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Compliant AI

Query, Summarize & Extract Unlimited Insights from Any Text Instantly.

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Description

Compliant AI is designed to enhance research and development processes by significantly reducing the time spent on data processing. It enables users to interact with any text data through querying, summarization, and insight extraction. The platform can connect to various data sources, including cloud drives and open-source data, and automatically cluster information into relevant topics and subtopics, visualized through AI-generated pie charts, treemaps, and heatmaps.

Leveraging a custom GPT model via the OpenAI Assistant API, Compliant AI provides consensus answers from questions asked across multiple documents, with performance improving over time through Reinforcement Learning from Human Feedback (RLHF). Users can add extra context for more tailored answers and save or export their findings. The tool emphasizes fully compliant data privacy, ensuring secure handling of information throughout the workflow.

Key Features

  • Database Clustering: Cluster database content into topics & subtopics.
  • Data Connectivity: Connect to Cloud Drive or any Open-Source Data.
  • AI Visualizations: Generate Pie-Charts, Treemaps, and Heatmaps automatically.
  • Multi-Doc Q&A: Get consensus answers by querying multiple documents simultaneously.
  • Custom GPT Model: Utilizes the OpenAI Assistant API for tailored interactions.
  • Reinforcement Learning (RLHF): Answers improve with continued usage.
  • Custom Context Input: Add supplementary context for enhanced answer accuracy.
  • Save & Export Results: Store and share findings easily.
  • Data Privacy Compliance: Ensures full compliance with data privacy standards.

Use Cases

  • Optimizing Research & Development (R&D) workflows.
  • Accelerating text data processing and analysis.
  • Extracting key insights from large volumes of documents.
  • Conducting comprehensive reviews across multiple sources.
  • Facilitating AI-powered research assistance for teams.
  • Visualizing complex data relationships and topic distributions.

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