Our client, Ruthia, wanted to create a new kind of fan economy by creating a more direct connection between artists and their fans through the "Listen to earn" method. Through this format, even independent artists can build their own brands and fan circles.

### Subject

Singers already have many ways to distribute their work now, such as Youtube, Spotify, etc., but there is no way for singers to directly relate and interact in any way with the fans who listen to their music through these platforms.

Therefore, the main issue this time was to enable singers to take the initiative, to fully interact with their fans closely, to drive each and every one of them and make them a powerful spreading force, so as to achieve a strong fan economy.

### Solution

In order to facilitate fans to listen to the music of the singers, we connected Spotify and Youtube, respectively, with Renaissance, so that fans can listen to the music directly inside the app without having the singers re-upload the songs to our platform, reducing the burden of the singers and the burden of the early servers.

After that, in order to increase fan motivation, we created a point mechanism linked to the singer. Fans can earn points by listening to the singer's songs and by the act of sharing them, using the points fans can exchange them for relevant peripherals provided by the singer, and the singer can use this act to expand his fan circle and increase his popularity, creating a win-win situation.

### Results

After the App was launched, it quickly accumulated over 100,000 members in a short period of time, with a maximum online volume of over 10,000 users in the same period. The singer and the fans formed a strong bond through this platform, and the interaction generated between the two not only increased the singer's own visibility and influence around the fans, but also drove a strong economic benefit between the singer and the fans.

![Renaissance (1).jpg](/uploads/Renaissance_1_7f6f9e8569.jpg)


Renaissance - Listen to earn

Daikin Air Conditioning, a world-renowned air conditioning brand, has seen rapid business development in China in recent years, with sales already accounting for one-fifth of the world's total, making it one of its key overseas markets.

With the expansion of the market, in order to grasp the overall air conditioning operation more accurately, Daikin Air Conditioning (China) proposed the development of an air conditioning data analysis system "Smart Report". We started from requirement analysis, participated and completed the whole stage of design and development including UI design, product development, testing and launch.

### Project

Due to the rapid growth of our business in China, our customers in China are also growing rapidly. The sales staff, located in various provinces and cities in China, often need to request data about the use of various types of air conditioners from the technical staff in order to provide better after-sales service and maintain customer relationships. This not only increases the workload of the technicians, but also increases the efficiency of communication through constant confirmation and often leads to communication errors.

Moreover, since each salesperson is responsible for a different regional scope, a lot of work is done to ensure the accuracy and security of the data they have access to, but it also increases the threat to the security of the data itself.

### Solution

Since Daikin Air Conditioning already has a large number of air conditioners in use and generates a large amount of data every day, it was a potential core requirement of the system to quickly and effectively process this huge amount of big data quickly. Using the efficiency of Go language for big data processing, we developed a set of Daikin air conditioner data processing applets to make the data clean and usable within seconds, which became a powerful cornerstone of the whole system.

At the same time, we used different data charts for different data types, so that customers could understand the meaning of the data more intuitively.

In addition, in order to ensure the security of the data, we fully investigated the characteristics of the organizational structure of sales, and divided the smallest unit of data authority into individual objects, i.e., allowing sales to bind to objects, and sales can only check the objects associated with themselves, protecting the security and privacy of the data. At the same time, a set of corresponding management permission mechanism is designed to allow managers to flexibly manage the data permissions of sales under them.

In terms of development, we also fully considered the scalability of the system, leaving sufficient flexibility for future data improvement.

### Results

The system is now up and running within Daikin Air Conditioning (Shanghai), providing timely and accurate data services to thousands of sales staff across China every day.

![smartmockups_l0rvd3bi.jpg](/uploads/smartmockups_l0rvd3bi_0a14f6e524.jpg)
![smartmockups_l0rv1bcq.jpg](/uploads/smartmockups_l0rv1bcq_4dc37c0a00.jpg)


Daikin Air Conditioner Automatic Data Report

A town company (A Division for short) is a group of companies responsible for the overall energy management of a town, and is responsible for a number of business lines including water supply, drainage, gas, electricity, public transportation, parking, emergency protection, etc. It is an important unit for the livelihood of the citizens of the town.

