Review the transparency, privacy, and safety principles guiding our AI infrastructure services.
TeleFork uses machine learning to speed up sandboxes (Predictive Scaling), suggest fixes (Log Clustering), and mock redundant resources (Smart De-duplication). We manage these models responsibly.
TeleFork's AI models are self-hosted. We **do not** send your code snippet files, log outputs, or repository directories to third-party model providers. Your assets are never used to train public models.
Whenever TeleFork's AI suggests mocking a subservice to save cloud costs, it discloses the suggestion in the workspace dashboard. Developers can override any AI-driven mockup decision with a simple configuration.
Our log clustering engine groups logs to identify errors. It acts as an advisory tool. The AI does not make modifications to your source code or database structures.
We test our classifiers continuously across different coding frameworks and language versions. We refine our models regularly to ensure error clustering remains fair and accurate.