CoSupport AI
What is Supametas.AI?
Supametas.AI is a powerful data platform offering code-free and low-code solutions for processing unstructured data. It enables enterprises to efficiently collect, construct, and preprocess industry-specific datasets from diverse sources, including APIs, local files, and web pages. The platform transforms this raw data into structured formats, specifically optimized for integration into Large Language Model (LLM) Retrieval-Augmented Generation (RAG) retrieval knowledge bases, significantly reducing processing time.
The tool supports a wide range of file types, such as documents (.docx, .pdf, .txt, .md) and media files (.jpg, .png, .mp3, .mp4), converting them into standardized JSON or Markdown. Supametas.AI intelligently extracts relevant information like paragraphs, titles, keywords, semantic meanings, tags, sentiment indicators, media timelines, and subtitles using natural language processing. Integration is facilitated through pre-built connections with OpenAI Storage and Dify Datasets, or custom integration into any knowledge base via its API.
Features
- Unstructured Data Processing: Handles diverse data types including documents, media files, and web data.
- LLM RAG Integration: Structures data specifically for seamless integration into LLM RAG knowledge bases.
- Low-Code/Code-Free Interface: Simplifies dataset creation and management for enterprise users.
- Comprehensive Data Collection: Extracts data from APIs, local files, and performs web scraping with automated field extraction.
- Format Conversion: Converts various input formats into standardized JSON or Markdown.
- Intelligent Content Extraction: Uses NLP to extract specific elements like titles, keywords, tags, sentiment, timelines, and subtitles.
- API Access: Provides API endpoints for data extraction and file processing integration.
- Automated Web Scraping: Handles complex web pages, list pages, pagination, and scheduled updates.
- Universal File Format Support: Processes .docx, .pdf, .txt, .md, .jpg, .png, .mp3, .mp4, and more.
- Built-in & External AI Model Support: Utilizes AI for processing, allowing users to use built-in tokens or connect their own models (e.g., OpenAI).
Use Cases
- Building knowledge bases for LLM applications.
- Automating data extraction from websites for market research.
- Processing diverse internal documents for enterprise search.
- Converting multimedia content (audio/video) into searchable text data.
- Structuring financial reports or legal documents for analysis.
- Preprocessing educational materials for AI tutors.
- Creating datasets for training custom AI models.
- Integrating real-time web data into business intelligence dashboards.
FAQs
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What are built-in AI models and external AI models?
AI models handle hard-to-structure data. The system integrates optimized built-in models (consuming provided tokens) and allows users to connect external AI providers (like OpenAI) when built-in tokens are exhausted or preferred. -
How is dataset capacity calculated?
Capacity is based on the total size of uploaded data, processed data, and exported data stored long-term. Deleting tasks and data frees up the occupied capacity. -
How is data privacy ensured?
Original data is deleted shortly after task deletion, pause (3 days), completion (3 days), or failure (3 days). The platform adheres to privacy standards. A private deployment option is planned for enhanced privacy needs. -
How can I integrate Supametas.AI with my existing project?
Integration into knowledge bases or direct calls is possible via API. Register an account, create a dataset, generate an API Key, and then follow the documentation for integration instructions. -
How are Built-in and External AI Model Tokens Consumed?
Data is converted into tokens (with conversion efficiency similar to OpenAI) for interaction with the AI model. Token consumption covers data input, model interaction, and data output. The system uses algorithm optimization to reduce token consumption, which can be monitored in real-time during import tasks.
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Supametas.AI Uptime Monitor
Average Uptime
94.65%
Average Response Time
522.3 ms
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