PROJECT OVERVIEW

Kontxt

Kontxt is an AI platform that detects and prevents mobile messaging fraud, designed for RealNetworks.

Kontxt - Dashboard

Dashboard

PROBLEM DEFINITION

How to stop fraud and spam messages?

According to statistics, the average person receives around 50 text messages per day, but not every notification is something they actually want.

Fraud attempts, spam, and smishing have become increasingly common, often appearing as simple texts, links to call or message a number, or URLs leading to suspicious websites or app downloads. These unwanted messages create frustration, waste time, and can lead to serious financial or security risks.

At the same time, important messages, especially those related to safety, urgency, or expectations are often missed or never delivered.

How can we detect and prevent harmful messages while improving the overall messaging experience?

DESIGN PROCESS

Design process

The Double Diamond framework (Discover → Define → Develop → Deliver) was used to identify key user types, understand their needs, and uncover the challenges they face when interacting with messages.

Discover

• Personas

• Journey map

• Competitive analysis

Define

• HMW questions

Develop

• Wireframes

• User flows

• High-fidelity design

• High-fidelity prototype

Deliver

• Usability testing

• Final design

• Design handoff

DISCOVER

Persona

We identified two types of users for this product: customers (mobile network operators, aggregators, businesses) and subscribers (message recipients).

This phase focused on subscribers, so I created a persona representing their pain points, needs, and goals.

Journey map

Building on the previously established persona, I created a journey map to illustrate how subscribers experience both wanted and unwanted text messages. This helped me uncover pain points, explore potential solutions, and inform design decisions.

Competitive analysis

Competitors with machine-learning capabilities offer many of the features explored in this project, but most lack a comprehensive yet simple web-based solution.

DEFINE

How might we

I created How Might We questions to align on both the customer’s goals and the subscribers’ needs, guiding design decisions that cater to all types of users.

  1. How might we improve the messaging experience by stopping fraud and spam content for subscribers?

  2. How might we provide a platform that would analyze and classify messages while allowing customers (mobile networks operators, aggregators, businesses) to access all critical information?

  3. How might we enable customers to strengthen loyalty and grow revenue?

DEVELOP

Wireframes

In the wireframing phase, I explored information architecture solutions that aligned with customers’ expectations.

Pictured below are explorations of the Dashboard page.

User flow

After aligning on the proposal with the team, I created a user flow that helped us define the functionality and the information architecture.

DELIVER

Usability testing

I created a prototype of the proposed solution and led in-person usability testing with 4 participants, one from each future partner company:

  • Vodafone (mobile network operator)

  • Syniverse (aggregator)

  • Cloudli (business communications provider)

  • Iconectiv (telecom infrastructure)

All participants successfully completed key tasks, such as monitoring messages and understanding analytics.

Final design

A comprehensive web app with a clean and easy to use interface. White space throughout the app is used to balance design elements and convey grouping.

Message types are presented in various colors to indicate a message type, for instance, green is used for authentication while red for emergency. Having strong and contrasting colors help in better readability, sense of hierarchy and space.

The screens below show how the web app works when the user is a mobile network operator.

Design system

I’ve designed a scalable design system, based on the atomic design methodology that covers all use cases that have been indentified for this project.

Usability heuristics

  • Visibility of System Status

  • User Control and Freedom

  • Consistency

  • Recognition Rather than Recall

  • Flexibility

  • Flexibility and Efficiency of Use

Challenges and trade-offs

Message classification

The challenge was how to visually distinguish each message type using a unique accent color to signal both type and urgency. While usability testing validated our color choices, the trade-off is that users may interpret colors differently, so the distinction might not be universally intuitive to each user.

Below is a preview of the design system’s responsive web components, alongside screens from the web app.

Kontxt - Dashboard
Kontxt - Analytics

Analytics

Kontxt - Messages

Messages

Kontxt - Support Requests

Support Requests

Kontxt - Classification

Classification

Kontxt - Notifications

Notifications

SOLUTION

Platform for preventing spam and fraud messages

An AI platform that helps improve mobile content deliverability and detects spam/fraud over SMS, voice and IP channels. It helps mobile network operators, aggregators, businesses deliver a better messaging experience for their subscribers.

Using machine learning, the technology can analyze and classify most message types: Two Factor Authentication, Customer Support, Promotion, Emergency Alert, Notification, Grey Route, Fraud, Spam.

The end result is a spam-free messaging experience for subscribers and increased trust in mobile network operators and brands, delivered through a quote-based enterprise solution tailored to each client’s infrastructure.

How Kontxt Works

IMPACT

KPIs

Analytics indicate strong adoption and engagement among customers. Customer reports also show a significant reduction in spam and fraudulent messages received by subscribers.

82%

spam and fraud reduction rate

81%

adoption rate

92%

engagement rate

How we measured success

  • Spam and fraud reduction rate (82%) was calculated by comparing the number of verified spam and fraud messages recorded in the customer’s network logs before and after the launch of the Kontxt filtering system. Kontxt’s analytics provided the message-level data used to identify and validate threats over time.

  • Adoption rate (81%) was calculated by the proportion of users who interacted with the app out of the total user base, since launch.

  • Engagement rate (92%) was calculated based on users who performed meaningful actions in the app, such as monitoring messages and interacting with analytics, per month.

All usage-based metrics were tracked with Google Analytics.

PROJECT DETAILS

Project details

  • Role: Senior Product Designer, UX Researcher

  • Company: RealNetworks

  • Industry: AI, SaaS

  • Market: B2B

  • Team: Product Designer, Product Manager, Engineering

  • Frameworks: Double Diamond, Scrum

  • Tools:

    • Design: Figma, Miro

    • Project management: Jira, Confluence

    • Product analytics: Google Analytics

  • Year: 2018

  • Platform: Web

  • Link: Kontxt.com