AI-Powered Inbox Triage
Classify, draft and route every incoming email so you only see what needs you.
/ quick answer
An LLM classifies every email into intent buckets (sales, support, admin, spam, personal), drafts a reply where safe, and applies labels + priority. Classify, draft and route every incoming email so you only see what needs you.
- 01Ingest new mail via Gmail API or a Make/Zapier trigger.
- 02Run a classifier prompt with a fixed enum of intents and a confidence score.
- 03For 'sales' and 'support': draft a reply grounded on your FAQ / RAG index; leave in Drafts, don't send.
- 04For 'admin': extract structured fields (invoice #, date, amount) and push to Notion.
- 05Apply Gmail labels + star; the human only reviews high-priority + drafts.
- Add a weekly 'inbox health' report with time saved and top intents.
- Route sales leads directly to CRM with enrichment.
What does the AI-Powered Inbox Triage workflow do?
An LLM classifies every email into intent buckets (sales, support, admin, spam, personal), drafts a reply where safe, and applies labels + priority.
What problem does AI-Powered Inbox Triage solve?
Founders and operators lose 1–2 hours/day to email that a system could handle. Filters aren't enough because intent is fuzzy.
How many steps does AI-Powered Inbox Triage take?
5 steps. It starts with ingest new mail via gmail api or a make/zapier trigger. and ends with apply gmail labels + star; the human only reviews high-priority + drafts..
Which tools does AI-Powered Inbox Triage need?
It uses ai-automation-operator-stack, no-code-automation-stack — each linked below with its own node.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →Structured Output
Forcing AI responses into predictable schemas that software can use.
- →Human-in-the-Loop
A control pattern where humans review high-risk AI decisions before execution.
- →Webhook
An HTTP callback that lets one system push data to another the moment an event happens.
- →PII Redaction
Stripping personally identifiable information before sending to a model.
Related workflows
Turn this into a repeatable process.
- →Telephony AI Voice Integration
Telephony AI Voice Integration is a workflow that connects AI voice agents with traditional phone systems to automate customer interactions, providing scalable and efficient support.
- →Automated GitHub Issue Triage
Label, prioritize, and route incoming issues without a maintainer.
Related tool stacks
The tools that run it in production.
- →AI Automation Operator Stack
The default toolset for one operator running multiple AI-powered business workflows.
- →No-Code Automation Stack
The default toolset for an operator running business workflows without engineers.
- →Solo Founder Daily AI Stack
The 6-tool stack a solo founder actually uses every day — no fluff.
- →Community Manager AI Stack
Run an active community (Discord/Slack/Circle) with 1 CM + AI.
Related prompts
Reusable prompts for this job.
- →Lead Qualification Prompt
Score a lead against your ICP and explain the decision.
- →No-Code Automation Spec Writer
Turn a vague 'I want to automate X' into a buildable scenario spec for Make / n8n / Zapier.
- →Meeting to Actions Prompt
Extract decisions, owners, and dates from a meeting transcript.
- →Weekly Review Prompt
Automate the Friday review from calendar + tasks + notes.
Related use cases
How people apply it, and what came out.
- →Law Firm Automates Intake, Cuts Response Time 90%
Mid-sized firm deploys an AI intake agent for after-hours leads.
- →3-Person Agency Outproduces 15-Person Competitors
Boutique agency uses AI ops across delivery, sales, and reporting.
- →Coach Automates Onboarding, Doubles Client Count
Executive coach removes the 4h admin tax on every new client.
- →HR Team Screens 10x More Applicants With AI
50-person company handles 500 applicants/role instead of struggling with 50.
Comparisons & alternatives
Pick between the options.
- →Notion vs Airtable for AI Ops
Which one should run your AI workflow review queues and content calendar?
- →Airtable vs Notion vs Baserow
Relational data with different personalities.
- →Notion vs Obsidian vs Logseq
Cloud team wiki vs local plain-text knowledge base.