InsightFinder AI Observability Platform
AI Observability for teams building and running LLMs and ML models. Detect drift, bias, hallucinations, and prompt attacks. Ensure data quality and performance at every stage.
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 delivers complete LLM observability and ML observability, and helps ensure high performance for companies deploying AI and LLM models at enterprise scale.
AI Observability Platform Features
Model and Data Drift
Detect and prevent model drift through dynamic baseline learning, unsupervised neural network-based anomaly detection and root cause analysis. Identify both data drift and concept drift and take preventative actions to avoid model drift.
LLM Trust and Safety
Identify and remediate LLM hallucinations and protect against malicious prompt injections. Use unsupervised machine learning with custom and pre-configured LLM evaluations to ensure accuracy of model results.
LLM Usage, Cost, & Performance
Complete LLM observability for model usage & consumption, model health & performance.
Model Data Quality
Complete data pipeline visibility. Continuously monitor and remediate data quality issues, including latency, transformational errors and incomplete records.
Key Capabilities of AI Observability Platform
Flexible Deployment options
SaaS
On Premise
Co-Pilot
Query and drill into data
Perform model troubleshooting and root cause analysis
Fast Onboarding
Model Management - model setup, definition + its associated model data
Integrations - onboard Model Data from Open Telemetry, Elastic, Prometheus, Google BigQuery
Add workbench for each use case in minutes
Model Monitoring
Out of the box monitors for data & model drift, LLM Trust & Safety, LLM performance, model data quality, and more.
Automatic detection of model drift, model performance and model accuracy anomalies
Complete LLM observability and ML observability
IFTracer SDK for collecting streaming prompt data (traces and spans)
Notifications via email for health/performance for each monitor.
Workbench
Analyze anomalies and perform deep dive analysis
Trace Viewer - view LLM traces with anomalies
Prompt Viewer - view all LLM prompts anomalies
Charts with flexible filtering
Compare models, anomalies, cost
Timeline view to analyze when anomalies occur, deliver root cause analysis, and more
Instant workbench creation for each use case
Dashboards
Tailored dashboards for LLM and ML models
Data quality, model drift, total model performance (ML)
Token consumption, malicious prompt identification (LLM), cost
Analyze model drift using PSI or distance metrics
LLM Insights Dashboard for model usage & consumption, model health & performance
Easy Integrations
InsightFinder AI’s anomaly detection, root cause analysis, and incident predictions integrate easily into the leading Observability platforms – bringing high-power AI-based analysis to your existing Observability and Monitoring environment.
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