RunLLM: The AI Support Engineer

Product Introduction

1. RunLLM: The AI Support Engineer is an enterprise-ready platform that resolves complex technical support issues by analyzing logs, code, and documentation using advanced AI models. It automates ticket resolution, reduces engineering workload, and integrates seamlessly with existing tools like Slack, Zendesk, and documentation platforms.
2. The core value lies in its ability to save 30%+ engineering time, cut mean time to resolution (MTTR) by 50%, and deflect up to 99% of support tickets through AI-driven analysis and validated code generation.

Main Features

1. RunLLM combines agentic planning, knowledge graphs, and fine-tuned LLMs to deliver context-aware solutions by scanning logs, telemetry, and documentation for precise answers. It autonomously seeks clarifications and validates responses to ensure accuracy.
2. The platform enables customization of AI agents with distinct tones, behaviors, and output formats, such as generating step-by-step code for engineers or concise business-level responses for sales teams.
3. It integrates fragmented data sources—including docs, support threads, and codebases—into a unified knowledge graph, enabling consistent answers across Slack, Zendesk, and internal documentation platforms.

Problems Solved

1. RunLLM addresses the inefficiency of manual technical support by automating root cause analysis, code generation, and documentation updates, reducing repetitive engineering tasks.
2. It targets enterprise technical support teams at companies with complex products, such as Databricks and Sourcegraph, who need scalable solutions for high-volume, technical inquiries.
3. Typical use cases include resolving customer-reported bugs by analyzing logs, deflecting common queries via AI-generated answers, and improving documentation accuracy through proactive updates.

Unique Advantages

1. Unlike generic AI assistants, RunLLM uses multi-LLM orchestration and fine-tuned models trained on domain-specific data to handle nuanced technical terminology and edge cases.
2. Its agentic RAG (Retrieval-Augmented Generation) framework rigorously validates answers against logs and codebases, ensuring higher precision than standard retrieval-based systems.
3. Competitive advantages include proven deployment at scale with SOC 2-compliant infrastructure, prebuilt integrations for enterprise tools, and results validated by customers like Corelight and Arize.

Frequently Asked Questions (FAQ)

1. What channels does RunLLM integrate with? RunLLM deploys AI agents directly into Slack, Zendesk, Discord, and documentation sites, ensuring seamless support across user-preferred platforms.
2. How does RunLLM handle complex debugging scenarios? The AI analyzes logs, replicates issues in test environments, and generates validated code snippets tailored to the user’s specific configuration.
3. Is RunLLM compliant with enterprise security standards? Yes, RunLLM operates on SOC 2-compliant infrastructure and offers on-premises deployment options for sensitive data environments.
4. How quickly can teams deploy RunLLM? Teams can start testing within minutes by linking their documentation, with full deployment possible in days using prebuilt connectors and APIs.
5. Can RunLLM adapt to unique product terminologies? Custom fine-tuning trains models on your codebase and support history, ensuring alignment with proprietary terminology and workflows.