UPP Global Technology JSC

Overview:

Client running businesses in the furniture field would like to convert their existing system from paper to cloud and develop a sales management system for their businesses in Viet Nam. In this project, we carried out the following tasks:

-Collecting information and expertise from all relevant departments.

-Gathering reports according to the requirements of relevant department heads.

-Collecting working habits and methods of relevant departments.

-Consulting solutions to convert from Excel to paper form by cloud.

-Implementing a Sales & Services system.

-Integrating available on-premium systems (Accounting, HR, etc).

Project's challenges and our solutions:
1. Every department has its own business, using its own excel format. Changing user habits from separate to common among departments becomes increasingly complex.

=> Divide implementation milestones, deploy each part to help employees adapt to each part, collect comments and suggestions to draw experience for the next deployment.

2. High operating costs

=> Use the solution to minimize the number of licenses needed

Outcome:

-The departments have access to the system in a short time.

-60% reduction in operating costs

Techstack:
Sales Cloud, Services Cloud, Salesforce Platforms ( Apex, Aura component, Salesforce process ... )

Overview:

The water flow warning system is developed with the following features:

1) To predict the water inflow of a wastewater treatment plant (Matlab/AI forecasting)

2) To set alarms and notification functions related to various system and sensor data, etc.

3) To collect, store, edit, and display on the web different data from sensor devices

4) To provide real-time sensor data acquisition and screen display

5) To notify the system admin if there is an abnormal detected in the sensor data.

Project's challenges:
Monitoring of water levels in the plant's reservoirs is done manually. This leads to slow response when situations occur. The customer has many treatment pools in different regions. It is desirable to make a highly accurate prediction of the water level of each lake.

Our solutions:
We propose to build a system running on Azure, allowing us to receive data from pre-installed sensors in lakes, and then combine it with the weather forecast in the corresponding location to predict the water level of the lakes. The data relating to the sensors is also stored so that training can be accomplished to improve the predictive model.

Outcome:
The system allows customers to predict possible situations in advance to make appropriate response plans. The prediction model is improved to give the best accuracy.

Techstack:

Azure, ReactJS, Spring Boot, Java, Highcharts

Overview:

To apply the corporate governance system of UPP Global Technology by building an enclosed system across Sales, Project Management, Human Resources, recruitment and Asset Management through which we can optimize license usage.

Project's challenges:

Salesforce is not ERP.

Our solutions:

For each small ERP system (Asset Management, Project Management, HR ....), Upp has brought the main objects of each of these systems such as asset, project, and human, etc to become customers in CRM. Take advantage of Salesforce's existing processes and flows as well as based on the specific needs of each system to create custom.

Outcome:

The system is built to meet the specific needs of each individual system.

Techstack:

Sales Cloud, Services Cloud, Salesforce Platforms ( Apex, Aura component, Salesforce process ... )

Overview:

As a credit company, they give thousands of approval/refuse to customers' applications. Clients want to analyze their decisions on which factor is the most important in the decision making. They also want to analyze the differences between years and prediction the change after each year.

Project's challenges:

Clients require a clear and detailed explanation of our output (generated by AI).

Our solutions:

We apply ExplanableAI so that the model could give some explanation for their reasoning. Furthermore, we analyze hundreds of the critical (represenatative and outliner) data points (customer profile). So that client is confident with our solution.

Techstack: PyTorch, MLOps (Kubeflow or Dataiku), Tableau

Overview:

After the consultation process, the next step is implementing and operating the data system. Here, we must ensure good system operation, high-quality data, and absolute security.

Project's challenges:

During the construction and operation we encountered the following problems:

1. We do not have in-depth knowledge of the technology used. Accompanied by big data problems, the processing performance (Ingest Data, ETL Data) has not been optimized in terms of processing time. Chaining the system again costs in terms of time, increasing a lot of data that is not updated in time, which will affect the following problems.

2. How to organize the design of pipelines, the accompanying components are overlapped, and the technology is not optimal. Increases the cost of system operating infrastructure.

3. Capturing business logic is not clear which leads to low-quality data output, incorrect information, more seriously not as required. Influence subsequent decisions or problems.

Solutions:

Encrypt sensitive data, and clearly decentralize the right people, the right authorities, responsibilities, and indirect access. Use technology proficiently, design clearly organize related component structures to avoid repetition. Optimized data query, and structure. There is a system to monitor and fix unexpected problems during data operation.

Techstack: Cloud, Programming Language, Database, Bigdata Frameworks, ETL Orchestration, Data Warehouse

Overview:

In this game, our responsibilities are as below:

- To participate in gameplay sketching and brainstorming process.
- To implement TBM token (ERC20) which is used for purchasing items in the game.
- To implement TBR token (ERC20) - a reward token that helps prevent game economy inflation.
- To develop game characters and items by using ERC1155 and ERC721.
- To build an upgradeable marketplace contract to help users trade game items.
- To implement a server API by using Moralis SDK.

Challenges:
-Inflation of the economy in the game
-High gas fee, slow game speed due to the influence of processing speed of blockchain network
-High gas fee when listing products as well as slow speed when querying and filtering products sold in the marketplace and transaction history

Solutions:
+ Issue 1 sub-token (TBR) for payout
+ Build mechanisms to burn both sub-token (TBR), main-token (TBM) and NFTs
+ Build a staking mechanism to prevent players from selling off user tokens
+ Save the metadata in the backend when only basic information is stored on the blockchain
+ Updated result information of each game is provided to the blockchain after the game is over
+ Save the metadata in the backend when only basic information is stored on the blockchain
+ Build lazy minting solution to transfer gas fee to user
+ Build event sourcing system to record and save transactions in the backend, which can help query and filter products in the marketplace and transaction history

Outcome:
-Reduced gas fee as well as increased speed and gaming experience that increases performance by more than 75%
-Reduced paid gas fee paid which boosts performance by query and filter by 80%
-The departments have access to the system in a short time.

Techstack: Solidity, Hardhat, Truffle, Moralis SDK, EtherJS, etc.