The article examines why a single data engineer can run an end‑to‑end data team, outlines the essential abilities—semantic ownership, building an agentic data stack, and leveraging historical context—while discussing ChatBI’s limits, validation loops, and the open‑source Datus 0.3 harness for practical implementation.
The paper introduces DIVER, an automated expert system that gives large language models human‑like exploration, reasoning, and verification abilities for Text‑to‑SQL, addressing the severe performance drop without expert evidence by innovating dynamic interactive value linking, multi‑agent automation, and adaptive evidence generation, and demonstrates up to 10.82% accuracy gains and strong robustness on real‑world benchmarks.
This article walks through turning a generic AI model into a personal assistant by explaining user‑centric workflows, crafting effective natural‑language prompts, adding clarification steps, validating AI‑generated results through multiple methods, and handling errors with product interactions to create a reliable, evolving assistant.
This case study examines a normative analysis framework that combines operations research, statistical learning, and large language models to automate end‑to‑end decision making in a ChatBI product, demonstrating up to 20‑fold efficiency gains, cost reductions, and enhanced interpretability across structured optimization and semi‑structured pricing scenarios.
This presentation by iQIYI’s Technical Director Zhang Xiaoming details the evolution of BI systems, introduces the Chat BI framework, explains its three‑step implementation, outlines architectural design, data‑warehouse integration, performance optimizations, and user‑operation strategies, revealing how AI and RAG empower smarter data analytics.
This article explores how the convergence of business intelligence and large‑model generative AI creates new business value, outlines the evolution of BI technology through three stages, and details Baidu’s ChatBI design, implementation challenges, and real‑world impact.
This article, excerpted from Baidu’s Data Platform technical salon, explores how generative AI reshapes Business Intelligence by outlining three perspectives: the technical trend and business value, the design principles of Baidu’s ChatBI platform, and practical challenges and solutions encountered during its deployment.
This article details Cainiao Group’s ChatBI platform, describing its business background, construction scenarios, AI‑driven logistics use cases, underlying data architecture, DSL‑based query engine, and governance mechanisms, while also sharing a brief Q&A with the product director.
This article explores how large language models break the natural‑language‑to‑SQL barrier, outlines the challenges of NLP‑driven data retrieval, compares Text2SQL and Text2DSL approaches, and proposes a unified data service and metric platform to power enterprise‑grade ChatBI solutions.
With the rapid evolution from Generative AI to Agentic AI, enterprises face both opportunities and challenges in building AI‑Native applications; this article examines the shift in AI development models, outlines key technical hurdles, and presents architectural and tooling strategies—including Chat BI, LangStudio, and multi‑modal data pipelines—to guide successful AI integration.
This guide walks you through creating a ChatBI intelligent agent by integrating Alibaba's Qwen3 large language model with PAI‑LangStudio, configuring the Model Context Protocol (MCP) server, and connecting to Hologres real‑time data warehouse, covering setup, deployment, and verification steps for enterprise data analysis.
This article details Didi's journey since early 2023 in building ChatBI, covering the evolution of BI platforms, the technical advances behind intelligent BI such as LLM‑driven NL2SQL, two main product paths, practical implementations, key challenges, and future directions for AI‑enhanced data analysis.
This article examines the rise of ChatBI in automotive companies, outlining current BI challenges, the five “no” and five “difficulties” issues, the motivations for adopting ChatBI, its evolving architecture, and practical implementation steps to achieve data‑driven decision making.
This article examines the current challenges of enterprise data analysis platforms, outlines three traditional analysis modes, and presents a next‑generation solution that combines Headless BI’s semantic modeling with Chat BI’s large‑language‑model interaction to deliver a more efficient, secure, and user‑friendly analytics experience.
The article explores how generative AI transforms business intelligence by detailing BI's evolution, the ChatBI platform's architecture, NL2SQL challenges, performance and accuracy optimizations, and real‑world deployment outcomes that demonstrate reduced user barriers and enhanced analytical efficiency.
This article examines the evolution of BI through generative AI, outlines the design and implementation of Baidu’s ChatBI platform, and discusses technical challenges such as NL2SQL integration, performance, accuracy, and user experience improvements that enable intelligent, low‑cost data analysis.
