RunLLM

Overview

In the fast-paced world of software development and customer support, resolving complex technical issues quickly is paramount. Enter RunLLM, an innovative AI-powered support platform built on over a decade of UC Berkeley research through RISELab, designed to revolutionize how engineering and support teams tackle intricate problems. By intelligently analyzing logs, code, and documentation through advanced agentic reasoning and multi-LLM orchestration, RunLLM aims to resolve complex support issues while saving over 30% of engineering time, cutting Mean Time To Resolution by 50%, and achieving up to 99% support ticket deflection rates. Trusted by industry leaders including Databricks, Sourcegraph, Corelight, DataHub, vLLM, and Arize AI, RunLLM offers a comprehensive solution that transforms technical support from reactive troubleshooting to proactive problem-solving.

Key Features

RunLLM stands out with enterprise-grade capabilities designed to optimize support and engineering workflows through advanced AI technology:

• Agentic reasoning and multi-step analysis: Employs sophisticated AI agents that analyze questions, seek clarification, scan logs and telemetry data, and orchestrate multiple specialized LLMs to surface the most relevant and accurate solutions.
• Custom fine-tuned models per customer: Trains dedicated language models tailored to each product’s specific terminology, functionality, and edge cases, ensuring domain expertise rather than generic responses.
• Advanced data pipeline integration: Precisely ingests and annotates documentation, APIs, guides, code examples, support tickets, and logs from platforms like Datadog, Splunk, GCP Logging, and Grafana for comprehensive context.
• Real-time code execution and validation: Automatically writes, tests, and validates code in isolated sandbox environments, ensuring generated solutions work in customer-specific configurations before delivery.
• Multimodal support with human escalation: Handles text, code, and images while seamlessly escalating complex edge cases to human engineers when AI confidence levels require expert intervention.

How It Works

RunLLM operates through a sophisticated architecture combining multiple advanced AI technologies. The platform begins by ingesting comprehensive data from documentation, codebases, support tickets, logs, and telemetry systems. Using custom fine-tuned language models specifically trained for each customer’s product, RunLLM employs agentic reasoning to deeply understand user questions, perform multi-step analysis, and determine optimal solution paths. The system leverages GraphRAG (Graph Retrieval-Augmented Generation) to build structured knowledge graphs that support hierarchical retrieval and alternative solution exploration. Through policy-driven re-ranking, RunLLM scores information by authority, freshness, and relevance while executing code in ephemeral sandbox environments to validate solutions. This multi-agent approach ensures consistently accurate, contextually appropriate responses while maintaining enterprise-grade security through SOC 2 Type II compliance and granular data governance controls.

Use Cases

RunLLM’s advanced capabilities address critical needs across technical support and development operations:

• Enterprise technical support automation: Handles complex customer issues by analyzing debugging logs, pinpointing root causes, and providing validated solutions with code examples, significantly reducing escalation to human engineers.
• Developer productivity and knowledge scaling: Transforms fragmented documentation, past support threads, and tribal knowledge into an always-available expert assistant that helps both internal teams and external developers navigate complex technical products.
• Proactive documentation improvement: Continuously analyzes support interactions to identify knowledge gaps, suggest documentation updates, and auto-generate missing content, creating a feedback loop for continuous improvement.
• Multi-channel support deployment: Integrates seamlessly across Slack, Discord, Zendesk, documentation sites, and custom applications, providing consistent expert-level assistance wherever users and teams work.

Pros \& Cons

RunLLM offers significant advantages while maintaining transparency about current limitations:

Advantages

• Proven enterprise results with measurable ROI: Demonstrated success with customers like DataHub saving over \$1 million in engineering costs, vLLM deflecting 99% of community questions, and Corelight reducing support workload by 30%.
• Superior accuracy through specialized training: Fine-tuned models and multi-agent architecture deliver domain-specific expertise that significantly outperforms generic AI solutions for complex technical queries.
• Comprehensive integration ecosystem: Supports extensive data sources including major documentation platforms, ticketing systems, communication tools, and logging platforms with enterprise-grade security and compliance.
• Continuous learning and improvement: Instant feedback incorporation, automated knowledge gap identification, and proactive documentation suggestions create a self-improving support system.

Disadvantages

• Requires structured technical content: Optimal performance depends on well-organized documentation, code repositories, and historical support data, making initial setup more complex for organizations with fragmented knowledge bases.
• Enterprise-focused with corresponding complexity: Advanced features and customization options may present a steeper learning curve compared to simpler chatbot solutions, though comprehensive onboarding support is provided.
• Limited to technical support domains: Specialized optimization for technical products means reduced effectiveness for general customer service, marketing, or non-technical support scenarios.

How Does It Compare?

RunLLM occupies a unique position in the 2025 AI support landscape through its specialized focus on technical products and proven enterprise results.

Forethought (2025): Forethought has evolved into a multi-agent, omnichannel AI platform following its \$25M Series D funding, expanding beyond customer support into sales and marketing functions. With features like Solve, Assist, Triage, and Discover, Forethought offers broader CX automation but focuses on general customer service rather than deep technical problem-solving. While Forethought excels at ticket routing and basic deflection across multiple channels, RunLLM provides superior technical accuracy through code execution, log analysis, and domain-specific fine-tuning for complex developer tools and infrastructure products.

Kapa AI (2025): Kapa AI specializes in transforming technical documentation into AI assistants, serving companies like OpenAI, Docker, and Logitech with SOC 2 Type II compliance and technical data connectors. Both platforms target technical products, but Kapa AI primarily focuses on documentation-based Q\&A, while RunLLM offers comprehensive support including log analysis, code execution, real-time debugging, and proactive knowledge management. RunLLM’s multi-agent architecture and custom fine-tuning provide deeper problem-solving capabilities beyond Kapa’s documentation-centric approach.

Zendesk AI (2025): Zendesk’s Advanced AI features include intelligent triage, generative responses, and conversation bots integrated with their comprehensive ticketing platform. Zendesk excels in workflow automation and general customer service but lacks RunLLM’s specialized technical capabilities like code execution, log analysis, and domain-specific fine-tuning. While Zendesk serves broader customer service needs, RunLLM specifically addresses the complex requirements of technical support teams managing developer tools, SaaS platforms, and infrastructure products.

Emerging Technical AI Support: The 2025 market includes various specialized technical support tools, but RunLLM distinguishes itself through proven enterprise success stories, UC Berkeley research foundation, and comprehensive multi-modal capabilities combining documentation, code, logs, and real-time execution in a single platform.

Final Thoughts

RunLLM represents a transformative approach to technical support that goes far beyond traditional AI chatbots or generic support automation. Built on rigorous academic research from UC Berkeley’s RISELab and proven through real-world deployments at leading technology companies, RunLLM addresses the unique challenges of supporting complex technical products. Its combination of custom fine-tuned models, agentic reasoning, code execution capabilities, and comprehensive data integration creates an AI support engineer that truly understands and solves technical problems rather than simply providing generic responses. The platform’s demonstrated results—from DataHub’s \$1 million cost savings to vLLM’s 99% question deflection rate—validate its effectiveness in transforming support operations. For organizations committed to scaling technical support without compromising quality, RunLLM offers a compelling solution that enhances both customer experience and internal team productivity. As the complexity of technical products continues to grow, RunLLM’s specialized approach to AI-powered support positions it as an essential tool for maintaining competitive advantage through superior customer support and faster issue resolution.