AI & RAG

Slack objection handler with cited battlecard and call-snippet answers

Reps trigger a slash command in Slack with a deal objection; the flow retrieves matching battlecard and call-transcript passages from Postgres and replies in-thread…

CategoryAI & RAG
Enginesim
Difficultyintermediate
Triggerchat
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerRep runs objection slash command in SlackSlack
  • ActionRetrieve battlecard and call vectors from PostgresPostgreSQLPostgres
  • LogicBlend and re-rank, favoring approved battlecards
  • ActionDraft cited rebuttal via OpenAIOpenAI
  • OutputReply in Slack thread with answer and sourcesSlack

What it does

Gives reps an in-Slack way to handle objections live during a deal. They type the objection, and the flow searches both approved battlecards and real call transcripts in your Postgres vector store, then returns a suggested rebuttal grounded in those sources with quotes and Dropbox links so the rep can verify before using it.

When to use it

Use it when speed matters mid-deal and reps live in Slack. It is ideal for distributed sales teams who need an instant, trustworthy second opinion on pricing pushback, competitor FUD, or security concerns without paging enablement.

How it works

  1. 1A rep invokes the Slack slash command with their objection text, firing the trigger.
  2. 2The query is embedded and matched against battlecard and call-transcript vectors in Postgres.
  3. 3A logic step blends and re-ranks results, weighting approved battlecards above raw call snippets.
  4. 4OpenAI drafts a recommended response strictly from the retrieved passages, with citations.
  5. 5The answer and quoted sources are posted back into the Slack thread for the rep.

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 OpenAIModels, embeddings, files.
  4. 4
    Connect DropboxFiles and folders.
  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.

Run this workflow in your colony.

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