an AR empowered service for a traditional business


As technological innovations advance at an unprecedented pace, many traditional services are lagging
behind in terms of utilizing these features. Therefore, our team looks to refine an organizational offering
by introducing service elements to its products and integrating it with AR functions that are accessible
from most modern smart phones.

project origin:

FU comes from the two Chinese characters 赋/服, representing empowerment and service. This echos
our motiviation in empowering a traditional business with AR powered service. 


We were assigned with Markel Food Group, a multi-national publically traded company that provides
businesses with mass food production solutions. Our job is to shift the company towards today’s ever
growing tech dominated and service-oriented business scene. Instead of focusing on a product, we
will work on a hybrid product/service app with native cellphone functionalities.


The on-going challenge for us in the whole process was to determine the type of service that we
choose to offer. Since there could be multiple choices for the same product, ranging from purchasing
to maintenance, we were well aware of the effect of a lack of focus. Although this process took much
time, our design process was largely done without too much conflict and led to a postive outcome.

initial research:

We investigated Markel’s business model during our domain research and discovered that it consists
of 3 independent companies that shares their resources, such as technology and local outreach. This
insight was furthered by mapping our essential stakeholders, as demonstrated on the right. This step
is critical for our design, since it clearly indicates that the opinions our direct customers, the food
factories, are our primary concern.



voice & tone:

We conducted individual assessments of potential voices and tones that our company should adopt
before conducting a discussion to determine the most appropriate types that our company should
have. We decided that our product should remain formal, informative, and empathetic while avoiding
authoritative, dry, or inconsiderate elements because our customers(food factories) expect the
company to provide quality and professional services. Our Design Brief captures the initial intention
of our product: to help clients expand their businesses and stay competitive by offering a specialized
product selection feature. Although this somehow differs from our final product, it granted us
invaluable insights into the stage 1 of our design.


During ideation, we first identified that there are two types of user in current Markel Food Group
business model: the existing customers and the potential customers. We realized depend on the
user type, different service can be offered. The new customer will be primarily engaged in pre-
purchase experience, which is browsing for the new machines. We envisioned that the location-based
(GPS) recommendation consulting can be provided for them. Specifically, the GPS will get access to
the user’s location and provides information about their local area such as market trends and
recommends the suitable machines. On the other hand, the existing customer can be involved with
managing their existing machines, self assemble their machines, or browsing new machines. We
envisioned that AR can be integrated to self-check the machine by identifying the machine parts and
providing self-check guides. In addition, 3D interactive self assembly guide can potentially help them
to self-assemble the machines once they purchase. 


low-fi prototype:

As explained in the ideation process, we generated three distinct ideas focused on pre-purchase and
post-purchase experiences. Our general user flow of the app is that once the user logs in, it asks
whether the user wants to purchase, assemble their newly purchased machine or self-check the
status of their machines. Depend on the service chosen, the user can go through each process.

pivot & reflection:

During in-class critique, we received feedback that among the three ideas, AR machine maintenance
service that provides self-checkup seems to have the strongest value to further develop. Therefore,
we decided to focus on AR machine maintenance service in our next iteration process. We also
decided to keep the location-based recommendation idea (using GPS) as a subfeature of this app,
which allows the current user to still explore new machines and expand their business. As we decided
to focus on AR machine maintenance features, we were able to clearly identify who our target audience
is. Since AR machine maintenance is the post-purchase service, we aimed to think from the current user’s
perspective to improve their post-purchase experience and therefore, were able to develop further ideas
such as communication method with technician and sending reports for the next iteration.


mid-fi prototype:

According to previous feedback, our mid fi focued on creating a more detailed experience for the AR self
check function. In this prototype, we tried to emphasize the primary function of the app is to check
machine status. Thus, the first page users see will be the status page for all product lines. As users click
into each product line, they will then be able to view specific machines and their status. From there, we
added the AR feature for the users to experience. For the AR self check, we prototyped the layout of the
interface, trying to make the AR check easy to understand for the user. The AR labels will appear on the
screen on the correct parts of the machine telling the user what to do next. Eventually, after all steps are
finished a report will be generated telling the user the approximate results of the check. The feedback
for this prototype is to create a better AR interface that could also display the remaining tasks of the self
check and also making connecting to tech support easier after the check is completed.

high-fi prototype (link):

In the final high-fi prototype, we were able to rethink the feedback from mid-fi and fill in the design details
of the project. The AR interface now contains more information for the users to know how long the
process will take, and the during after the check is completed, the users can now directly choose from
call, video chat, and messenger chat to contact tech support.

