MARKETING

Build a Cumulative Landing-Page Learnings Knowledge Base

When an experiment closes, an agent extracts the durable insight — what changed, who it moved, and why — and writes a structured.

CategoryMarketing
Enginepaperclip
Difficultyadvanced
Triggerwebhook
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerExperiment-closed webhook arrivesHTTP webhook
  • ActionRead full results and original hypothesisPostgreSQLPostgres
  • ActionSearch knowledge base for related prior testsNotionNotion
  • LogicSynthesize and link a structured learnings entry
  • ActionWrite the entry to the knowledge baseNotionNotion
  • OutputPost distilled takeaway to the teamSlack

What it does

Converts each finished landing-page experiment into a reusable lesson. Rather than just logging numbers, an agent reasons over the full result and records the transferable principle, then connects it to related past tests so patterns emerge over time.

When to use it

Use it when your team keeps re-running variations of tests you already learned from. This compounds experiment results into institutional knowledge instead of scattered one-off logs.

How it works

  1. 1A webhook fires when an experiment is closed (won or lost).
  2. 2The agent reads the full result set and original hypothesis from Postgres.
  3. 3It searches the existing knowledge base in Notion for related prior experiments to compare against.
  4. 4It synthesizes a structured learnings entry — the change tested, the audience segment affected, the directional finding, and confidence — and links it to the related tests it found.
  5. 5It writes the entry to the Notion knowledge base.
  6. 6It posts the distilled takeaway to Slack so the lesson reaches the team immediately.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect HTTP webhookTrigger any URL on agent actions.
  2. 2
    Connect PostgresAny Postgres URL — query, write, migrate.
  3. 3
    Connect NotionPages, databases, comments.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  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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