AI & RAG

ADR Answerbot: Slack questions grounded in Confluence + Postgres

Answers engineers' architecture questions in Slack by retrieving the relevant Architecture Decision Records from Confluence and the decision index in Postgres.

CategoryAI & RAG
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerEngineer asks a question in SlackSlack
  • ActionEmbed question and retrieve top ADR ids from Postgres indexPostgreSQLPostgres
  • ActionFetch full ADR pages from ConfluenceConfluenceConfluence
  • ActionSynthesize grounded answer with OpenAI over retrieved textOpenAI
  • OutputReply in Slack thread with cited ADR linksSlack

What it does

Turns your team's scattered Architecture Decision Records into an on-demand Slack answerbot. When someone asks "why do we use Postgres over DynamoDB?" the bot finds the ADRs that decided it and answers in plain English with links back to the source records.

When to use it

Use it when engineers keep re-asking settled architecture questions, when onboarding hires spend hours hunting for "why" decisions, or when ADRs live in Confluence but nobody reads them. Best for teams with a maintained ADR space and a decision index table.

How it works

  1. 1An engineer mentions the bot or posts in a watched Slack channel, sending the question.
  2. 2The bot embeds the question and queries the Postgres decision index for the top matching ADR ids and metadata (status, supersedes, owner).
  3. 3It fetches the full body of those ADR pages from Confluence.
  4. 4OpenAI synthesizes a grounded answer using only the retrieved ADR text, refusing to speculate beyond it.
  5. 5The answer posts back in-thread with ADR titles, statuses, and direct links as citations.

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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