AI is a type of software, and to benefit from its capabilities, a company must purchase an AI solution that solves the business case at hand. At the moment, change and hype are very turbulent, and locking into the wrong vendor might be problematic and costly.
One way to avoid vendor lock-in is to create your own solution or to manage your organization’s data usage with the AI provider’s solution correctly. However, size matters with generative AI solutions at the moment. ‘Size’ refers to a large model, and creating a large model requires a vast amount of quality data, along with the correct computation and training resources. So far, only a few organizations in the world, such as OpenAI + Microsoft and Google, have been able to create models capable of performing useful tasks by understanding human language. This success is mainly due to their access to quality data and financial resources. However, as technology evolves, many open-source models are already outperforming the previously mentioned models in certain tasks or with specific languages (e.g., Finnish). These open-source models can be further trained and used as your own solution, for example, as a private AI. Also, if AI usage extends beyond interactions with generative AI, such as in machine learning, a self-managed solution might be mandatory.
We provide the AI solution needed for your business case. The process unfolds as follows: When a business case requiring an AI solution and its implementation is identified, a proof of concept is created and validated through rapid iteration with end-users/environment. This ensures that no resources are wasted on the wrong solution. The acknowledged solution’s implementation is then aligned with the vision of the solution’s expected lifecycle, and necessary modifications are made. Subsequently, the solution is deployed to production. The main goal of this process is to provide a useful tool and keep the project duration short.
Neptunux 28.2.2024
