AI AGENTS
RFP Section Drafter from a Requirements Doc
Reads an RFP requirements doc in Notion, researches your past wins and product facts, then drafts a complete response section back into the same Notion page for human review.
How it runs
The automated pipeline, trigger to output.
- TriggerNotion page tagged ready-to-draftNotion
- ActionRead requirement text and eval criteriaNotion
- ActionResearch supporting evidenceExa
- LogicEnough evidence to draft confidently?
- ActionDraft section in proposal voiceOpenAI
- OutputWrite draft back to Notion for reviewNotion
What it does
Turns a single RFP requirement section into a fully drafted, on-message response. The agent pulls the requirement text from Notion, gathers supporting evidence from public sources and your knowledge base, then writes a compliant draft and posts it back as a new block on the page marked Draft - Needs Review.
When to use it
Use it when a proposal manager has split an RFP into requirement sections and needs first-pass prose for each one fast. It removes the blank-page problem without letting the agent submit anything unreviewed.
How it works
- 1A Notion page is tagged ready-to-draft, firing the trigger.
- 2The agent reads the requirement text and any evaluation criteria from the page.
- 3It runs Exa research to find supporting facts, standards, and comparable language.
- 4A decision step checks whether enough evidence was gathered to write confidently.
- 5If yes, OpenAI drafts the section in your proposal voice; if not, it flags gaps as a checklist.
- 6The draft (or gap list) is written back to the Notion page for the proposal manager to edit and approve.
Set it up
What you configure once, before turning it on.
- 1Connect NotionPages, databases, comments.
- 2Connect ExaNeural search across the web.
- 3Connect OpenAIModels, embeddings, files.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
More AI Agents workflows
Stale Doc-PR Chaser for Runbook Gaps
On a daily schedule the agent finds runbook doc PRs that were opened from resolved incidents but never reviewed, summarizes what each one fixes.
On-Call Runbook Gap Closer: Resolved Sentry Issues to Doc PRs
An agent reads each newly resolved Sentry issue, compares the actual fix against your existing runbook, and opens a GitHub PR adding the missing remediation steps.
Datadog Bill Spike Attribution Agent
When a daily Datadog cost check detects a spend jump, an agent attributes the increase to the specific services and metric types driving it and posts a ranked breakdown to Slack.
Sentry-to-Confluence Runbook Updater
When a Sentry issue is resolved, the agent finds the matching Confluence runbook page and proposes an inline update with the verified fix.
Custom Metrics Cardinality Spike Pager
A webhook from a Datadog monitor fires when custom-metric cardinality jumps; an agent pinpoints the offending metric and tag, estimates the added cost.
Resolved Incident to Public Troubleshooting Doc
For customer-facing errors resolved in Sentry, the agent drafts a sanitized troubleshooting entry and opens a PR to your ReadMe documentation.
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.