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RunLLM Overview

RunLLM is an AI-powered technical support engineer that saves your team time and improves customer experience by providing instant answers to customer questions. Its high-quality answers help support, engineering, and product teams focus on more important tasks.

RunLLM learns about your products by reading your documentation, guides, APIs, and other data sources. Using a mix of custom data engineering, fine-tuned language models, and multi-LLM agents, it provides precise answers and valuable customer insights.

  • Save Time: Automate support processes to reduce workload for support and engineering teams.
  • Improve Customer Experience: Provide instant, accurate answers to enhance customer satisfaction.
  • Generate Valuable Insights: Gain insights from customer interactions to improve product and documentation.

Key Features​

  • Highest-Quality Answers: Accurate, contextually appropriate responses to customer inquiries.
  • Strong Guardrails: Prevents AI from generating off-topic or inaccurate responses.
  • Adaptive Chat: Human-like, multimodal interactions for richer customer engagement.
  • Data Connectors: Connect easily to product documentation, APIs, and guides.
  • Flexible Deployment: Deploy to Slack, Discord, Zendesk, or embed on your website. You can also use the RunLLM to build custom experiences.
  • Insights and Analytics: Topic modeling, documentation improvement suggestions, and weekly summary digests.

Use cases​

Support and product teams use RunLLM in two ways:

  1. Autonomous support agent: Most commonly, RunLLM is a resource that's available to your customers. They can come to RunLLM (on your documentation site, via support Slack channels, etc.), ask RunLLM a question, and receive an answer within seconds to unblock themselves.
  2. Support copilot: Support teams looking to improve their ticket resolution rate use RunLLM to auto-generate answers (via Slack, Zendesk, etc.) that can be edited before being sent out to customers.

Why RunLLM​

  • Reduced Workload: Automate routine inquiries to save time for support and engineering teams.
  • Higher Ticket Deflection: Empower customers to self-serve, reducing the number of support tickets.
  • Faster Response Times: Decrease mean time to resolution, enhancing customer satisfaction and team efficiency.