AI AGENTS
RFP Past-Proposal Reuse Finder
When a new RFP section needs drafting, the agent searches your archive of past proposals in Google Drive for reusable answers and drafts a tailored response combining the best…
How it runs
The automated pipeline, trigger to output.
- TriggerNotion requirement card moved to needs-draftNotion
- ActionRead requirement textNotion
- ActionSearch past-proposals archiveGoogle Drive
- LogicStrong prior match found?
- ActionAdapt and merge into tailored draftOpenAI
- OutputWrite draft with source refs to NotionNotion
What it does
Mines your library of previously submitted proposals to avoid re-writing answers you already have. For a given new requirement, it finds the closest matching passages from past proposals, then drafts a fresh response that reuses proven language while tailoring it to the new RFP.
When to use it
Use it when your team has a deep archive of won proposals and most new requirements are variations on questions you have answered before. It turns institutional knowledge into first drafts.
How it works
- 1A Notion requirement card is moved to needs-draft, firing the trigger.
- 2The workflow reads the requirement text from Notion.
- 3It searches the Google Drive past-proposals folder for relevant prior answers.
- 4A decision step checks whether a strong enough prior match was found.
- 5If matches exist, OpenAI adapts and merges them into a tailored draft; if not, it writes from scratch and flags the section as net-new.
- 6The tailored draft, with source references to the reused proposals, is written back to the Notion card.
Set it up
What you configure once, before turning it on.
- 1Connect NotionPages, databases, comments.
- 2Connect Google DriveDocs, sheets, slides, files.
- 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
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.
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.
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.
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.
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.
Weekly On-Call Doc-Gap Digest
Each week the agent reviews every Sentry issue resolved in the last 7 days, ranks the ones whose runbook coverage is missing or thin.
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.