As pioneers in the uncharted territory of Data Infra in the AI era, DataCloud has launched its autonomous data analytics service—Powerdrill Enterprise (hereinafter referred to as Powerdrill). Leveraging cutting-edge large language models (LLMs), human-in-the-loop automation (HITL), and multi-agent frameworks, Powerdrill has garnered high-frequency usage and validation from over 15,000 organizations worldwide and more than 1.2 million professional data users. It has processed over 2 million files and 12 million data tasks, demonstrating its significant productivity advantages in deep application cases across industries such as retail, new energy, and high-end manufacturing. This has also attracted widespread attention from leading research and advisory company like Gartner.

Gartner analysts believe that Powerdrill is evolving into a "DataScience Agent," expanding its end-to-end platform capabilities from data warehouses to BI and data science, rather than simply natural language enhanced BI. This enables it to cover a broad user base ranging from pure business personnel to advanced analysts with data literacy.

Innovative Features: From Formal Business Scenarios to Personalized Insights

Designed for complex and challenging B2B data analysis scenarios, Powerdrill offers highly accurate conversational analysis, multi-agent collaboration, and explainable, intervenable analytical processes. Its core innovations include:

• Global Leading Analytical Accuracy: Powerdrill ranks first globally in the QuALITY benchmark test, which focuses on long-text input and question answering, assessing the model's ability to understand extended paragraphs and handle complex questions. Compared to ChatGPT, Powerdrill excels in deep long-file understanding, large-scale multi-file processing, intent recognition, and verifiability of results.

• Multi-Agent Collaboration and Customizable Metrics Platform: By leveraging intent recognition, DAG code execution engine, hybrid search, and data preprocessing (OCR, cleaning, charting, keyword extraction), Powerdrill builds a multi-agent data service framework. Users can define personalized variables and functions through natural language, breaking through traditional metric platform limitations and achieving a personalized data analysis experience.

• Traceable, Explainable, and Intervenable Insight Generation: Powerdrill breaks down the analytical process into controllable steps, generating verifiable code that allows users to directly intervene in the computational logic. The insight generation uses a candidate set mode, filtering key factors from vast amounts of data and providing optimization suggestions. It also recommends deeper-level questions based on problem relevance, helping users transition from descriptive to predictive and inferential insights.

• Decoupling of Data and Business: Through deep metadata mining, Powerdrill decouples data tables from business metrics. Even if table or column names change, the system can still accurately apply metric logic, significantly enhancing adaptability.

  

· Data+AI Integrated Architecture

Relyt AI-ready Data Cloud addresses challenges of scale, real-time performance, accuracy, and cost. It supports GB-level file processing and real-time data insights, providing flexible and efficient AI analysis application capabilities for enterprise customers.

Scene Revolution: Activating Frontline Business Momentum

Powerdrill has been widely adopted across industries such as retail, new energy, and high-end manufacturing. Compared to ChatBI and RAG solutions, which suffer from stability issues and semantic understanding deviations, Powerdrill demonstrates the following technological innovations:

• Smart Intent Recognition Engine: Automatically parses multiple data tables uploaded by users, matching business scenario needs through metadata association with industry-leading accuracy of 95%. For example, when a store manager in a city center inputs "next week's foot traffic forecast," the system automatically correlates historical cycle data, weather factors, and surrounding event information to generate a dynamic prediction model.

• Dynamic Semantic Layer Architecture: Innovatively decouples business metrics from data, supporting the definition of personalized operational metrics through natural language. For example, "prime-time space efficiency" = (sales from 17:00-19:00) ÷ (dining area × labor cost).

• Intervenable Code Generation: Breaks down complex tasks through multi-agent collaboration, generating explainable Python code for data computation. Users can adjust the computational logic in real-time. In practical tests, the accuracy for processing join queries involving three or more tables is 80% higher than traditional NL2SQL.

Powerdrill vs. ChatGPT: Focused on Serious Business Scenarios

General-purpose large language models are limited by their long-context processing capabilities and data interaction efficiency, making them unsuitable for serious business scenarios. While ChatGPT excels in general conversations, Powerdrill leverages core technologies such as deep metadata mining, intent recognition, and DAG code execution engines to address challenges like long-text understanding, large-scale multi-file processing, and result verifiability.

Powerdrill: Serving 15,000 Organizations Across Multiple Fields

Powerdrill serves over 15,000 organizations in government, education, retail, fintech, new energy, and other fields. More than 1.2 million professional data users benefit from its high-accuracy conversational analysis, traceable/explainable/intervenable analytical processes, personalized memory and data spaces, real-time data insights, and GB-level file support. These innovations help business data personnel and AI solution providers increase efficiency by 100 times, pushing autonomous data analysis into a new stage.

Powerdrill is not just a tool; it is a philosophy. We are committed to lowering the barrier to data analysis, empowering everyone to unleash the potential of data to drive decision-making and growth.