In order to better grasp the status of each business line and ensure the life and safety of local people in a timely manner, Company A wanted to achieve accurate management of multiple business lines through digital and big data means. As the main technology provider, we were involved in the complete system development cycle, including design, development, testing and release, starting from the full communication with the client on the requirements.

### Subject

Division A has a team dedicated to overseeing various business lines, which needs to quickly determine various problems and situations that may arise by looking at different data every day and based on personal experience. This kind of work, not only consumes a lot of time and energy, but even for experienced veteran team members, there are inevitably cases of misjudgment and misjudgment. As the core brain of town management, it cannot play the role of quickly grasping the overall situation and accurately issuing the correct instructions.

### Solution

First, in order to allow the supervisory team to quickly grasp the real-time situation of various types of data, we developed a data monitoring platform for our client using data visualization. We used IoT to collect data from various channels such as water supply, drainage, gas, electricity, public transportation, parking, etc. After cleaning and processing the big data through Go, we provided targeted data graphs and charts for each type of data and management needs, so that the management team can quickly view and identify them.

At the same time, to quickly grasp the state of the whole town, we added a set of data monitoring system based on the basic map service, which can grasp the situation of each area more intuitively at the geographic level, and according to the source of data, it can even be accurate to a certain building.

Secondly, in order to deal with various emergency situations more accurately, we have developed an emergency warning management system by combining existing warning programs. The system forms a data model of the existing programs and incorporates the hotline work order system and execution units into the system at the same time, forming a set of closed-loop problem disposal from problem discovery, reporting, research and judgment to treatment.

### Results

After the system went live in December 2020, it made a huge difference in handling water supply/drainage disasters during the following year's severe cold weather, helping to handle thousands of cases per day. Since then, it has also been making a huge difference in the safe operation of various lines for the overall town.

![city4.jpg](/uploads/city4_e20247ef04.jpg)
![smartmockups_l0rweu7t.jpg](/uploads/smartmockups_l0rweu7t_9048444453.jpg)
![smartmockups_l0rwif87.jpg](/uploads/smartmockups_l0rwif87_cce25af989.jpg)

Digital Town (Smart City)

Our vision is to increase automation in running and managing a Shopify store, reduce the repetitive and time-consuming work involved, reduce the burden on store owners, and increase store revenue.

This is our first attempt to automate the tagging of orders in the Shopify App, not only to improve the efficiency of order processing, but also to unlock the potential value within the orders.

### Subject

When a store has a large number of orders coming in every day, how to handle different orders in a quick and corresponding way, such as sorting and packing, shipping in batches, etc., is the main task.

At the same time, each order has its own habits on how to handle orders. For example, some stores will give priority to orders with large amounts because they are important customers, and some stores will handle the same type of products in a uniform way.

There are also stores that want to be able to tap into customer value through the orders, in addition to processing the orders themselves.

### Solution

To be able to meet the different needs of our customers, we intend to make full use of the attribute of tags within Shopify orders. The store owner can use the tags to quickly find a specific type of order, and after that, whether it needs to be packaged for shipping or prioritized, it can be handled with ease.

One of the most central questions here is how to automate the process of tagging orders with the right labels?

To solve this problem, we use two approaches.

1. set rules (Workflow)

Rules are the conditions for automated tagging. We allow store owners to set the required conditions from multiple latitudes such as product, amount, shipping, customer, payment, etc. For example, you can set the rule to "order amount greater than 10000" and set the tag to "large order". In this way, all orders that meet this rule will be automatically tagged with "large orders", so that large customers will not easily miss them.

2. Rule Template

For the first time store owners, may not be familiar with how to make rules, for such users, we provide a rule template, that is, the common rules have been set up, you can directly use, convenient for new stores to quickly start.

By means of the above

### Effect

The product has been officially launched in January 2021, and there are already more than 20 default rules available for use, processing over a thousand orders per day.

### Techniques

Shopify App / Ruby on Rails / MySQL / React


![ordertagger-min.jpg](/uploads/ordertagger_min_50c6ebf194.jpg)