Calmo is an AI-powered debugging copilot that helps developers diagnose and fix production issues up to 10x faster. Instead of manually combing through logs, traces, and metrics, Calmo connects to your existing observability stack and automatically surfaces the root cause behind incidents, performance regressions, and edge-case failures. By combining application context, user impact, and infrastructure signals, it explains what went wrong in clear, developer-friendly language and suggests the next steps to remediate. Designed for modern engineering teams, Calmo fits naturally into your existing workflow. It can be triggered directly from error pages, dashboards, or alert notifications, and it keeps a full timeline of each incident so teammates can collaborate and learn from previous outages. Calmo reduces the time spent on repetitive investigation work, freeing engineers to focus on building features instead of chasing issues in production. Calmo supports a wide range of languages, frameworks, and cloud environments, making it ideal for SaaS products, APIs, microservices, and complex distributed systems. Whether you are on call, leading an SRE team, or managing a fast‑moving dev squad, Calmo gives you an AI partner that understands your system behavior and helps you restore reliability quickly and confidently.
On-call engineers quickly investigate production incidents by letting Calmo correlate logs, traces, and metrics to highlight the most likely root cause and impacted services.
Backend teams analyze recurring errors in APIs or microservices, using Calmo’s explanations and suggestions to implement durable fixes instead of temporary workarounds.
SRE and DevOps teams streamline incident response by triggering Calmo from alerts, then sharing its incident timeline and insights during postmortems.
Product engineering teams monitor new feature rollouts, relying on Calmo to flag performance regressions and surface the specific code paths causing slowdowns.
Technical leads and engineering managers use Calmo’s historical incident context to improve reliability practices and reduce future downtime.