Machine Learning involves training algorithms to learn patterns from data, allowing them to make predictions or decisions without being explicitly programmed.

Deep Learning

Deep Learning is a subset of Machine Learning that focuses on neural networks with multiple layers, allowing them to learn complex features and hierarchies in data.

Natural Language Processing (NLP)

NLP deals with the interaction between computers and human language. It enables machines to understand, generate, and respond to human language in a meaningful way.

Computer Vision

Computer Vision focuses on enabling machines to interpret and understand visual data from the world, such as images and videos.

Recommendation Systems

This field focuses on creating algorithms that suggest relevant items to users, such as products, content, etc. These systems use collaborative filtering, content-based filtering, and hybrid approaches.

Explainable AI (XAI)

Creating models and systems that provide clear insights into their decision-making processes. This subfield ensures transparency, accountability, and trustworthiness in AI applications.

Reinforcement Learning

Reinforcement Learning involves training agents to make sequences of decisions by maximizing cumulative rewards in dynamic environments. This is instrumental in areas such as game-playing AI, resource optimization, and robotics.