InsightFinder AI Observability Platform

AI-driven Model Observability – Manage your AI models to detect
and prevent drift, ensure accuracy, and deliver performance.

Data scientists, data engineers, ML engineers, AI platform engineers, and Chief AI Officers need to deliver and run new LLM and ML models operating in production environments.

InsightFinder’s proven approach to AI Observability ensures AI model integrity – protecting against model drift, identifying hallucinations, and monitoring both model data and supporting infrastructure to ensure performance and reliability.

By applying unsupervised machine learning with proven, patented algorithms for anomaly detection, root cause analysis, and incident prediction, AI Observability helps ensure high performance for companies deploying AI and LLM models at enterprise scale.

AI Observability Platform Features

Model Drift

Detect and prevent drift through dynamic baseline learning, unsupervised neural network based anomaly detection and root cause analysis. Monitor model behavior and take preventative actions to avoid model drift.

LLM Accuracy

Identify and remediate LLM model “hallucinations” that create false information and potential costs and liabilities. Use unsupervised machine learning with custom and pre-configured evaluations to ensure accuracy of model results.

Model Performance

Full visibility into all the elements of performance – the models themselves, the infrastructure (GPUs and underlying infrastructure),  and the end-to-end traces of each individual request to ensure high AI service availability and performance.

Workbench

Model Workbench for deep dive anomaly investigation and root cause analysis. Compare models, view LLM traces and LLM prompts with anomalies.

Key Capabilities of AI Observability Tool

Flexible Deployment options

Co-Pilot

Fast Onboarding

Model Monitoring

Workbench

Dashboards

Join our Beta Program

We are currently taking on new members for our AI Observability Beta product program. Contact Us to learn more!

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