CoSupport AI
What is Delv AI?
Delv AI streamlines research and development tasks by employing compliant artificial intelligence to interact effectively with text data. It allows users to instantly query, summarize, and extract valuable insights from diverse sources, including cloud drives and open-source datasets, aiming to reduce data processing time significantly, potentially by up to 75%.
The platform incorporates advanced features such as automatic clustering of databases into distinct topics and subtopics, visualized through AI-generated pie charts, treemaps, and heatmaps. It facilitates obtaining consensus answers by querying multiple documents concurrently via a custom GPT model, which refines its responses through continued usage (RLHF). Furthermore, users can enhance answer relevance by adding specific context, all while maintaining full data privacy compliance.
The platform incorporates advanced features such as automatic clustering of databases into distinct topics and subtopics, visualized through AI-generated pie charts, treemaps, and heatmaps. It facilitates obtaining consensus answers by querying multiple documents concurrently via a custom GPT model, which refines its responses through continued usage (RLHF). Furthermore, users can enhance answer relevance by adding specific context, all while maintaining full data privacy compliance.
Features
- Compliant AI Search: Query, summarize, and extract insights from text.
- Data Clustering: Automatically groups data into topics and subtopics with visualizations (Pie-Charts, Treemaps, Heatmaps).
- Multi-Document Q&A: Obtains consensus answers from multiple documents using a custom GPT model.
- Adaptive Learning (RLHF): Improves answer quality based on user interaction.
- Custom Context Input: Allows adding extra context for more precise results.
- Data Connectivity: Integrates with cloud drives and open-source data.
- Data Privacy Compliance: Ensures secure and compliant data handling.
- Export Functionality: Save and export generated results and insights.
Use Cases
- Accelerating Research & Development cycles.
- Analyzing large volumes of text documents efficiently.
- Extracting key insights from multiple sources simultaneously.
- Topic modeling and thematic analysis of databases.
- Streamlining information discovery in regulated industries.
- Improving search efficiency for research teams.
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