AI can read and learn from proprietary formats, but only after those formats are decoded or converted into something the model can process. At AEHEA, we often work with clients who store valuable data in custom file formats, specialized databases, or software-specific exports. While AI models themselves do not natively understand these proprietary structures, we can build pipelines that translate the content into standard, structured formats suitable for training or inference.

The first step is gaining access to the format’s structure. If documentation exists, we use it to extract the data programmatically. For example, formats used by tools like SAP, AutoCAD, or custom ERP systems often require a software development kit (SDK) or API to access the underlying content. Once retrieved, we extract the meaningful data whether it’s text, tables, images, logs, or metadata and convert it into formats like JSON, CSV, XML, or plain text. These are universally understood by machine learning tools and AI frameworks.

In cases where documentation is unavailable, reverse engineering becomes necessary. We analyze the binary structure, use tools like Hex editors or file sniffers, and write custom parsers to extract content. This approach is more labor intensive but effective when the data is too valuable to ignore. Once extracted, we apply standard preprocessing steps: cleaning, normalization, and, if needed, tokenization. This prepares the data for AI models that require consistent input formats to function accurately.

At AEHEA, we automate this process when possible building converters that regularly scan proprietary files, extract relevant data, and feed it into AI workflows. Whether the goal is training a model, generating reports, or running predictions, the challenge is always the same: unlock the data, structure it correctly, and make it useful. While AI cannot directly interpret proprietary formats, with the right engineering and preprocessing, it can absolutely learn from the insights those formats contain.