TaskMiner automatically extracts tasks from your email to reduce the amount of time you need to spend organizing your tasks.
TaskMiner's RAG-driven extraction process is fully automated, without the need for manual prompting. Generated tasks are compiled in a simple dashboard for users to review and mark as completed. Through this simple workflow, TaskMiner’s mission is to provide a task tracking solution for professionals who live in email, but do not have a systemized process for handling incoming requests.
Smart Task Detection: Identifies actionable items requiring your attention
Context-Aware Extraction: Uses email history for better task understanding
Web Dashboard: User-friendly interface for reviewing and managing extracted tasks
Batch Processing: Upload and process multiple emails at once
TaskMiner addresses a persistent challenge faced by professionals who primarily rely on email to manage communication and workflow, but do not use formal ticketing or task management systems.
In many organizations, especially those with smaller teams, consulting firms/groups, or service-oriented roles, important tasks are assigned and discussed directly through email—often informally. These action items are scattered across threads, hidden within conversations, and rarely transferred into structured tools like calendars or to-do lists. As a result, critical follow-ups are missed, deadlines are forgotten, and professionals are left to manually track tasks with no support.
TaskMiner will enable working professionals to focus solely on the tasks at hand without having to worry about missing deadlines or searching for additional information through piles of emails.
Email batches are ingested through a website portal and uploaded to the user’s folder in an S3 bucket.
Uploaded emails are parsed and stored into an Emails table on a DynamoDB database. These parsed emails are then stored in a Pinecone vector store along with email metadata.
For each email, an LLM is queried to identify whether it contains a task. For each email with a task, the vector store is queried to retrieve related emails that supplement context for that task.
If the original email or queried context indicates the task is already complete or duplicative of another task, it is marked as such and TaskMiner continues to the next email.
If the task is new, using the queried context, the task is summarized by the LLM, including task metadata such as requestor, due date, and links to related emails.
Tasks are stored in a Tasks table, through which they can be displayed on a Streamlit-based front-end (i.e., the website) for the user to review.
Approved emails are tracked using the Task Manager portal on the website, where the user can mark them as complete.
Ensure no task is ever missed and free users from the mental burden of managing their inbox, so they can focus on the work that truly matters!