Building an AI application is a complex multi-faceted process comprising different stages.
Our internal development experience at Synch, the tech law firm behind Legal AI Blog, has proved that it is possible to build a useful AI product in-house as long as the following criteria are met:
|A||the application’s focus is clearly defined|
|B||necessary data are collected and cleaned by relevant experts, i.e. those who understand the data|
|C||the collected and cleaned data are well-organized and labelled|
|D||the datasets are checked for consistency before training using an efficient sanity-check algorithm, e.g. for avoiding data duplicates etc.|
|E||multiple AI models have been tested, and the best-performing AI model is selected for the task|
|F||if a set of tasks is performed on the same data, multiple AI models have been tested for each task (the same model may perform differently depending on the task)|
|G||the selected best-performing model has been tweaked using an efficient fine-tuning algorithm, with the goal to reach the highest accuracy within a reasonable timeframe|
|H||internal testing by different user groups has resulted in a well-documented and non-biased feedback|
|I||training and deployment routines are well-documented|
|J||tools for continuous training (i.e. improvement) of the AI model have been developed, and the routines have been thought through|
We at Synch will be announcing our first client-oriented AI solution soon. Stay tuned for updates!