Sazabi
Sazabi is an AI-native observability and security platform that automatically ingests production logs, detects anomalies without manual monitor configuration, and lets engineers investigate issues through a conversational chat interface. The platform integrates with AI coding agents like Claude Code, Codex, and Cursor to enable automated incident remediation through pull requests. It also provides security monitoring including code scanning, CVE detection, and real-time botnet and fraud alerts.
- Developer Tools
- Artificial Intelligence
- AI Agents
- Software Only
- Cybersecurity
Funding
Funding not disclosed
Founders
Founder details are not available yet.
Product
Problem
Traditional observability tools require teams to manually configure monitors, navigate complex dashboards, and dig through mountains of telemetry to diagnose issues. This legacy approach creates alert fatigue, misses critical signals, and forces engineers to spend valuable time firefighting instead of building software.
Solution
Sazabi provides an AI-native observability platform that works for engineers rather than requiring them to work for it. The platform automatically ingests logs from any source, learns what is normal for each system, and generates rich, actionable alerts without any setup or configuration. Users can interrogate their applications in plain language through a conversational chat interface, receiving instant answers about root causes, impact, and recommended fixes. Sazabi integrates tightly with major AI coding agents like Claude Code, Codex, and Cursor, allowing it to not only diagnose issues but also launch remediation agents that open pull requests with fixes directly. The platform maintains a continuously learning memory of past incidents, enabling it to surface historical context and accelerate future troubleshooting.
Target Audience
Primary customers are fast-moving engineering teams and product engineers at startups and scaling companies who need to ship quickly without dedicated observability specialists. The platform is designed for teams that use modern AI coding tools and want a conversational, low-friction approach to production monitoring and incident response.
Features
- Autonomous alerts that self-configure, learn system baselines, and escalate only when issues genuinely matter, eliminating false positives and manual monitor tuning
- Conversational debugging interface that lets engineers ask questions in natural language and receive precise answers with code search capabilities pinpointing exact files, commits, or lines responsible
- Native integrations with AI coding agents including Claude Code, Codex, and Cursor, enabling automatic incident remediation via cloud agents that open pull requests
- Cross-source correlation engine that unifies commits, deploys, errors, support tickets, and Slack conversations into a single event timeline
- Error clustering that groups thousands of related failures into a single root-cause alert
- Dynamic visualizations that generate charts, tables, diagrams, and code blocks on demand rather than static dashboards
- Built on a "logs are all you need" architecture that reconstructs metrics and traces from log events, supported by automatic instrumentation capabilities
- Security monitoring features including code scanning, CVE detection, real-time botnet detection, fraud detection, and intrusion detection