AI & RAG
Slack RFC Answer-Bot Grounded in Confluence + GitHub
Answers engineering questions posted in Slack by retrieving relevant Confluence RFCs and GitHub code, then replies with a grounded answer and clickable source citations.
How it runs
The automated pipeline, trigger to output.
- TriggerEngineer @-mentions bot in SlackSlack
- ActionRetrieve matching RFC + code chunks from vector storeSupabase
- LogicGate: any chunk above similarity threshold?
- ActionCompose grounded answer with citation markersOpenAI
- OutputReply in Slack thread with linked citationsSlack
What it does
Turns a Slack channel into a self-serve engineering knowledge desk. When someone asks "how does our auth token refresh work?", the bot searches your Confluence RFC space and indexed GitHub code, composes an answer strictly from what it found, and posts back with inline citations linking to the exact page and file.
When to use it
When senior engineers keep answering the same architecture questions in Slack, or when onboarding hires need design context that lives across wikis and repos. Best when your RFCs are in Confluence and the implementation is in GitHub.
How it works
- 1An engineer @-mentions the bot in Slack with a question.
- 2The question is embedded and matched against a Supabase vector store of Confluence RFCs and GitHub source chunks.
- 3A relevance gate checks whether any chunk clears the similarity threshold; if nothing does, the bot replies that it has no grounded source and stops.
- 4OpenAI composes an answer constrained to the retrieved passages, with numbered citation markers.
- 5The bot posts the answer in-thread with each citation rendered as a Confluence/GitHub deep link.
Set it up
What you configure once, before turning it on.
- 1Connect SlackChannels, DMs, threads, mentions.
- 2Connect ConfluenceSpaces, pages, blueprints.
- 3Connect GitHubRepos, issues, pull requests, actions.
- 4Connect SupabaseTables, auth, storage, edge functions.
- 5Connect OpenAIModels, embeddings, files.
- 6Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 7Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 8Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
More AI & RAG workflows
RFP and security questionnaire drafter grounded in Coda
Drafts answers to inbound RFP and security questionnaire questions by retrieving approved language from your Coda hub, then files the cited draft for review before a rep sends it.
Detect Breaking API Changes from Spec Diffs and Alert Owners
Compares the new OpenAPI spec against the previous version on each GitLab merge, uses retrieval over the changelog to classify whether changes are breaking.
Grounded reply suggestions for inbound sales email
Reads inbound prospect emails, retrieves the matching answers from your Coda hub.
Coda-grounded sales answer bot with citations in Slack
Reps ask product, pricing, or competitive questions in Slack and get an answer drawn only from your Coda knowledge hub, with links to the exact docs and rows it pulled from.
On-Call Spec Answerer from Dropbox Engineering Corpus
Answers on-call questions posted in a Slack channel by retrieving the most relevant Dropbox engineering specs and replying with a grounded, source-cited answer in the thread.
Agentic Deep-Dive API Researcher for Hard Spec Questions
An agent fielded via webhook that answers multi-part API questions by iteratively searching OpenAPI specs, changelogs, and Confluence runbooks.
Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.