We specialise in machine learning and generative AI technologies, including Automated Speech Recognition (ASR), Small Language Models (SLM), Retrieval Augmented Generation (RAG), and vector databases.

• Selective Builds: Including only necessary operations required for a specific model, producing a compact library tailored for the mobile device. This reduces the overall size of the model, facilitating efficient on-device execution.
• Quantization: Compressing large models by mapping high precision values to lower precision ones, significantly reducing memory usage without substantially affecting model accuracy. Methods like dynamic range quantization and float16 quantization optimize the model for speed and storage, making it suitable for resource-constrained environments.
• Model Adapters: Adding adapter modules to pre-trained models to adapt them to new tasks without retraining the entire model. This conserves computational resources and maintains the compactness necessary for deployment on edge devices. Adapters allow customization for specific applications while keeping the model lightweight and efficient.