Email AI Agent Bounty

1. Product Overview Objective: The AI Email Agent automates email data processing by reading emails, extracting relevant data from attachments and email bodies, cleaning the data, and storing it in a structured database. This eliminates manual effort, reduces errors, and ensures timely data processing. Target Audience: Organizations handling high volumes of emails (e.g., invoices, orders, customer inquiries). Businesses needing automated data extraction and ingestion for operational efficiency. Teams looking to integrate email data into their existing workflows and systems. Key Goals: Automate the extraction of structured and unstructured data from emails and attachments. Perform data cleansing to ensure accuracy and consistency. Seamlessly ingest the processed data into a database or other systems.
2. Features and Functionality 2.1 Email Reading and Parsing

Description: The agent will connect to email servers to read and process incoming emails. Key Features: Integration with email protocols (IMAP, POP3, SMTP). Support for multiple email providers (Gmail, Outlook, etc.). Real-time or scheduled email processing. 2.2 Data Extraction

Description: Extract relevant information from email bodies and attachments. Key Features: Extraction of structured data (e.g., tables, forms) and unstructured data (e.g., plain text). Support for common attachment formats (PDF, Excel, Word, CSV). Use of Natural Language Processing (NLP) for text extraction and context understanding. 2.3 Data Cleansing

Description: Clean and standardize extracted data for accuracy and consistency. Key Features: Remove duplicates, correct formatting errors, and handle missing data. Validation against predefined rules (e.g., email addresses, phone numbers, dates). Flag and report anomalies or inconsistencies. 2.4 Data Ingestion

Description: Store processed data in a structured database or other target systems. Key Features: Integration with relational (MySQL, PostgreSQL) and NoSQL databases (MongoDB). Support for APIs to push data into third-party systems (e.g., CRMs, ERPs). Batch and real-time ingestion modes. 2.5 Configurable Rules and Templates

Description: Allow users to define rules and templates for data extraction and processing. Key Features: Rule-based extraction (e.g., “extract invoice number from subject line”). Customizable templates for specific email formats or attachment types. Ability to save and reuse rules/templates for recurring tasks. 2.6 Monitoring and Reporting

Description: Provide insights into email processing activities and data ingestion. Key Features: Dashboard showing processed emails, extracted data, and ingestion status. Logs for errors, anomalies, and failed extractions. Exportable reports for auditing and compliance. 3. User Interface and Experience (UI/UX) User Flow: Connect email account to the agent. Define extraction rules and templates (optional). Agent processes incoming emails and attachments. View extracted data and ingestion status on the dashboard. Monitor errors or anomalies and adjust rules as needed. Design: Intuitive, minimalistic interface with a focus on usability. Interactive dashboards with real-time updates. Drag-and-drop functionality for rule and template creation. 4. Technical Requirements Platform: Web-based application with optional desktop client for advanced configurations. Integrations: Email Providers: Gmail, Outlook, Yahoo Mail, custom mail servers. Databases: MySQL, PostgreSQL, MongoDB, AWS RDS, and Google BigQuery. APIs: Integration with tools like Salesforce, HubSpot, or custom APIs. AI/ML Models: NLP for extracting context-sensitive data. Optical Character Recognition (OCR) for reading scanned attachments. Machine learning for anomaly detection and rule optimization. Data Storage: Secure cloud-based storage for temporary data processing. GDPR and CCPA compliance for data privacy. Performance: Capable of processing 100+ emails per minute. Scalable to handle high email volumes during peak periods. 5. Timeline and Milestones Phase 1: Develop email reading and parsing module (4 weeks). Build basic data extraction for email body and attachments (6 weeks). Phase 2: Implement data cleansing and validation features (6 weeks). Develop database ingestion and API integration (4 weeks). Phase 3: Add configurable rules and templates (4 weeks). Build monitoring dashboard and reporting features (4 weeks). Phase 4: Conduct beta testing with select users (4 weeks). Launch full product (2 weeks). 6. Metrics and KPIs Processing Speed: Time taken to process an email and extract data. Accuracy: Percentage of correctly extracted and ingested data. Error Rate: Number of failed extractions or ingestion errors per 100 emails. User Satisfaction: Feedback score from users on ease of use and reliability. 7. Constraints and Assumptions Budget: Development and deployment costs should remain within $X. Technology Limitations: Accuracy of data extraction depends on the quality of email content and attachment formatting. User Expertise: Assumes users can define basic rules and templates for custom use cases. 8. Risks and Mitigations Risk: Privacy concerns over email data processing. Mitigation: Implement robust encryption and adhere to data protection laws. Risk: Inconsistent email and attachment formats. Mitigation: Use AI models that adapt to diverse formats and user-defined templates. Risk: High error rates during initial deployment. Mitigation: Conduct extensive testing and provide user feedback mechanisms for improvement."