Arpeggio AI
Enterprise-grade observability and real-time guardrails for LLM-powered applications.
The iterative operations platform for closing the loop between AI monitoring and model improvement.
Gantry is a leading AI observability and evaluation platform designed to bridge the gap between production monitoring and model retraining. Unlike traditional monitoring tools that simply alert on performance drops, Gantry emphasizes iterative improvement by providing the infrastructure to identify problematic data clusters and funnel them into fine-tuning pipelines. As of 2026, the technical architecture focuses heavily on Large Language Model (LLM) systems, offering sophisticated tools for RAG (Retrieval-Augmented Generation) triaging and hallucination detection. Gantry’s core value proposition lies in its 'Feedback Loop' capability, which allows developers to ingest human feedback directly into the observability suite to create high-quality evaluation sets. Its position in the 2026 market is defined by its deep integration into the enterprise AI stack, following its acquisition by Mainstay (formerly Adaptive), pivoting towards a comprehensive governance and performance management suite for Fortune 500 AI deployments. The platform provides a unified view of model health, data drift, and semantic performance, making it an essential tool for AI Solutions Architects who require more than just vanity metrics.
Uses embedding-based analysis to detect shifts in the semantic meaning of inputs/outputs rather than simple statistical distribution changes.
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 Intelligent AI Observability Platform for Enterprise Scale MLOps.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
A specialized workflow for identifying whether failures occurred in the retrieval step or the generation step of an LLM pipeline.
Integrates directly with labeling workforces or internal UI elements to capture ground truth labels in real-time.
Programmatically run models against historical 'golden sets' to benchmark new versions before deployment.
Real-time scanning of outputs using pre-trained NLP classifiers to flag harmful or biased content.
Automatic correlation of performance drops with specific data slices or model versions.
Maintains a full history of how data evolved from raw production logs to curated training subsets.
A bank's LLM is providing incorrect interest rate information.
Registry Updated:2/7/2026
Export the failing samples to the prompt engineering team for refinement.
Model accuracy drops as economic conditions change user income distributions.
An e-commerce site notices a drop in click-through rate (CTR) on suggested items.