Monolith Task Navigator (MTN)

Inspired by the monolithic structures in 2001: A Space Odyssey and the alien intelligence of Hyperion's Shrike, MTN addresses the common problem of companies struggling to automate tasks buried within unstructured data. The story begins with companies drowning in emails, documents, and scattered data, unable to efficiently automate repetitive processes. MTN acts as the 'monolith,' surfacing and organizing these hidden tasks.

Concept: MTN uses a combination of techniques:

1. AI-Powered Task Discovery: Uses NLP and machine learning to analyze emails, document repositories (SharePoint, Google Drive), and chat logs to identify potential RPA candidates. It looks for patterns, keywords, and repeated phrases indicating recurring tasks (e.g., "generate weekly sales report," "process invoice requests"). It scrapes data from company websites similar to the 'AI Workflow for Companies' scraper project.
2. Smart Task Prioritization: Ranks potential tasks based on frequency, estimated time savings, and business impact (e.g., tasks that affect revenue are prioritized). User input is crucial here; users can manually prioritize tasks or adjust the system's prioritization rules.
3. Automated Workflow Creation (Low-Code): Once a task is identified, MTN automatically generates a basic RPA workflow using a drag-and-drop interface. The system can suggest actions based on the task's description and learned patterns from past automated tasks.
4. Adaptive Learning: MTN continuously learns from user interactions. When a user modifies a workflow, rejects a suggested task, or changes a prioritization rule, the system updates its models to improve future task discovery and workflow creation. This mimics the HAL 9000 concept of self-improvement, but with human oversight to prevent rogue behavior.

How it works:

- Data Ingestion: Connects to various data sources via APIs (Gmail, Outlook, SharePoint, Google Drive, Slack, etc.).
- NLP & ML Engine: Processes data to identify potential tasks, using pre-trained models and fine-tuning them on company-specific data.
- Workflow Engine: Allows users to create, modify, and run RPA workflows.
- Learning Module: Tracks user interactions and updates its models to improve future performance.

Niche: Focuses on automating tasks from unstructured data, a gap in many existing RPA solutions that prioritize structured data inputs.

Low-Cost: Can be built using open-source NLP libraries (NLTK, spaCy), low-code workflow platforms (e.g., n8n, Pipedream), and cloud-based machine learning services (AWS SageMaker, Google Cloud AI Platform).

High Earning Potential: The system can be sold as a SaaS subscription with tiered pricing based on the number of data sources, users, or automated tasks. Another route would be the sale of bespoke versions of the model, tailored to a given company's dataset.