AI & RAG
Agent that rewrites weak help articles to match how users actually ask
For each article flagged as a chronic retrieval miss, an agent reads the failing user phrasings, drafts an improved version with better headings and synonym coverage.
How it runs
The automated pipeline, trigger to output.
- TriggerScheduled rewrite batch
- ActionLoad flagged articles + missed phrasingsPostgres
- ActionFetch current article bodiesIntercom
- ActionAgent drafts retrieval-optimized rewriteOpenAI
- LogicSelf-check accuracy and intent
- OutputOpen Linear ticket with before/after draftLinear
What it does
This is the fix step, not just detection. It takes articles already flagged as recurring retrieval misses, pulls the real phrasings customers used, and has an agent rewrite each article so its headings, intro, and keyword coverage match those phrasings. The agent produces a ready-to-review draft rather than publishing blindly.
When to use it
Run it after your gap auditor has accumulated a backlog of weak articles and you want drafts prepared instead of starting each rewrite from scratch. Best for content teams comfortable reviewing AI drafts before publishing.
How it works
- 1A schedule kicks off the rewrite batch.
- 2Read the flagged articles and their missed phrasings from Postgres.
- 3Fetch each article's current body from Intercom.
- 4An agent analyzes the gap between user wording and article content, then drafts a revised article optimized for retrieval recall.
- 5The agent self-checks that the draft preserves factual accuracy and intent.
- 6Open a Linear ticket per article with the original, the draft, and a changelog for an editor to approve and publish.
Set it up
What you configure once, before turning it on.
- 1Connect PostgresAny Postgres URL — query, write, migrate.
- 2Connect IntercomConversations, contacts, articles.
- 3Connect OpenAIModels, embeddings, files.
- 4Connect LinearIssues, projects, cycles, triage.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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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.
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