Tero is a read‑only log analysis platform that plugs directly into existing log storage without requiring migration, pipeline changes, or new infrastructure. Using AI, it continuously scans incoming logs to surface emerging issues in real time, attaching context such as evidence, owner, severity, and suggested remediation. The platform then routes each detected problem to the appropriate workflow—whether a telemetry policy, code change, infrastructure fix, or team review—enabling faster, more targeted incident response.
Funding
Funding not disclosed
Founders
Product
Problem
Production teams generate massive, noisy log streams that are stored as read‑only files. Because logs are large and contain a lot of irrelevant data, continuously monitoring them for emerging issues is costly and often delayed, leading to problems being discovered only after they become expensive incidents.
Solution
Tero offers an AI‑driven platform that connects read‑only to existing log stores without requiring migration, pipeline changes, or additional infrastructure. The system continuously reads each new log entry, applies machine‑learning analysis to identify subtle signals such as duplicate trace contexts or misconfigurations, and surfaces these findings while they are still small. For every detected issue, Tero generates a structured ticket that includes the relevant log excerpts, service context, owner, severity, and a recommended remediation action. These issue tickets can be routed automatically to the appropriate workflow—whether a telemetry policy, code change, infrastructure fix, or team review—integrating with existing observability tools, version‑control systems, and ticketing platforms. By turning raw logs into actionable issues, Tero enables a proactive, evidence‑based production workflow that reduces incident cost and accelerates resolution.
Target Audience
Primary customers are production engineering and reliability teams at mid‑size to large enterprises that manage high‑volume, log‑centric services and need automated, continuous detection of emerging operational issues.
Features
- Read‑only, no‑migration connection to any existing log storage backend
- Continuous AI analysis of each log entry shortly after creation, detecting patterns such as duplicate trace contexts, noisy warnings, and misconfigurations
- Automatic issue generation with attached evidence, service metadata, owner attribution, severity rating, and suggested fix
- Configurable routing of issues to downstream tools (telemetry policies, CI/CD pipelines, ticketing systems, or alerting platforms) via open standards and APIs
- Compatibility with existing observability stacks, OpenTelemetry data, and common engineering workflows
- Scalable processing capable of handling tens of millions of log records per day without additional infrastructure