DevOps Pipelines
From log ingestion to incident response, each pipeline deploys in minutes.
Ingest high-volume application logs, filter noise with severity-based rules, tokenize and aggregate error patterns over tumbling windows, then use AI to classify anomalies and generate root cause hypotheses. Reduces alert noise by 97%+ while surfacing only actionable incidents.
The filter → aggregate → threshold pattern reduces millions of raw log lines to only the anomalous clusters. AI analysis runs only on the aggregated output, cutting LLM costs by 97%+ while maintaining full observability.
Poll API endpoints continuously, measure response times, aggregate latency percentiles over sliding windows, detect degradation patterns with AI, and route alerts based on severity. Provides a real-time API reliability dashboard with historical trend analysis.
Ingest security logs from multiple sources, normalise event formats, correlate related events using windowed joins, classify attack patterns against the MITRE ATT&CK framework using AI, and route confirmed threats to SIEM sinks with enriched context.
An autonomous AI agent that takes an incident description, uses HTTP tool calls to query monitoring APIs, log aggregators, and documentation wikis, then synthesises a comprehensive root cause analysis report with suggested remediation steps.
The LLM Agent autonomously decides which APIs to call (Prometheus, Elasticsearch, Confluence), iterates through Thought → Action → Observation cycles, and produces structured incident reports without human intervention.
See AI-powered observability running live with your logs. Book a 30-minute demo with our team.