AI & RAG
Grounded runbook answer bot for Slack incidents
Answers engineers' on-call questions in Slack using only the versioned runbook wiki.
How it runs
The automated pipeline, trigger to output.
- TriggerEngineer mentions bot in Slack on-call channelSlack
- ActionEmbed question and retrieve top runbook chunks from pgvectorPostgres
- LogicGate on relevance score; abstain if below threshold
- ActionGenerate citation-constrained answer with OpenAIOpenAI
- OutputReply in-thread with answer and Confluence source linksSlack
What it does
Gives on-call engineers fast, trustworthy answers drawn strictly from your engineering runbook wiki. Every reply includes citations to the source Confluence pages, and the bot abstains rather than guessing when the runbook does not cover the question.
When to use it
Use it during incidents or routine on-call when engineers keep asking "what's the runbook for X?" in Slack and you want answers grounded in the canonical wiki instead of half-remembered tribal knowledge or hallucinated steps.
How it works
- 1A Slack message mentions the bot in your on-call channel, carrying the engineer's question.
- 2The question is embedded and matched against the indexed runbook chunks stored in Postgres (pgvector).
- 3A relevance gate checks the top match score; if nothing clears the threshold, the bot posts an honest "no runbook covers this" reply.
- 4OpenAI generates an answer constrained to the retrieved passages, with inline source markers.
- 5The bot replies in-thread with the grounded answer plus deep links to each cited Confluence page.
Set it up
What you configure once, before turning it on.
- 1Connect SlackChannels, DMs, threads, mentions.
- 2Connect PostgresAny Postgres URL — query, write, migrate.
- 3Connect OpenAIModels, embeddings, files.
- 4Connect ConfluenceSpaces, pages, blueprints.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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