Product Introduction

1. RunLLM is an enterprise-ready AI platform designed to automate and enhance technical support operations through advanced language models and data integration. It processes logs, codebases, and documentation to resolve complex customer issues autonomously while integrating with existing workflows like Slack, Zendesk, and documentation portals. The system leverages agentic planning and multi-LLM orchestration to deliver context-aware solutions validated for accuracy.
2. The core value of RunLLM lies in its ability to reduce engineering workload by 30%, cut mean time to resolution (MTTR) by 50%, and deflect up to 99% of support tickets through precise, automated responses. It transforms fragmented knowledge sources into a unified knowledge graph, enabling 24/7 support scalability without compromising quality. Enterprises like Databricks and Corelight use it to improve customer retention and operational efficiency.

Main Features

1. RunLLM employs agentic reasoning to analyze support queries, seek clarifications, and scan logs/telemetry data to generate validated code snippets or configuration fixes. This feature combines retrieval-augmented generation (RAG) with fine-tuned LLMs to ensure answers align with product-specific terminology and edge cases.
2. The platform integrates custom data pipelines that ingest and annotate documentation, code repositories, and support tickets to build a dynamic knowledge graph. This enables context-aware responses that reference the latest product updates, customer configurations, and historical troubleshooting data.
3. Multi-LLM agents collaborate on complex queries, applying rigorous validation steps like code testing and documentation cross-referencing before delivering answers. Users can configure distinct AI agents for specific roles, such as a Support Engineer for technical resolutions or a Sales Copilot for business-oriented responses.

Problems Solved

1. RunLLM addresses the inefficiency of manual support processes by automating root cause analysis, log parsing, and code generation for recurring technical issues. It eliminates delays caused by human ticket routing and reduces dependency on specialized engineers for routine troubleshooting.
2. The product targets technical support teams at enterprises with complex software products, such as SaaS platforms or developer tools, where high ticket volumes and intricate customer environments strain resources. It also serves open-source projects needing scalable community support.
3. Typical use cases include resolving deployment errors by analyzing debug logs, generating environment-specific code fixes, and proactively updating documentation based on recurring user queries. It also deflects repetitive questions via Slack or Zendesk integrations, freeing engineers for high-impact tasks.

Unique Advantages

1. Unlike generic AI chatbots, RunLLM combines fine-tuned domain-specific models with a knowledge graph architecture, enabling deeper contextual understanding of technical products. Competitors lack its UC Berkeley research-backed agentic planning framework for multi-step problem-solving.
2. The platform innovates with hybrid RAG-fine-tuning pipelines that continuously train models on ingested data while enforcing answer validation through automated code testing and documentation verification. This ensures responses are both accurate and actionable.
3. Competitive advantages include proven production results (13k+ monthly questions answered, 99% deflection rate) and seamless integration with developer tools like GitHub and observability platforms. Enterprise-grade security and SOC 2 compliance further differentiate it for large-scale deployments.

Frequently Asked Questions (FAQ)

1. What systems does RunLLM integrate with? RunLLM supports native integrations with Slack, Zendesk, Discord, and documentation platforms like ReadMe, alongside API access for custom deployments. It synchronizes with code repositories (GitHub, GitLab) and observability tools to ingest real-time log data.
2. How does RunLLM handle sensitive data? The platform uses isolated data pipelines with role-based access controls (RBAC) and SOC 2-compliant encryption. Customers can opt for on-premise deployments or private cloud instances to maintain full data governance.
3. Can we customize AI agent behavior? Yes, users define agent personas with specific tones, response formats, and escalation protocols. For example, agents can be configured to provide step-by-step CLI commands for developers or summarize issues for product managers.
4. How long does deployment take? Initial setup requires linking documentation URLs, after which RunLLM generates a functional AI agent within hours. Full production deployment with code/log integration typically takes 3-5 business days.
5. Does RunLLM support non-English languages? While optimized for English, the platform’s fine-tuning framework allows training on multilingual documentation and support tickets. Custom language models can be deployed for specific regional or technical lexicons.