AI that doesn’t just respond—it resolves
Built on 10 years of UC Berkeley research, RunLLM reads logs, code and docs to resolve complex support issues. Saves 30%+ eng time, cuts MTTR by 50%, deflects up to 99% of tickets. Trusted by Databricks, Sourcegraph and Corelight—try for free on your product.
Herald builds a context graph of observability, codebase, CI/CD, and dependencies to establish baseline behavior, then applies custom anomaly detection models to each data stream. This enables proactive detection of brewing issues and validated signals before alerts fire or customers complain.
Herald automatically correlates and prioritizes incoming alerts, cutting through noise to surface real incidents. The system eliminates the need to manually instrument and tune thresholds for each data stream, reducing false positives and alert fatigue.
Unlike reactive AI SREs that require pre-existing runbooks, Herald achieves 70%+ accuracy on novel incidents by learning system behavior and reasoning through new failure patterns. The agent never repeats mistakes and continuously improves from every investigation.
Herald provides developer Q&A capabilities that answer questions about code, infrastructure, past incidents, and design docs by leveraging collective team knowledge. Teams and Enterprise plans include collaboration via Slack/Teams and team-wide agent training to distribute expertise.
Herald investigates root causes autonomously without requiring pre-written runbooks, delivering RCAs in minutes. The AI agent evaluates multiple hypotheses simultaneously against relevant data sources, enabling on-call engineers to jump directly to resolution instead of spending hours diagnosing issues.