As users enter the app, they can easily log into their account. Once they're logged in, they can directly see
all their product line’s general status on the first page. The erroneous product line is overlaid in red instead
of black and also brought up to the top of the list in order to make it stand out for the users to realize the
problem. Under the product line name, the small note of how many machines are normal and how many
has errors also hint the user of the product line status.


As the users move to the specific machines page, they will be able to swipe left and right to see all
machines in the current system. As before, the problematic machine will be labeled in red and pushed
to the first of the list for priority inspection. The users can also check the detailed status of the machine
by clicking the details button on the top right of the cards. On the back, the specific status and a service
log will be provided to track the status and the history of the machine. Users can directly enter AR mode
after clicking on the “AR Status Check” button in the front.


During the AR check, the goal is to make sure the user can easily use the feature and also be well
informed in the process. On the top left, the user can see their current task and the approximate time
for the task. On the bottom there’s also a drop down bar that when clicked can display the check list of
the checkup. The user can track the task they’re on with a sense of progress in mind. The main
directions will be shown in the center of the screen, telling the users to either open up places using
specific methods, or asking them to hold a certain angle so the the app can scan the erroneous parts
of the machine.


After all tasks are completed, the app will start generating the report for this checkup while uploading
those data to the company at the sametime. By doing this, the tech support can understand the users
machine status without being at the place, those data can also help engineers analyze the design and
improve future products. After the report is done, the results page displays the approximate results for
the user and offers a few possible causations according to the database. If the status is serious, the
user is directly offered three methods of contact with tech support: reserve, video chat, or message chat.


Once clicked on chat now, the user will be navigated to the chat box function of the app and start a direct
conversation with the tech support regarding issues in the checkup. During other times, the user can chat
about other issues with the bot or with a real agent. Another function of the app is the Explore tab on the
bottom right of the app. This feature uses the GPS function on users phone and according to the users
location, analyzes what potential business the user can expand on. According to those businesses the
app will then recommend machines from MARKEL that can fulfill their needs.


value created:

With the introduction of AR diagnostic, the customers can easily self-identify the machine’s problem, and
if the problem is easily mendable, with the guide of self-checking process, they can efficiently troubleshoot
the problem without calling for help from the operator. This will save the time for not only the customers
but also your technicians, which could potentially decrease staffing requirements. At the same time, the
aggregated data of reports of machines can help engineers to get better insights on your systems in
practice and therefore, can better design their machines. With this data, we believe that the customers
can also be benefited from the improved quality of technical support.



Overall, this project taught us how to design new services by integrating native smartphone capabilities
(such as augmented reality, location services via GPS). In order to do that, we first interpreted available
data about Markel Food Group, analyzed its stakeholders and existing services via doing initial research.
By doing this, we learned what the current-state model looks like and gained insights on how the value
has been exchanged among stakeholders. The voice and tone activity also allowed us to see how critical
it is to create the persona of our company to better communicate with the users. During ideation, we kept
in mind what “value” can be created through this service and whether or not the service we are designing
can be enhanced by the capabilities of native smartphone apps. At the same time, we learned how the
context (types of user) determines to offer different types of solutions. Throughout this entire process,
we were enlightened by how designing for a service app can create value for both customers and the
service provider. By allowing users to be engaged in the value creation process (via letting them do
self-checkup, fix the easily mendable problems, send reports to the technicians), the company can not
only aggregate data but also can improve the quality of their technical support to offer a better service.
Realizing that the app that we designed can offer such “value co-creation experience,” it was rewarding
and at the same time, hoping to see if our innovative and transformative solution can be implemented to
Markel Food Group’s value co-creation experience in the future.

Thank you for reading,