In various aviation companies, the reliance on manual, labor-intensive processes for data preparation, cleansing, and
preprocessing has become a significant bottleneck, leading to inefficiencies, errors, and delays. The need for a more
advanced and automated approach is evident, as the current manual methods not only consume valuable time and resources but
also pose a risk of inaccuracies, hindering organizations from realizing the full potential of their data for informed
decision-making.
With superior levels of data preprocessing, data preparation, and data refinement, PrepAI is crafted with AI and NLP
principles so that it frees up valuable time for data analysts to craft strategies with ready-to-use data and do more
meaningful work besides mundane tasks such as data cleansing.
Our AI-powered data processing solution, the PrepAI tool, tackles challenges in airline departments. The tech stack
(Alteryx, React, Python, Django REST, SQLite) used to design this solution employs a two-stage process. First, AI and NLP
automate much of the data labeling, ensuring consistency. This is made possible by supercharging natural language
refinement with the use of reinforcement learning in ML. Experts then validate for accuracy. Second, a trained AI model
integrates into existing systems, using its learnings to predict and format inspection data. Continuous monitoring
triggers retraining for ongoing optimization. This frees up resources by enabling autonomous data formatting and storage
in the required structure, empowering versatile and efficient fleet-wide data analysis.