WildEdge.dev

Wild Edge is a robust platform designed to continuously improve model performance by transforming real-world failures into actionable training data. Key capabilities include: * Real-time inference monitoring * Performance degradation detection * Automated dataset creation from production events * Deep analytics for hardware and agentic traces * Data export with flexible querying This system instruments your models with a zero-dependency SDK, providing detailed insights into drift, latency, and confidence shifts across all models. It offers comprehensive analytics, breaking down performance by device, OS, accelerator, and even thermal state. Furthermore, for agentic pipelines, it meticulously traces every step, capturing timing, token usage, and tool call sequences. Wild Edge facilitates the creation of high-quality datasets by allowing you to filter production events based on confidence scores, device types, or outcomes. This enables targeted data collection from challenging cases and user feedback, ensuring that your models learn from their actual weaknesses. The platform makes all inference history accessible through natural language or SQL queries, and data can be exported to popular open table formats or data warehouses like Snowflake and BigQuery. Ideal for engineering and data science teams, Wild Edge ensures continuous model enhancement by closing the feedback loop from production to training. It supports custom, open-source, and remote models across Python, Android, and iOS environments, integrating seamlessly with your existing stack without requiring changes to inference code.