What this automation does
Requests buried in email threads turn into tracked tasks with the right owner and date, and the email stays one click away from the task.
- Watch. The workflow monitors a shared mailbox, a forwarding address or a label such as “To task”. New messages and replies are both picked up.
- Strip. Quoted history, signatures and legal footers are removed so the model sees only what the sender wrote in this message.
- Classify. A model labels the message as a new request, an update to an existing request, informational, an automated notification or something else. Only requests and updates continue.
- Extract. For requests, the model returns a verb-first task title, a two-sentence description, requester, due date (resolved against the email’s sent time), priority, a suggested project from the sender’s domain, and a suggested assignee.
- Deduplicate. The thread ID is checked against a small table. If a task already exists for the thread, the new message is added as a comment, and any changed due date is proposed to the task owner.
- Create. The task is created with a link to the original email and any attachments, and the thread is tagged so the team can see it has been captured.
- Close the loop (optional). The requester gets a short acknowledgment, and when the task is completed the workflow can notify them.
When it is worth building
This fits teams where clients and colleagues send requests by email and work slips because nobody turned the email into a task. Agencies, consultancies, property managers and construction project teams are common examples. A shared inbox where three people might act on the same email, and sometimes all three do or none does, is a strong sign.
It is not the right tool for customer support queues. If emails are support tickets, use a help desk and AI ticket triage instead. For a general inbox that needs sorting and routing rather than task creation, see our shared inbox triage guide. And if your volume is low and requests are clear, forwarding to your task tool’s email address costs nothing.
Architecture
| Layer | What we use | Why |
|---|---|---|
| Trigger | Gmail or Outlook nodes in n8n watching a label or folder | Scope stays narrow and easy to explain to staff |
| Pre-processing | Code that removes quoted text, signatures and footers | Less noise means fewer false tasks and lower model cost |
| Classification and extraction | OpenAI’s GPT models or Claude through the API with structured output | Reliable JSON with a fixed set of labels and fields |
| Routing data | A table of sender domains to projects and a people directory | Projects and owners resolve to real IDs |
| Thread state | A small table mapping thread ID to task ID | Replies update the existing task |
| Task tool | Asana, ClickUp, Jira or monday.com | Work lands in the board people already use |
| Review lane | A “Needs review” section in the task tool | Uncertain items are visible without polluting the main board |
Our Asana notes cover custom fields and the API details for linking tasks back to emails.
How to build it
- Define what a task is. Pull 100 to 200 real emails from the target inbox and label them by hand: request, update, informational, automated. This test set drives every later decision.
- Build the pre-processor. Strip quoted replies and signatures, and keep attachments’ names and types for context.
- Build and score the classifier. Run the test set and measure how many created tasks are real (precision) and how many real requests were caught (recall). Favor precision at first, because a board full of junk gets ignored.
- Write the extraction schema. Include a confidence flag for due date and assignee, so vague cases are marked rather than guessed.
- Set up routing tables. Map sender domains to clients and projects, and email addresses to task tool user IDs.
- Add thread state and deduplication. Store thread ID and task ID together, and treat replies as comments or proposed updates.
- Run with a review lane for two weeks. All auto-created tasks go to “Needs review” first. Promote to direct creation once precision is consistently high.
- Capture feedback. Deleted or merged auto-tasks are logged and added to the test set.
The downloadable starter template below gives you the Gmail trigger, classification, extraction and task creation steps to adapt.
What breaks in production
- Reply-all chains. In a long thread, every “thanks” or “+1” looks like a new message. Classification plus thread state keeps these from becoming tasks.
- Old requests in quoted text. Without stripping, a reply can resurrect a request from three weeks ago. Pre-processing is not optional.
- Notification loops. Your task tool emails people when a task is created. If those notifications reach the watched inbox, the workflow can create tasks about tasks. Exclude your own tools’ sender addresses from day one.
- Ambiguous dates. “EOD Friday” sent on a Friday afternoon from another time zone can resolve to the wrong day. Resolve in the sender’s zone and show the source phrase in the task description.
- Wrong assignee. First names collide and people go on leave. When confidence is low, assign to the project lead rather than guessing.
- Sensitive content. HR, legal and payroll emails should never land on a shared board. Exclude sender lists and labels explicitly.
Timeline and cost
One inbox feeding one task tool typically takes one to three weeks, including the review period. Expect a Lumeor build to cost $3,000 to $9,000. The range depends on the number of inboxes, how many projects and clients need routing rules, and whether completion notifications back to requesters are included. Model costs per email are small because pre-processing keeps inputs short.
How to measure success
- Precision, measured as the share of auto-created tasks that are kept
- Missed requests found in a monthly sample audit of the inbox
- Minutes from email received to task created
- Requests with no reply or owner after 48 hours
- Duplicate tasks per week
- Time staff spend manually copying emails into the task tool