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Caicloud Cabernet
AI Solution

Details

Product Advantage

Ease of Use

Models and platforms can be delivered in one package and can be used out of the box.

Continuous Enhancement

Provides a variety of features to ensure that the model has continuous learning abilities to enhance model's ability.

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Multiple Models

Support time series prediction and models of image identification provide model customization service.

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Self-controllable

Provide model usage and production scenarios to ensure that companies have independent ability to use AI.

Product Features

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Image Classification

Invoice classification, natural scene classification and font classification.

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Image Identification

Physical detection, text recognition and behavior detection.

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Time Series Prediction

Sales prediction, supply chain prediction and financial prediction.

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Voice and Text

Speech recognition, dialogue robot and text classification.

Application Scenario

State Grid OCR Project
Container cloud architecture design service
Container cloud implementation service

Project Background

The process of bill submission, certification and verification in the internal bill reimbursement of State Grid Zhejiang Power has been manually operated.The overall efficiency is low. In order to promote accuracy and speed and to reduce the work pressure of the team, State Grid had an urgent need to adopt an intelligent bill approval system based on text recognition technology to verify the key information of bills automatically to provide strong support for intense financial management.

User Pain Points

• Challenges are many: huge amount of group corporate financial instruments, complexity of invoice types, which involves a variety of business routines such as engineering construction, power dispatching, employee reimbursement, and etc.

• General OCR technology lacks information structuring function and costs huge labor efforts.

Solution

• Version Analysis: Support multiple-types of bill analysis, automatically complete bill classification, reduce bill classification workload to improve overall reimbursement process efficiency.

• Key segment identification: Extracts and recognizes English and Chinese characters and numbers with high accuracy.

• Information structuring: Support structured processing of identified content, summarizing key information with no need of secondary processing.

• Deep learning: Model training is continuously conducted given massive data in business with strong anti-noise robustness to improve performance according to continuous feedback from real-time business.

• Container technology: Based on container technology with high portability, isolation and security for continuous integration and version control.

Project Background

In order to simplify and reduce the labor cost of complex work in Finance Dept, State Grid Zhejiang Electric Power Co., Ltd. needs financial forecasting system to calculate cash outflow of each branch in different financial categories by month to predict and optimize cash and finance plans.

User pain points

• Diversified financial categories, complex database and large proofreading workload; lack of unified or agreed standards for financial data statistics.

• Lacks approaches to formulate data indicators to form any types of early warning mechanism.

• Struggles to establish systematic financial process and update financial data in time.

Solution

• Time Series Forecasting Model: based on financial related business logic and existing financial data of State Grid Zhejiang Power, we customized time series forecasting model to output multi-category financial forecasting results.

• Agile Feedback and Iteration: place the predictive model in production environment and trigger the model as scheduled to gain results, which will in the meantime be sent back to the predictive model for continuous learning and performance optimization.

Project Background

As the leading fast food retailer in China, Yum! China operates more than 8,400 restaurants in China, covering more than 1,200 cities and towns. In order to improve distribution efficiency of chain stores and reduce storage cost, Yum! China needs advanced forecasting system to provide guidance for distribution and supply based on information of store supply and its history inventory in distribution center warehouses.

User pain points

• The historical data is complicated; input formats are diverse; data lacks unified specifications and some key data is missing.

• The business is complex, which involves multi-sector data within the enterprise, model demands are diverse; model results should have high reliability and data needs to be adjusted continuously to improve model accuracy.

Solution

• Data Cleaning: Customize the data (pre-processing) to provide standardized data for modeling. Time series prediction model: based on the greatest commonality of the needs of various departments of Yum! to establish as few models as possible to forecast supply and marketing.

• Model Output: automatic screening of high-deviation data (that part of data will be handled manually), and the results will be returned to the model for performance optimization.

• Container Technology: based on container technology, high portability, isolation and security for continuous integration and version control.

Contact Us Now

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