AI Data Aggregator & Processor
Budget: ₹1,500 – ₹12,500 INR
1. Project Overview
The AI Agent will be designed to automatically pull data from multiple sources, including different projects, documents, cloud storage services (Dropbox, S3), URLs, and media content (YouTube videos). The agent will process, clean, and store the extracted data for further analysis, search, and retrieval.
2. Objectives
Develop an AI-driven system to aggregate data from various sources.
Enable automated extraction, transformation, and loading (ETL) processes.
Ensure seamless integration with cloud storage, document repositories, and media platforms.
Implement Natural Language Processing (NLP) for document comprehension and YouTube transcription.
Provide an API interface for querying and accessing the processed data.
Support integration with various Large Language Models (LLMs), allowing flexibility in model selection.
3. Data Sources & Integration
3.1 Supported Data Sources
Documents: PDF, Word, Excel, Google Docs
Cloud Storage: Dropbox, Amazon S3, Google Drive
URLs: Web pages, APIs, public datasets
YouTube Videos: Speech-to-text transcription & metadata extraction
Projects & Repositories: GitHub, Jira, Trello
Databases: SQL, NoSQL (MongoDB, Firebase)
3.2 Data Collection Methods
APIs: Dropbox API, AWS S3 SDK, YouTube API, Web Scraping
OAuth Authentication: Secure access to third-party services
Webhook Triggers: Auto-detection of new data
Manual Uploads: User-initiated document ingestion
The AI Agent will be designed to automatically pull data from multiple sources, including different projects, documents, cloud storage services (Dropbox, S3), URLs, and media content (YouTube videos). The agent will process, clean, and store the extracted data for further analysis, search, and retrieval.
2. Objectives
Develop an AI-driven system to aggregate data from various sources.
Enable automated extraction, transformation, and loading (ETL) processes.
Ensure seamless integration with cloud storage, document repositories, and media platforms.
Implement Natural Language Processing (NLP) for document comprehension and YouTube transcription.
Provide an API interface for querying and accessing the processed data.
Support integration with various Large Language Models (LLMs), allowing flexibility in model selection.
3. Data Sources & Integration
3.1 Supported Data Sources
Documents: PDF, Word, Excel, Google Docs
Cloud Storage: Dropbox, Amazon S3, Google Drive
URLs: Web pages, APIs, public datasets
YouTube Videos: Speech-to-text transcription & metadata extraction
Projects & Repositories: GitHub, Jira, Trello
Databases: SQL, NoSQL (MongoDB, Firebase)
3.2 Data Collection Methods
APIs: Dropbox API, AWS S3 SDK, YouTube API, Web Scraping
OAuth Authentication: Secure access to third-party services
Webhook Triggers: Auto-detection of new data
Manual Uploads: User-initiated document ingestion