Aeyesafe inc | B2B | SaaS
Aeyesafe is an AI-powered smart monitoring system that helps care homes keep seniors safe through non-intrusive technology.
Built for B2B environments, the platform uses a network of sensors (thermal, radar, LiDAR, and sleep sensors) to detect abnormal behavior, monitor well-being, and alert caregivers in real-time. Unlike traditional systems, Aeyesafe doesn’t rely on wearables or cameras—making it ideal for sensitive care settings.
Building a 0-1 Alert System for B2B SaaS
PROBLEM
Care home administrators struggle to navigate incoming alerts quickly, leading to missed critical notifications.
BUSSINESS GOAL
Deliver finalized, developer-ready designs by mid-August to support a Beta release, ensuring a successful 1.0 launch by mid-October 2023.
OUTCOME
By mid-August, the final designs were delivered to developers with clear documentation, ensuring a smooth transition into development.
MY ROLE
Product Designer (UX, Research, UI)
TEAM
Two other Product Designers, Project Manager, Two Engineers
TIMELINE
July 2023 - August 2023
I designed multiple workflows for this project, but to keep the story clear, I’m focusing on one key workflow. This example best shows the user journey and the important design decisions that solved major challenges.
THE CHALLENGE
Building a robust Alert System within a tight 6-week timeline required fast, focused decision-making. We prioritized rapid ideation and took a phased approach—delivering early, then iterating quickly based on feedback.
I collaborated closely with developers and the PM from day one to define key micro-interactions, account for edge cases, and ensure seamless data validation. This alignment helped us balance speed with thoughtful UX, enabling a successful launch under pressure.
To maintain focus and efficiency, I created a structured roadmap guiding the design process.
Who are Aeyesafe users?
Meet James, a care home administrator overwhelmed by a growing volume of incoming system alerts.
With multiple residents to oversee and limited staff resources, it’s becoming harder for him to identify which alerts are urgent and require immediate action. He’s looking for a software solution that can help him prioritize alerts, reduce noise, and ensure nothing critical gets missed—without adding to his administrative burden.
RESEARCH
To guide product strategy and uncover unmet user needs, we conducted user research focused on real-world workflows, pain points, and expectations.
Methods
Secondary research and semi-structured user interviews
Participants
2 care home administrators
Research Findings from Secondary Research
50%
Exceeded expected response times
Research Findings from User Interviews
Time
1 week
The real challenge for care homes isn’t just the number of alerts—it’s the risk of missing critical ones and the limited time they have to respond.
“When an alert comes in without context, I have to stop what I’m doing to track down the details. That slows everything down.”
10%
of calls canceled
over 3%
forgotten entirely
Lack of context delays action—while new alerts keep piling up.
Without key details, staff must pause to investigate each alert. Meanwhile, the system continues to generate new ones, increasing the overall volume and stress.
“It’s overwhelming—by the time I understand one alert, there’s already a backlog of others waiting.”
“If I had the right information upfront, I could prioritize and act faster instead of playing catch-up.”
How might we help care home administrators navigate alerts more efficiently to prevent missed notifications?
Guiding Principles
Clarity & Prioritization – Alerts should be easy to scan and prioritize, ensuring critical issues stand out while reducing cognitive overload.
Efficiency & Speed – Users need to quickly understand and act on alerts with minimal friction, reducing time spent on unnecessary steps.
Context & Relevance – Every alert should provide enough relevant information upfront, preventing users from searching in multiple places.
IDEATION
Understanding the administrator's need for rapid response, I created an end-to-end alert workflow. The diagram below illustrates the resident-to-administrator journey and highlights the key touchpoints we designed for efficiency.
With a clear workflow as our priority, I led a cross-functional session with developers and the PM to confirm every interaction. We navigated technical constraints and edge cases, resulting in a streamlined handoff and significantly reducing the potential for costly revisions.
Design
Turning insights into low-fidelity prototypes, I began exploring key design directions grounded in usability, clarity, and efficiency.
The goal was to create an intuitive alert system that made it easy for users to see, understand, and act on what mattered most. Each design decision was informed by user pain points uncovered during research—ensuring that visibility, prioritization, and actionability were at the core of the experience.
DESIGN
After validating key design decisions, we refined the final designs to ensure clarity, usability, and seamless developer handoff.
Each element was optimized based on user feedback, prioritizing alert visibility, quick actions, and efficiency. The finalized designs balanced functionality and scalability, ensuring administrators could manage alerts effectively without feeling overwhelmed.
KEY DESIGN ELEMENTS
WORKFLOW
Alert Filters
Users can easily find alerts by filtering them through multiple options.
Resident Alerts
ADDITIONAL PAGES I CREATED
Users can track alerts directly on the resident page, helping administrators monitor alert history and status efficiently.
Edge case
This message is shown when there are no alerts to display, indicating an empty state.
Alert Reports
Reports are available for all alerts from the past 12 hours, week, and month, helping users analyze trends and adjust alerts by category or resident.
Custom Thresholds
Care homes can adjust alert thresholds based on their specific needs and protocols.
Key Learnings
Better Prioritization Improves Usability – Categorized alerts and contextual info reduce response time.
Privacy vs. Safety Balance – AI-driven monitoring must protect privacy while ensuring safety.
Although I left before the official launch, Beta Version 1.0 of the alert system was built on the framework I designed, ensuring long-term impact and future scalability.
Next Steps
AI Enhancements – Conduct further AI model training to improve alert accuracy and reduce unnecessary notifications. Implement machine learning to recognize false positives and refine alert categorization.
Broader Research – Gather insights from caregivers and diverse care homes.
Impact Measurement – Track response times and user satisfaction.