AI & RAG

On-call remediation assistant with postmortem citations

Answers 'how do I remediate X' questions posted in Slack by searching past incident postmortems and returning step-by-step fixes with linked source citations.

CategoryAI & RAG
Enginesim
Difficultyintermediate
Triggerevent
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerEngineer @-mentions the bot in Slack with a remediation questionSlack
  • ActionEmbed question and run pgvector similarity search over postmortemsPostgreSQLPostgres
  • ActionFetch full text of top-matching postmortem pagesConfluenceConfluence
  • LogicIf no match clears the relevance threshold, flag low confidence
  • ActionSynthesize cited remediation plan from retrieved sourcesOpenAI
  • OutputReply in Slack thread with steps plus citation linksSlack

What it does

Gives your on-call engineer an in-Slack assistant that answers remediation questions by retrieving the most relevant past incident postmortems and synthesizing a concrete fix — every claim backed by a citation link to the source doc.

When to use it

Use it when an alert fires at 2am and the responder needs to know how a similar incident was resolved last time, without manually grepping through a quarter's worth of postmortems. Best for teams with a mature postmortem habit in Confluence.

How it works

  1. 1An engineer mentions the bot in Slack with a question like 'how do I remediate Redis connection pool exhaustion'.
  2. 2The workflow embeds the question and runs semantic search against the postmortem vector index in Postgres (pgvector).
  3. 3It pulls the full text of the top-matching postmortems from Confluence.
  4. 4An LLM synthesizes a ranked remediation plan, quoting only what the sources support.
  5. 5The answer is posted back in-thread with inline citation links and a confidence note when coverage is thin.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SlackChannels, DMs, threads, mentions.
  2. 2
    Connect PostgresAny Postgres URL — query, write, migrate.
  3. 3
    Connect ConfluenceSpaces, pages, blueprints.
  4. 4
    Connect OpenAIModels, embeddings, files.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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