How we work with AI

At Point, we have worked on several innovation and insight projects involving AI and machine learning. These projects have varied widely, from developing AI-driven chatbot assistants to using machine learning for market segmentation and uncovering user cause and effect relationships.

The backbone of many of our tools is something called Large Language Models (LLMs). These advanced systems have only recently become more accessible, and they’re transforming the way we think about and utilize technology. Among the most notable LLMs are OpenAI’s ChatGPT, Google’s Gemini, and Mistral. LLMs excel at processing diverse information and generating clear, high-quality content. Think of an LLM as a system that “understands” and replicates human-like conversation. When set up properly, it allows for interactions that feel as natural as speaking with another person.

For our clients, we’ve crafted AI chatbot prototypes powered by these LLMs, streamlining administrative tasks and enhancing user experience. The potential applications for LLMs are vast and varied, suggesting a future where many processes could be made more efficient and user-friendly through their use.

Beyond LLMs, we also use machine learning and artificial neural networks for tasks requiring deep analysis, such as segmenting markets or modeling complex scenarios. While there are off-the-shelf tools available for some of these tasks, our approach often involves custom programming to meet the unique requirements of each project.

Point has used machine learning and artificial neural networks to create segmentations and analyze factors behind outcomes for clients like H&M Move, Västtrafik and the Swedish Transport Authority.

If you would like to know more about how we work with AI at Point, please contact Rasmus Lehnér or Michael Steele.