Data Agent

Enterprise-Grade Business Analysis Agent

Your 'AI Business Partner' 'You say a sentence, I'll write the report' — bridging the 'last mile' from data assets to business decisions, enabling agile leap from fragmented queries to global business decisions.

Business Decision Engine

Business Decision Engine Integrating LLM & AI Agent

Data Agent deeply integrates Large Language Model (LLM) and AI Agent technologies. It is not just a query tool, but a 'digital employee' capable of autonomous planning, tool calling, and result delivery. Through natural language interaction, data sleeping in various databases can be transformed into professional research reports with deep insights in seconds.

AI Agent

Autonomous planning, reasoning, and task orchestration capabilities

Data Fusion

Connects 26+ data sources and heterogeneous data

Deep Insights

From Q&A to global business decision analysis

6 Core Elements

Empower All Roles with 'One Size Fits One' Business Insights

Data Agent provides customized intelligent analysis support for different decision levels in enterprises

Executive Decision

CEO/Strategy Dept

Real-time monitoring of global risk radar, strategic drill through 'dynamic DuPont analysis', tracking strategic goal achievement.

Financial Audit

CFO/Finance Director

Automatically perform consolidated statement variance analysis and profit calculation, predict funding gaps through real-time trial calculation, and control financial health.

Sales Management

Sales Director/Marketing

Quickly break down regional market share comparison, deeply understand marketing campaign ROI attribution, predict high-value customer renewal probability.

Production Supply

Factory Director/Supply Chain Manager

In-depth investigation of production yield fluctuation drivers, analysis of workshop energy consumption costs, prediction of raw material price fluctuation impact on gross margin.

Human Resources

HRBP/HR Director

Scientific evaluation of talent inventory data, deep analysis of the underlying logical relationship between employee turnover and department performance.

6 CORE ELEMENTS

Six Core Capabilities Build Competitive Barriers

Deeply integrate AI technology to build enterprise-level intelligent analysis platform

Interactive Logic Innovation

Let machines adapt to humans

Built-in industry corpus and exclusive tuning library, accurately understand enterprise 'jargon'. Users don't need to learn complex code or prompts, and can issue analysis instructions in daily spoken language.

Multi-format Reports in Seconds

Automated Delivery

One-sentence command triggers full-scenario output: logically rigorous Word research reports, beautiful PPT presentations, automatically calculated Excel spreadsheets, and dynamic interactive digital dashboards.

Multi-dimensional Attribution & DuPont Analysis

Analysis Nuclear Weapon

Integrate multi-level attribution analysis, intuitively present the root causes of business fluctuations. Dynamic DuPont analysis supports real-time adjustment of underlying variables, simulating the global chain reaction of 'indicator change → target impact'.

Deep Research

Strategic-level Deep Research

Capable of intelligent industry data retrieval and logical reasoning. Automatically collect and cross-analyze multi-source Internet data, output in-depth industry research briefs with rigorous argumentation chains and source citations.

Composite Architecture Anti-Hallucination

Completely eliminate data hallucination

'Knowledge Base + Database + Metric Base + Algorithm Base' anti-hallucination architecture, combined with large model and data separation design, ensures financial-grade accuracy of output results and eliminates AI misreporting.

Agent Cloning

High-efficiency Reuse

Support business experts to customize exclusive agents with 'zero code'. Analysis logic is built once and can be cloned and distributed to different departments for reuse, significantly reducing enterprise digital construction costs.

Technical Architecture

1+2 Full-Scenario Architecture Matrix

Three-layer technical architecture drives enterprise intelligent decision-making

Data Agent

1+2 Application Matrix

1+2 Application Matrix

1 basic coverage layer + 2 deep customization layers

General Report Agent

General analysis agent that meets the daily standardized and agile query needs of all employees.

Studio (Production Factory)

Zero-code visualization platform that injects industry knowledge and proprietary indicators into the model, completing development in hours.

Runtime (Execution End)

Encapsulated as an on-demand 'digital employee', supporting high-concurrency interactive analysis and scenario reload implementation.

Core Engine Layer

Core Engine Layer

Composite architecture anti-hallucination engine

Knowledge Base

Knowledge

Accumulate experience, drive cognition

Knowledge Driven

Data Base

data

Multi-source fusion, efficient supply

Data Unified

Metric Base

Metrics

Unified caliber, ensure decision-making

Metrics Aligned

Algorithm Base

Algorithm

Multi-modal driven, intelligent decision

Algo Driven

Foundation Layer

Foundation Layer

Multi-source data meta-tank collection
MySQL
Oracle
SQL Server
PostgreSQL
MongoDB
Excel
CSV
API
...26+
Case Studies

Industry Practice Cases

Value multiplication in practice

Banking · Retail Business Analysis Assistant

A Leading Commercial Bank · Agile Growth Attribution for Retail Business Line

Solutions for Leading Commercial Banks

Challenge Challenge
The retail business line has a huge amount of data. When conversion rates fluctuate, the business director needs to wait several days to get an attribution report, resulting in serious decision-making delays.
Application Applications
Deploy Rongzhi Data Agent to build a retail business analysis assistant. The director only needs to ask in natural language: 'Analyze the reasons for the decline in wealth management products in a certain district'.
Value Creation Value
The agent responds in seconds and calls algorithms to automatically break down the deep drivers of customer churn, and generates Word briefing with one click. Decision cycle reduced from days to minutes.

95%+

Efficiency Improvement

Seconds

Response Time

Minutes

Decision Cycle

Zero Threshold

Usage Difficulty

Comparative Analysis

Traditional Analysis Vs DataAgent

Compared with traditional data analysis models, efficiency and capabilities are comprehensively upgraded

Evaluation Dimension
Traditional Data Analysis
DataAgent Mode
Efficiency Improvement Rate
Report Generation Cycle3-5 working days, requiring multi-position coordinationSecond-level response, generated instantly with one sentence
>95%
Tool Usage ThresholdNeed to master SQL, Python or BI softwareZero threshold, supports spoken language and business jargon
Universal Adoption
Business Insight DepthStays at surface-level query and static displayDeep diagnosis, supports attribution and dynamic trial calculation
Logical Closure
Data AccuracyManual extraction prone to errors, inconsistent calibersMetric middle platform + anti-hallucination architecture, precise and unified
Financial-grade Accuracy
Result OutputSingle view, requires manual cross-platform formattingOne-click closed-loop output for Word/PPT/Dashboard
Automated Delivery
Quick Reference

Professional Answers · Eliminate Enterprise Worries

Q1
What are the advantages of Data Agent compared to traditional ChatBI?

Traditional ChatBI is mostly 'natural language to SQL', relies on general knowledge, prone to data hallucinations, and only outputs static charts. Data Agent adopts a composite architecture to ensure accuracy and can achieve closed-loop delivery from a single chart to global reports such as Word and PPT.

Q2
Can AI understand personalized indicators from business departments?

Yes. Data Agent supports inputting business 'jargon', proprietary indicator aliases, and enumeration values. You can even manually tune the model's understanding weight for specific terms. The 'Rule Base' and 'Workflow' modules ensure that when handling highly rigorous business, the agent will strictly follow established business rules, with full process transparency and secure control.

Q3
Is enterprise sensitive data safe during AI analysis?

Absolutely safe. The platform supports private deployment, and core data never leaves the enterprise. At the same time, it adopts a separation design between large models and data. AI is only responsible for task distribution and does not directly touch underlying sensitive data.

Start
AI-Driven New Era

Don't let inefficient report preparation slow down your strategic decisions! Let data truly become the engine driving enterprise growth, and let every insight of yours instantly transform into business value.

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