RunLLM: The AI Support Engineer

RunLLM reads the material your product actually runs on, including documentation, help-center articles, past support threads, logs and source code. When a question arrives, it draws from those sources rather than from general knowledge, which is what keeps answers tied to your product instead of generic advice. The more sources you connect, the more specific the replies get.

The assistant reasons about what a user is really asking and decides which sources to consult. If a question is missing a detail, it asks for it instead of inventing an answer. It can also run code to verify a fix before suggesting it. This behavior is what lets it handle the vague, multi-step questions that trip up a simple documentation bot.

For products that generate logs, RunLLM reads error output and telemetry to diagnose what went wrong. This is the part behind its support ticket deflection, since the assistant resolves an issue from the failure data itself rather than waiting on a human. Instead of asking the user to describe the problem again, it works from the actual failure data and points to a likely cause. This is the part aimed at the hardest tickets, where the answer lives in a log line rather than in a help article.

The assistant can generate and validate code that responds to a specific question, such as a working request for an API a user is struggling with. Because it reads your own code and docs, the snippets match your real interface instead of a plausible guess. For developer-facing products, a runnable example is often the difference between a resolved ticket and a round trip.

You can build separate AI support agents for different audiences, such as one for support, one for sales and one for customer success, each with its own sources and instructions. A support assistant can be tuned to resolution while a sales assistant draws on different material. This keeps each assistant focused on the questions it is meant to field.

RunLLM answers inside the channels your customers use, including a widget on your own site, Slack, Microsoft Teams and helpdesks like Zendesk. Users get help where they already are rather than being sent somewhere new. That placement is a large part of why it can deflect tickets, since an answer in the same window beats a redirect to a portal.

The pitch is a short onboarding: connect your sources and your channels, and the assistant starts answering. From there it improves as you correct weak replies and as it sees more real questions. The team frames it as fast to start and sharpened by use, rather than a months-long deployment.