## 1 Introduction
Contemporaneous: This addresses the issue of recording data promptly and accurately for both individuals and systems. When dealing with electronic data, it is customary to timestamp the activities or actions to ensure a chronological order. 4. Original: This refers to keeping records in their original form rather than using duplicates or transcriptions, especially when it comes to manual record-keeping. The initial recording of the data, whether it is on paper or in a digital system, should serve as the primary record. In the case of digitally recorded data, it is crucial to have technical and procedural measures set to prevent alterations to the original recording. 5. Accurate: This ensures that all records accurately depict the actual events without any mistakes. Moreover, it is crucial to refrain from altering the original information in a manner that causes its loss (e.g., compression, rounding of numerical values, acronyms, etc.). 6. Complete: It is necessary for all captured information to possess a comprehensive audit trail to demonstrate the absence of deletions or losses. This requirement encompasses not only the primary data recording but also includes metadata, retest data, analysis data, and other related elements. Additionally, audit trails must be in place to track any modifications made to the data. 7. Consistent: This refers to the need for data timestamping, that must be sequential in an ascending order. Timestamps should also be added to indicate modifications made to the original data recording. 8. Enduring: This refers to the long-term storage of data that must be able to be read and understood for many years after their creation. Available: This refers to the ability of data to be accessible to every interested party and at any time during its life cycle. Assessment of whether pharmaceutical process data comply with ALCOA+ principles is currently a tedious and sometimes cumbersome task, involving largely manual investigations of lengthy reports and audit trails tracing through general-purpose software. Although some guidelines for automating ALCOA assessment have been introduced [5], they still focus on identifying compliance violations only after they have taken place, thus not preventing production delays and increases in production cost due to faulty batches being produced. Furthermore, raw data in the pharmaceutical industry are often heterogeneous and come in vast amounts and various types, as generated from pharmaceutical plant data sources, thus making data integrity and data management challenging tasks. To make things worse, various state-of-the-art technologies in contemporary pharmaceutical industries, such as cyber-physical systems, and process analytical technologies (PAT), have posed stringent demands for mass and smart manufacturing [8]. Given the sheer amounts of available data and the extreme variety in data types and content, artificial intelligence techniques and, in particular, deep learning models, provide efficient and adaptable solutions. Especially, when compared against machine learning or statistical methods, deep learning techniques can substantially alleviate the burden to manually process, transform and annotate large non-homogeneous datasets and also to provide data integrity and ALCOA+ compliance [2]. One step beyond such heterogeneous data-processed information, along with selected historical raw data and alarm signals, could be an adequate input for deep-learning-based prediction models and advanced data analytics. Such methods can derive safe prognosis results regarding ALCOA+ compliance and maintenance, as well as production line health conditions, and production line maintenance. It is therefore imperative for any pharmaceutical manufacturing system to be able to support ALCOA+ compliance prediction while also maintaining ALCOA+ standards and preserving a steady qualitative performance. In this paper, we present ALCOAi, a transformer-based deep learning network for calculating and forecasting each of the ALCOA+ principles mentioned above.