Converting unstructured data into AI-ready tokens is a crucial step in preparing inputs for models that work with text, images, audio, or other raw formats. Tokenization is the process of breaking down this raw data into smaller, structured units or “tokens” that an AI model can understand. At AEHEA, we use a range of tools to handle different types of unstructured input and transform them into usable, machine-readable formats.

For text data, some of the most reliable tokenization tools come built into machine learning libraries. SpaCy, NLTK, and Hugging Face’s Transformers all offer robust text tokenizers. These tools break sentences into words, subwords, or characters, depending on the model’s architecture. Hugging Face tokenizers, for instance, are tailored to specific language models like BERT or GPT, handling lowercasing, punctuation stripping, and subword encoding exactly as the model expects. This ensures compatibility and optimal performance during both training and inference.

For PDFs, scanned documents, and web content, the first step is usually text extraction. Tools like Tika, PyMuPDF, and Textract extract content from files, while OCR platforms like Tesseract or Google Vision API handle image-based text. Once the raw content is extracted, you can pass it through one of the tokenization libraries mentioned above. If the end goal is question answering or summarization, we often preprocess and chunk the data into sections before tokenization, ensuring context is preserved across longer documents.

For images and audio, the concept of tokenization is more about feature extraction. With images, tools like OpenCV or TensorFlow’s preprocessing utilities break images into pixel grids or features for input into vision models. For audio, libraries like Librosa or OpenAI’s Whisper tokenizer convert waveforms into frequency features or spectrogram segments. These are numerical representations that serve the same function as tokens do in language structured inputs that a model can learn from.

At AEHEA, we automate much of this process using workflow tools like n8n and Python pipelines. We set up systems that continuously pull in unstructured data, convert it into structured formats, tokenize it according to model requirements, and feed it into downstream AI tasks. The right tools not only make this conversion accurate, but scalable which is essential for deploying AI in real-world business environments.