AI AGENTS
Zoom Demo Objection Extractor to Per-Rep Notion Coaching Doc
When a Zoom sales demo recording finishes, an agent transcribes it, extracts every buyer objection with the rep's response.
How it runs
The automated pipeline, trigger to output.
- TriggerZoom recording completed for a demo callZoom
- ActionFetch transcript and meeting metadataZoom
- ActionExtract objections paired with rep responsesOpenAI
- ActionScore each rebuttal and flag missesOpenAI
- LogicResolve host rep and find their coaching page
- OutputAppend dated coaching entry to rep's Notion docNotion
What it does
Turns each recorded Zoom demo into a coaching artifact. The workflow pulls the recording transcript, uses an LLM to find every objection the prospect raised, captures how the rep handled it, scores the rebuttal, and writes a dated entry into the rep's individual Notion coaching page.
When to use it
Run this when you have a sales team doing live demos over Zoom and you want consistent, per-rep coaching instead of ad-hoc manager spot-checks. Ideal for enablement leads who want a paper trail of objection handling that compounds over time.
How it works
- 1Zoom fires its recording-completed event for a finished demo.
- 2The flow fetches the recording's transcript and meeting metadata (host, participants, duration).
- 3An OpenAI step extracts each objection (pricing, timing, competitor, authority) paired with the rep's verbatim response.
- 4A second LLM pass scores each rebuttal and tags missed opportunities.
- 5The agent resolves which rep hosted the call and locates their Notion coaching page.
- 6A dated coaching block is appended to that rep's Notion doc with objections, responses, scores, and suggested better lines.
Set it up
What you configure once, before turning it on.
- 1Connect ZoomMeetings, recordings, transcripts.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect NotionPages, databases, comments.
- 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.