Gantry
Get the full picture of your model's performance. Log inputs and outputs and seamlessly enrich them with metadata and user feedback.
Figure out how your model is really working, and where you can improve. Monitor for errors and discover underperforming cohorts and use cases.
The best models are built on user data. Programmatically gather unusual or underperforming examples to retrain your model.
Stop manually reviewing thousands of outputs when changing your prompt or model. Evaluate your LLM-powered apps programmatically.
Detect and fix degradations quickly. Monitor new deployments in real-time and seamlessly edit the version of your app your users interact with.
Connect your self-hosted or third-party model and your existing data sources.
Process enterprise-scale data with our serverless streaming dataflow engine.
Gantry is SOC-2 compliant and built with enterprise-grade authentication.
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Claude Opus 4.7
Claude Opus 4.7 is the latest Anthropic AI model release designed to significantly improve performance in advanced software engineering and complex problem-solving tasks. It builds upon the previous Opus 4.6 model by delivering stronger results on difficult coding challenges and long-running workflows. The model is known for its ability to follow instructions precisely and verify its own outputs for greater reliability. It also introduces enhanced multimodal capabilities, particularly in processing high-resolution images with improved accuracy. Opus 4.7 supports more detailed visual tasks such as analyzing dense screenshots and extracting data from complex diagrams. In professional settings, it produces higher-quality outputs including documents, presentations, and user interfaces. The model includes updated safety features that detect and block high-risk cybersecurity-related requests.
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Sup AI
Sup AI is a multi-LLM platform that merges outputs from several top large language models, such as GPT, Claude, Llama, and more, to generate richer, more accurate, and better-validated answers than any single model could provide. It applies real-time “logprob confidence scoring,” analyzing each token’s probability to detect uncertainty or hallucination; when a model’s confidence falls below a threshold, the response is halted, helping ensure that delivered answers remain high-quality and trustworthy. Sup’s “multi-model fusion” then compares, contrasts, and consolidates outputs from different models, cross-verifying and synthesizing the best parts into a final result. Sup also supports “multimodal RAG” (retrieval-augmented generation) to incorporate external data (text, PDFs, images) into context-aware responses, giving the AI access to factual sources and helping it “never forget” relevant information.
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Traceloop
Traceloop is a comprehensive observability platform designed to monitor, debug, and test the quality of outputs from Large Language Models (LLMs). It offers real-time alerts for unexpected output quality changes, execution tracing for every request, and the ability to gradually roll out changes to models and prompts. Developers can debug and re-run issues from production directly in their Integrated Development Environment (IDE). Traceloop integrates seamlessly with the OpenLLMetry SDK, supporting multiple programming languages including Python, JavaScript/TypeScript, Go, and Ruby. The platform provides a range of semantic, syntactic, safety, and structural metrics to assess LLM outputs, such as QA relevancy, faithfulness, text quality, grammar correctness, redundancy detection, focus assessment, text length, word count, PII detection, secret detection, toxicity detection, regex validation, SQL validation, JSON schema validation, and code validation.
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