Deploy an AI Engineering Assistant to handle alerts. It turns each alert into a search for the best (safe) automation to run, summarizing the results into engineering-ready tickets.

Let it automate remediation in non-prod environments, and set it to human-in-the-loop mode for production.

Industry benchmarks today show this process costs $500-700/ticket, but with RunWhen you can expect $5-7.

Deploy an AI Engineering Assistant to listen for questions in pre-production and production slack help channels. It turns each question into a search for the best (safe) automation, running this automation on the users' behalf.

It is instant self-service for your developers and L1 on-call staff, with guard rails attached and no training required.

>70% of dev teams building on Kubernetes report they get blocked on platform/infra issues at least once a month because they don't have tools like this.

Any engineer can just type what is on their mind, and an Engineering Assistants will suggest (and run) automation, find relevant previous tickets or re-draft a ticket based on new information surfaced by the automations' results.

Check which services are impacted, fetch errors from logs, correlated this back to observability data and more while the Assistant is handling the write-up in parallel.

It is like vibe coding, but for troubleshooting complex environments.

Each Engineering Assistant has its own scope, its own credentials and its own personality. Make some of them narrow for a small set of tasks, and others that cover entire environments. Make some read-only and others read-write.

The RunWhen platform is intended to build not one Engineering Assistant, but an entire team of Assistants that scales to the complexity of your organization.

By giving Assistants to your dev teams, your on-call teams and others, for the first time you actually can be everywhere at once.