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​AI Data Analyst - Ada

Ada is a dedicated data analyst for businesses. She helps process complex corporate data to connect the dots, interpret data to understand the big picture, effectively analyze operational indicators, easily improve employee productivity, unlock data insights, and enable businesses to make timely decisions in rapidly changing markets.

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Meet Her

Ada functions like an in-house data analyst for businesses. Company employees can easily communicate with Ada through conversation, asking her to assist with any data-related issues. This allows them to quickly obtain valuable business insights, thereby improving organizational operational efficiency and grasping growth opportunities.

Ada's core is an LLM (Large Language Model) enhanced by TranX specifically for data analysis expertise. Ada speaks based on data, presenting true knowledge (not giving no data-based information). This stands out from most AI assistants on the market that handle general-purpose questions.

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Why Ada

Originally required weeks or days of manual data analysis​​

Ada can obtain analysis results in several ten seconds

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How Ada Works - Just talk to her about your questions

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Features

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1

Access Data Freely

Quickly access any data at any time without troubling engineers. (e.g., "What's today's shipping volume?")

2

Effortlessly Generate Analytical Charts or Reports

1. Instantly produce analytical charts to understand company operations. (e.g., "Create a pie chart of this month's electricity usage by area")

2. Assist in generating daily reports or for cross-departmental discussions. (e.g., "Please produce this month's operational report")

3

Rapidly Generate Analytical Insights

1. Perform correlation analysis to deeply examine the causes of operational issues. (e.g., "Cross-reference data to find correlations")

2. Execute year-over-year comparisons to analyze changes in key indicators. (e.g., "Compare sales figures for the first and second quarters")

3. Predict future operational performance and data trends based on existing data. (e.g., "Predict delivery dates based on current inventory and production capacity")

4. Adapt to understand industry-specific knowledge for analysis as needed. (e.g., "Analyze specific indicators using particular calculation formulas")

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