RunDoc

I built RunDoc after spending years in DevOps watching engineers write the same runbooks from scratch during incidents. The AI Council feature — where 4 LLMs cross-review each other's output — came from frustration with single-model hallucinations in critical procedures. Happy to answer any questions about the architecture or use cases.

The AI Council angle is clever — having GPT-4o, Gemini, Claude and Grok cross-review before a Chairman synthesizes is a smart way to cut hallucinated commands, which is the real risk with auto-generated runbooks. One genuine question: do the rollback plans get validated against the user's actual stack/environment, or is that still on the engineer to verify before an incident? The 3am-during-an-incident framing really nails the pain point.

make doc great again!

It's a great product

https://neuronest.cc/

Great One man.. Good luck

The AI Council feature is a smart differentiator. Most AI tools generate content; having multiple LLMs review and refine it before delivery could make a huge difference for production-grade runbooks and incident response documentation.

This is such a smart solution to a real pain point — runbooks always seem to get written in a panic during incidents. Love that the AI Council cross-reviews to cut down on hallucinations. Congrats on the launch and wishing you tons of success!

The multi-LLM cross-review approach for runbooks is a smart design choice. Single-model outputs for critical infrastructure docs can miss edge cases or hallucinate commands — having 4 models independently generate then synthesize should catch those gaps. Curious whether the free tier's 5 runbooks/month resets or accumulates, and if there are plans to support custom templates for teams with specific compliance requirements.

The multi-LLM cross-review approach for runbooks is a smart design choice. Single-model outputs for critical infrastructure docs can miss edge cases or hallucinate commands — having 4 models independently generate then synthesize should catch those gaps. Curious whether the free tier's 5 runbooks/month resets or accumulates, and if there are plans to support custom templates for teams with specific compliance requirements.

The multi-LLM cross-review approach is a genuinely smart architectural choice for runbooks — single-model hallucinations in incident procedures could be catastrophic. Curious whether the Chairman model selection impacts output quality significantly, and if teams can customize the synthesis rules for their specific infrastructure stack. The free tier at 5 runbooks/month is generous enough to actually validate the tool before committing.

The multi-LLM cross-review approach is a genuinely smart architectural choice for runbooks. Single-model hallucinations in incident procedures could be catastrophic in production. The free tier at 5 runbooks/month is generous enough to validate the tool before committing. Would love to know if teams can customize the synthesis rules for their specific infrastructure stack.

This is a really clever approach to solving the hallucination problem in production environments. Having multiple LLMs cross-review the commands before a Chairman model finalizes the runbook is a solid design choice for infrastructure documentation where mistakes are high-stakes. Congrats on the launch!

This looks like a great product. I just upvoted.

The multi-LLM cross-review approach for runbooks is a smart design choice. Single-model outputs for critical infrastructure docs can miss edge cases or hallucinate commands — having 4 models independently generate then synthesize should catch those gaps. Curious whether the free tier's 5 runbooks/month resets or accumulates, and if there are plans to support custom templates for teams with specific compliance requirements.

It's a great product

the AI Council design is the standout here. most multi-LLM products just let you switch models — you're making them check each other, which matches my experience building on top of multiple AIs: the disagreement between models is where the real information lives, and a single model reviewing its own output never catches its own blind spots. one question on the Chairman step: when the four runbooks genuinely conflict (say, two different rollback strategies), does the synthesizer pick one, merge them, or surface the conflict to the user? for incident docs specifically, i'd almost want to see the disagreement — "models disagreed here" is itself a warning that the procedure has ambiguity. either way, "no more writing docs at 3am during an incident" is a painfully well-chosen pitch.

Really impressive approach — AI-generated runbooks validated by multiple LLMs is a smart safeguard against hallucinated commands in critical ops contexts. Curious how rollback plans handle environment differences across staging vs production?

Really worth it and it solve a lot of problems

I like the direction here. Runbooks + SOPs always end up being either too static or too scattered across tools, so having a more AI-driven layer makes sense. What stood out to me is the focus on turning operational knowledge into something executable. That’s usually where most AI productivity tools fail they stop at generation instead of workflow integration. Curious how you handle versioning of SOPs when teams start iterating fast.