Arpeggio AI
Enterprise-grade observability and real-time guardrails for LLM-powered applications.
Mona is a sophisticated AI monitoring and observability engine designed to provide deep granular visibility into how AI models perform in real-world environments. Unlike basic monitoring tools that aggregate metrics, Mona's architecture centers on context-aware segmentation, allowing data scientists to detect performance degradation in specific sub-populations of data that would otherwise be hidden in global averages. By 2026, Mona has positioned itself as the enterprise standard for cross-functional AI governance, bridging the gap between technical ML metrics and business-level KPIs. The platform utilizes a highly scalable, non-intrusive data collection layer that integrates via Python SDK or REST API, supporting both batch and streaming architectures. Its proprietary 'Insights Engine' automatically surfaces the root causes of model drift and data integrity issues, significantly reducing Mean Time To Resolution (MTTR) for production issues. In the 2026 market, Mona is particularly valued for its proactive bias detection and fairness monitoring modules, which help organizations comply with evolving global AI regulations while maintaining high-performance automated decision-making systems.
Enables the definition of specific data slices based on complex logical filters to monitor performance on sub-populations.
Enterprise-grade observability and real-time guardrails for LLM-powered applications.
The open-source AI observability platform for LLM evaluation, tracing, and data exploration.
The lightweight toolkit for tracking, evaluating, and iterating on LLM applications in production.
The Enterprise-grade Evaluation and Observability Infrastructure for High-Fidelity LLM Applications.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Uses automated correlation analysis to identify which input features are driving changes in model output.
Tracks disparate impact and equal opportunity metrics in real-time against protected classes.
Supports dynamic schema evolution without requiring system restarts or manual database migrations.
Allows users to write custom Python logic to define business-specific metrics derived from model outputs.
Optimized asynchronous ingestion layer that minimizes overhead on the production inference service.
Synchronizes monitoring configurations between staging, canary, and production environments.
A bank's credit model begins rejecting a specific demographic unfairly due to training data bias.
Registry Updated:2/7/2026
Seasonal shopping changes make a recommendation model obsolete, dropping conversion rates.
An imaging model performs differently across various hospital locations due to hardware discrepancies.