KnowData
AI-powered knowledge assistant for intelligent email management, database querying, and social media analysis
Created on 14th March 2025
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KnowData
AI-powered knowledge assistant for intelligent email management, database querying, and social media analysis
The problem KnowData solves
KnowWiz is a comprehensive AI assistant that solves multiple problems:
Email Management: Automates email drafting, scheduling, and sending based on natural language requests, saving time and reducing the cognitive load of email composition.
Intelligent Database Querying: Transforms natural language questions into complex database queries, making data retrieval accessible to non-technical users without SQL knowledge.
Social Media Analysis: Aggregates and analyzes content from multiple platforms (Mastodon, GitHub, Internet Archive) to provide insights about companies or topics.
Knowledge Integration: Combines information from multiple sources to provide comprehensive answers and insights, acting as a unified interface for various data sources.
The application uses Google's Gemini AI to understand user intent and generate appropriate responses, whether that's drafting an email, querying a database, or analyzing social media sentiment.
Challenges we ran into
AI Integration Complexity: Integrating Google's Gemini AI required careful prompt engineering to ensure accurate interpretation of user intent and generation of appropriate responses. We solved this by developing specialized prompts for different functionalities.
Database Schema Analysis: Creating a system that could dynamically understand and query any Firestore database structure was challenging. We implemented a database indexing system that analyzes and stores metadata about collections, fields, and relationships.
Natural Language to Query Translation: Converting natural language questions into structured database queries required sophisticated analysis. We developed a multi-step process that identifies required collections, fields, joins, and filters.
Email Scheduling System: Implementing a reliable email scheduling system required careful thread management and datetime parsing. We created a robust scheduler that handles various time formats and maintains a queue of pending emails.
Cross-Platform Data Integration: Aggregating data from multiple sources (Mastodon, GitHub, Internet Archive) with different APIs and data formats required extensive normalization and preprocessing