What is RunLLM?
RunLLM is an enterprise-grade AI support engine purpose-built for technical teams—designed not just to answer questions, but to debug, diagnose, and resolve real-world engineering issues autonomously. Born from over a decade of systems research at UC Berkeley, RunLLM functions as a self-updating AI support engineer that ingests your documentation, parses live code, interprets error logs, and learns from past customer interactions. It doesn’t mimic support—it replicates the reasoning of senior engineers: isolating root causes, validating fixes, and delivering actionable remediation steps. The result? Faster resolution of complex bugs and configuration errors, measurable ticket deflection before escalation, and sustained MTTR reduction—without adding headcount or compromising accuracy.
How to Use RunLLM
Getting started with RunLLM takes under five minutes—and zero engineering lift. Paste a link to your public or private docs (e.g., Confluence, ReadTheDocs, GitHub Wiki), connect your code repos (GitHub, GitLab, Bitbucket), and optionally import anonymized Zendesk or Slack support history. RunLLM’s adaptive training engine then constructs a contextual knowledge graph unique to your product stack. Within hours, your AI agent understands your architecture, common failure modes, and even nuanced terminology. Deploy it instantly across channels: embed it into your help center, activate it as a Slack bot for internal triage, or route incoming Zendesk tickets to RunLLM for pre-validation and auto-resolution. No APIs to manage—just intelligent, context-aware support, live and learning.
Go beyond reactive support by activating RunLLM’s closed-loop intelligence: it flags outdated documentation sections during troubleshooting, proposes verified code patches for recurring issues, and triggers smart handoffs to human agents only when confidence thresholds aren’t met—ensuring every interaction strengthens your operational resilience and brand voice.