JabFab
Real-Time and Reflective Experience
Optimization Platform
Brief
JabFab is a Real-Time Service Optimization platform that helps service-driven organizations improve on-site operations through structured live feedback. The platform enables teams to capture, analyze, and act on feedback from customers, employees, and third parties while they are still present turning service delivery from reactive to proactive.
Buzzinga Co. partnered with JabFab to modernize and evolve the entire platform. We took complete responsibility for rethinking the user experience, rebuilding core application flows, and upgrading their technology stack to support long-term scalability. Our role includes ongoing product development as we continue to design, optimize, and build new features for the platform.
Services
UX Research
UX/UI Redesign
Design system
Visual Design
AI Experience Strategy
Full Stack Development
Project Management
Email Designs
Deliverables
Web Application
Ongoing Support
Domain
Enterprise SaaS
Feedback & Engagement Platforms
Challenge
The previous version of the platform was functional but limited.
It had outdated technology, fragmented user flows, and limited scalability. As new use cases emerged, adding features became difficult and the experience felt inconsistent across industries and teams.
The UX was fragmented
Operational data existed, but it wasn’t being surfaced in a useful, actionable way
The existing technology stack slowed down development and limited scalability
New use cases and customization requests were difficult to implement
The platform needed a stronger foundation to support AI-driven insights in the future
JabFab needed a stronger foundation.
The UX had to be simplified, the tech stack needed modernization, and the system required flexibility to support future AI-driven features.
Discovery
We began by mapping out the entire operational flow, from frontline staff and service points to admins and decision-makers. Through workshops and interviews with JabFab’s team, we identified how feedback is captured, prioritised, reviewed, and acted upon across different environments like hospitals, hotels, and retail spaces.
This helped us define key user types, friction points, and improvement areas:
Too many steps to initiate feedback
Lack of clarity in user roles and permissions
Opportunities to automate repetitive tasks
Valuable insights were captured but not surfaced clearly
Different industries needed variations of the same features
Our insight
We translated these learnings into simplified user journeys, cleaner data pathways, and a feature roadmap designed for future scalability.
The result was a clear blueprint of how JabFab should work, not just how it currently worked, and that became the foundation for the redesign and rebuild.
Approach
We designed JabFab as a Real-Time Service Optimization (RSO) platform with an integrated AI foundation.
It combines instant frontline feedback with structured reflective analytics — two halves of one loop: action and understanding.
Core layers of the solution:
Real-Time Mode
Instant feedback from QR codes, or short links at service points. When something goes wrong, teams get live alerts, can respond immediately, and recognize great service moments on the spot.
Reflective Mode
Post-experience MicroSurveys sent to customers, patients, or staff via email. These uncover patterns and help leadership teams see where to invest in process, training, or communication.
RTLX Framework
JabFab’s AI tagging system (RTLX-A, L, X, and R) gives structure to every piece of feedback defining who, where, what, and why. It turns raw data into AI-interpretable context that can be searched, filtered, and explained naturally.
Audience Map
A unified data model that connects all feedback across sites, teams, and projects — without creating silos. It’s flexible, attribute-driven, and scalable from one facility to an entire enterprise.
UX Architecture & System Design
We designed JabFab’s architecture around a single idea one platform, two modes.
In Real-Time Mode, the interface centers on service points and alerts.
In Reflective Mode, it switches to audience-based analytics and trend summaries.
Both draw from the same Audience Map and RTLX model, ensuring total consistency between moment-level actions and organizational learning.
We created detailed interaction frameworks for each use case:
Frontline users receive alerts and respond to live service gaps.
Managers review reflective dashboards and identify systemic issues.
AI copilots summarize trends and recommend next steps.
This multi-layered UX system unified operations, analytics, and AI into a single, intuitive experience.
Visual Design & Prototype
The visual approach focused on clarity, hierarchy, and calm confidence.
We designed an interface that feels as intuitive for a nurse as it does for a regional operations leader.
Key components included:
Dual Dashboard Layout: one view for real-time signals, another for reflective analysis.
AI Insight Cards: short, explainable summaries that reveal the “why” behind patterns.
Dynamic Filters: simple controls powered by attributes like Region, Role, or Department.
Recognition Stream: positive feedback flows that highlight great performance instantly.
A high-fidelity prototype connected to simulated data validated the usability and flow across different user types.
Outcome
The new JabFab platform is faster, clearer, and more scalable, built to grow across industries and support real-time decision-making. User flows are now simpler, setup requires fewer steps, and data is surfaced in a way that helps teams take immediate action on the ground.
With the updated technology stack and redesigned experience, JabFab can now:
Onboard clients more efficiently
Customise features for different industries
Deploy updates faster
Support AI-driven insights in future releases
It went from a functional product to a platform built for long-term evolution. And we continue to support JabFab with ongoing design and development as they expand their reach.
Start Your Next Journey With Us
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