Modern IT and AI systems are increasingly complex, making it difficult to proactively identify and resolve issues like model drift, data quality problems, and infrastructure failures before they impact users. Existing monitoring tools often lack the intelligence to correlate data across diverse sources and provide actionable insights for remediation.

InsightFinder provides an AI observability and IT observability platform that uses unsupervised machine learning to automatically detect, diagnose, and predict incidents across enterprise-scale AI models, IT infrastructure, and applications. The platform's Unified Intelligence Engine (UIE) ingests log, metric, trace, and dependency graph data from various sources, applying patented anomaly detection algorithms to identify root causes and predict future incidents. By providing real-time insights and automated remediation recommendations, InsightFinder enables ITOps, DevOps, SRE, and AI/ML teams to reduce MTTD/MTTR, ensure system reliability, and optimize AI model performance.

Real-time anomaly detection using patented unsupervised machine learning algorithms

Automatic root cause analysis and incident prediction

Support for log, metric, trace, and dependency graph data

Threshold-less alerting to reduce false positives

Unified health view across all services, applications, and infrastructure

Automated remediation playbook creation

Integration with leading observability platforms like Datadog, Prometheus, and Splunk

LLM-driven Operation Co-pilot generates root cause summaries and action recommendations in natural language

IFTracer SDK for collecting streaming prompt data (traces and spans)

The primary target audience includes ITOps, DevOps, SRE teams, data scientists, ML engineers, AI platform engineers, and Chief AI Officers responsible for maintaining the reliability and performance of complex IT and AI systems.