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title_pics_INFER.jpg INFER

Product Designer


HMI Design

User Experience Design

Interface Design

User Research

User Testing

how does it WORK ?

INFER provides programmable REST APIs to integrate with your existing enterprise solution. With these APIs, you can create, view, edit, and delete various INFER IoT Center entities such as Devices, Campaigns, Alerts, Notifications, Groups, and Users, programmatically.

Multi-use Cases
Enterprise Systems
Devices & Things
External Ecosystem
Data Flow

I followed the double diamond design process for the INFER Edge management and monitoring platform dashboard project. It is a balance between finding the problem and solving it - exploring ideas and making decisions.

Understanding the Problem

  • Research: Gather information.

  • Analyze:  Identify patterns and trends.

Defining the Problem

  • Synthesize: Define the problem.

  • Focus: Narrow down to a specific issue.

Developing Possible Solutions

  • Ideate: Generate a wide range of ideas.

  • Prototype: Create low-fidelity prototypes.

Choosing and Developing the Solution

  • Test: Test with users and gather feedback.

  • Iterate: Refine based on the feedback.

  • Implement: Develop and launch.

edge device

The INFER Suite includes a comprehensive platform for Edge Device Lifecycle Management (EDLM). This lifecycle management system is designed to provide complete visibility and control over all IoT edge assets.

  1. Discover: Identify your edge assets across hardware, operating systems, and protocols.

  2. Plan: Strategize the deployment and management of your edge devices.

  3. Onboard: Implement Zero Trust onboarding, certificate-based identities, and connections.

  4. Configure: Set up your devices and orchestrate workloads at the edge on containers/bare-metal OS and VMs.

  5. Monitor: Keep track of your devices and proactively track vulnerabilities, anomalies, configuration drift, and data integrity of your Edge.

  6. Manage: Oversee your devices and manage millions of edge devices using customizable “Digital Twin” models specific to your use cases & industry.

  7. Secure: Implement security measures like Threat & Intrusion detection, mitigation by quarantining/access revocation.

  8. End-of-Life: Manage the retirement and replacement of your devices.

Image by Brands&People

Discover & Define


Throughout this process, I was not just a designer, but also a researcher, a strategist, and a problem-solver. I was the bridge between our users, our business, and our technology. It was a challenging role, but seeing our product, INFER, come to life was incredibly rewarding!

I was the one conducting user interviews, surveys, and observations - in the field, talking to users, understanding their needs, and empathizing with their pain points. Additionally, I analyzed the market, studied our competitors, and identified trends. I was responsible for understanding where our product could fit in and what unique value it could provide and communicate this with our stakeholders, ensuring that our design choices aligned with our business goals.

My current configuration lacks control points!

Vulnerable to security hacks

Creates another silo at edge and data

No uniform lifecycle protocol

No single source of truth


Creates vendor lock-in (H/W, Protocol, Platform dependent)

Unmanageable for enterprises,
when they have 1000s of IoT heterogeneous devices

The management procedures are disconnected and inconsistent

Now we have a problem statement!

HOW MIGHT WE  simplify and unify edge device management, secure the process and make the findings accessible and intuitive for end users.

In Addition, HOW MIGHT WE...

... make our solution scalable and user-friendly?

 ... ensure robust security for IoT devices and the data they generate?


... leverage AI to analyze the data generated by IoT devices and extract meaningful insights?

.... simplify the process of managing many IoT devices and sensors?

... ensure that our product aligns with our business goals and strategies while complying with all relevant regulations and standards? 

prioritizing REQUIREMENTS

The Edge Device Lifecycle Management (EDLM) platform requires a comprehensive Data Analytics Dashboard to report, analyze, and present data in real time. This cloud-based dashboard allows analysts to collect and organize all the relevant data into a single, convenient view. Stakeholders can visually monitor their IoT device portfolio, identify anomalies and discuss a course of action or remediate any potential issues.

A complex data analytics dashboard can greatly benefit from a modular interface in several ways:

  1. Flexibility: A modular interface allows components to be rearranged, added, or removed based on the user’s needs. This makes the dashboard adaptable to different use cases.

  2. Ease of Use: By breaking down complex data into smaller, manageable modules, users can more easily understand and interact with the data.

  3. Scalability: As the amount of data or the number of features grows, new modules can be added without disrupting the existing layout.

  4. Efficiency: Users can focus on one module at a time, reducing cognitive load and making data analysis more efficient.

  5. Customization: Users can customize the dashboard to their preferences, improving user satisfaction and productivity.

  6. Maintenance and Updates: With a modular design, updates or maintenance can be performed on individual modules without affecting the entire system.


