RunLLM - AI that doesn’t just respond—it resolves
by•
Built on 10 years of UC Berkeley research, RunLLM reads logs, code and docs to resolve complex support issues. Saves 30%+ eng time, cuts MTTR by 50%, deflects up to 99% of tickets. Trusted by Databricks, Sourcegraph and Corelight—try for free on your product.
Replies
Wow, congratulations on the launch! Looks awesome, I'm giving it a try right now.
Though this looks kinda weird here
RunLLM is a solid AI support tool, it speeds up debugging and fits right into your current workflow without making things complicated. The clean interface and smart automation really help cut down on repetitive tasks. If you're in tech support and drowning in tickets, this is definitely something worth trying. I'd recommend it to any team looking to boost efficiency without reinventing their process.
RunLLM
@vince_torres1 Hi Vince - Appreciate your point of view in particular. Always feel free to reach out to the team if there's anything more you would like in the product, or we can help you in any way! Cheers!
RunLLM has been a game-changer for our team. It reads our logs and code to resolve issues fast—saved us tons of engineering hours and drastically cut down support tickets. Highly recommend giving it a try.
RunLLM
@deep_rock Hey Deep - this is great to hear. We're able to be a competent first line of defense, always-on in the places users and your team work. And we're further improving our ability to resolve issues end-to-end, and always bring in human support seamlessly when we don't have enough information. That last bit may sound mundane, but having a sense of knowing what we don't know and not trying to solve issues we cannot is paramount to maintaining trust. We don't ever want any "hold my beer" moments from our LLM.
Just spun up a test agent, blown away by how well it reads logs and actually reasons through issues. Feels more like hiring a staff engineer than plugging in a chatbot. Insanely useful.
This is EXACTLY what the industry needs! 🔥
As someone who's worked in technical support and engineering, I can't tell you how many times I've seen brilliant engineers get pulled away from building features to debug obscure customer issues that could have been solved with better tooling.
What makes RunLLM stand out:
🎓 10 years of UC Berkeley research - This isn't just another ChatGPT wrapper. There's serious academic rigor behind this.
📊 Those numbers are incredible - 99% ticket deflection? $1MM in saved engineering time at DataHub? These aren't vanity metrics, these are game-changing results.
🧠 The agentic reasoning approach - Finally, an AI that doesn't just search docs but actually understands the problem context and can analyze logs/telemetry.
💡 Multi-agent setup - Love that you can customize agents for different teams. Support, success, and sales all have different communication styles and needs.
The fact that you're trusted by companies like Databricks and Sourcegraph speaks volumes about the quality and reliability.
Quick questions:
How does it handle proprietary/sensitive codebases?
Any integration with popular ticketing systems like Zendesk/Intercom?
What's the learning curve for setting up custom workflows?
Definitely trying this out! The promise of giving engineers back 30% of their time to focus on what they do best is huge.
Congrats on the launch, Vikram! This could fundamentally change how technical support scales. 🚀
RunLLM
@saka5577 Thanks for sharing your feedback. Truly appreciated!
Just tried this out, super impressed by how it dives into logs and code with zero hand-holding. Feels like having a staff engineer triaging support on autopilot.
Very useful information i feel totally satisfied i recommend to all.
RunLLM
@dedipta_roy Thanks Depidta! That endorsement means a lot to us, and we truly appreciate it!
RunLLM
@yun_gong1 Thanks, Yun. Looking forward to further feedback!
The future of AI is already here. The automation of complex processes is already common in the technological world. The progress made in all of science is incredible.
RunLLM
@miguel_m_ We are pushing the edge, increasingly getting toward agentic work with MCP, tool use and other techniques.