Simplify connecting & managing EDGE environment

Assimilate the data to create meaningful insights

On the spot remediation when things go wrong


Comprehensive Understanding:

To provide a comprehensive understanding of the IT/OT network topology.


Predictive Analytics:

To leverage valuable data from the edge to achieve predictive analytics.


Preventative Maintenance:

To enable preventative maintenance.

  1. Interoperability:
    Capable of running in private, public clouds, hybrid, distributed, and on-premises.

  2. APIs:
    All of the core features of the INFER IoT Center provide REST APIs.

  3. Integration:
    The platform provides programmable REST APIs to integrate with your existing enterprise solution.

  4. Security:
    The platform ensures secure onboarding and management of IoT and Edge devices.

  5. Scalability:
    The platform is designed to manage millions of edge devices.

Image by Tim Gouw

Design & Develop

  1. Idea Generation: This involves developing as many ideas and concepts as possible. It’s a creative, dynamic process, full of trial and error. The goal is to end with a solid set of potential solutions.

  2. Sketch: Begin the design process by sketching out your ideas. This allows you to explore different design possibilities before committing to digital mockups.

  3. Wireframing: This is where wireframes come in. They are used to lay out the structure of the interface, showing how the different parts relate to each other and how the user will interact with the product. Wireframes are typically low fidelity, focusing on function rather than form.

  4. Collect Feedback: Gather feedback from team members and stakeholders on your sketches. This helps to refine your ideas and ensure they align with user needs and business goals.

  5. Prototype: Create digital mockups and prototypes of your design. This gives you a more realistic representation of the final product and allows for more detailed testing.

This is crucial in creating and validating a product like’s INFER analytics dashboards and GUI. It involves generating a wide range of ideas for how the platform could meet the identified user needs and business goals. Prototypes are created and tested with users, and the design is iterated based on the feedback received. This process then continues until a design is developed that meets the user needs, business goals, and technical constraints.

Group 53.png

Modular Dashboard will enable user to:

monitor IoT health

run actions on devices

identify anomalies

update software OTA

troubleshoot remotely

upload directly to cloud

updated wireframes.png
design system

Outside of the actual product - the dashboard and everything associated with its conception - I was simultaneously directing the brand identity itself, and everything it encompasses (but that is another story for another time). 

However, this dichotomic opportunity allowed me to develop their design system from scratch - well, visually, that is. Given the financial constraints of a lean start-up, the quickest and most efficient solution was re-skinning an existing system that used our tech and design stacks. 

At that time, we used Carbon UI (thanks, IBM), but there are loads of UI kits.

Reskinning a UI kit isn't equal to a complete design system, which is why creating some form of documentation is advisable. One of the most significant benefits of starting lean is that you can prove your system's worth through doing.

Image by ilgmyzin


  1. Finalizing the Design: This involves singling out the best ideas and choosing the final design. It’s about making decisions based on the insights gathered during the previous stages.

  2. Building and Testing Prototypes: This involves creating high-fidelity prototypes that closely resemble the final product. These prototypes are then tested to ensure they meet the user needs and business goals.

  3. Delivering the Solution: This involves handing over the final design to the development team for implementation. It’s about ensuring that the design is technically feasible and can be built within the defined constraints.

  4. Validating the Solution: This involves conducting final usability tests and gathering feedback from users. It’s about ensuring that the final product not only meets the design specifications, but also delivers a great user experience.

  5. Determining the Solution: This involves assessing the success of the solution. It’s about measuring the impact of the design on the users and the business and learning from the process for future projects.

In the context of’s INFER analytics dashboards and GUI, this stage involves finalizing the design of the dashboards and GUI, building, and testing high-fidelity prototypes, delivering the final design to the development team for implementation, validating the final product with users, and determining the success of the solution.


A modular interface data analytics dashboard can be successful by adhering to the following principles:

  1. User-Centric Design: It is intuitive, easy to navigate, and presents data in an understandable manner.

  2. Modularity: To focus on one piece of information at a time and makes the dashboard easier to navigate.

  3. Customizability: Customize the dashboard to suit their needs.

  4. Scalability: Handle increasing amounts of data without becoming cluttered or difficult to navigate.

  5. Interactivity: Like drilling down into details, filtering data, or changing the display format.

  6. Performance: It loads and refreshes quickly and performs well, even with large amounts of data.

  7. Integration: With various data sources and systems.

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