Design at Zingage is not a support function. It is a way of seeing, reasoning, and building systems that actually work for the people delivering care.
We are not just here to ship features. We are here to define what it means to work alongside AI in one of the most human industries in the world.
The Problem: Designing for AI in a Trust-Based Industry
In home care, people don’t use software because they want to. They use it because they have to.
Schedulers and caregivers operate under constant stress: backlogs of patient visits, late cancellations, last-minute reassignments. Their current tools make this worse—clunky, slow, opaque.
Now, introduce AI. Software that doesn’t just coordinate schedules but acts on its own. Agents that message caregivers, reassign visits, and resolve gaps without human input. This is powerful. But it also creates a new kind of risk:
What happens when something goes wrong, and no one knows why?
Traditional UI patterns break down here. The job of design is no longer just to simplify a workflow—it’s to help users build trust in a system that behaves more like a colleague than a tool.
The Opportunity: Interfaces for Delegation, Not Just Execution
Our goal is not to make care schedulers faster typists. It’s to help them delegate work to intelligent agents. But delegation requires:
- Knowing what the agent is doing
- Understanding why it did something
- Stepping in when something goes off track
In other words: clarity, not control. We are designing for a world where the default state of software is action, not waiting. Where the system moves first, and the human refines it.
This requires new UI patterns, new metaphors, and new forms of accountability. Most importantly, it requires a deep understanding of the users who will live in this hybrid loop.
Core Design Principles
1. Supervision Over Control
Users shouldn’t be expected to micromanage the AI. The interface must:
- Show intent, not implementation
- Offer intuitive paths for feedback and override
- Make risk visible without creating fear
2. Asynchronous by Default
Work doesn’t happen in one sitting. Schedulers reach out, wait for replies, reschedule, escalate, wait again. Our interfaces must:
- Support interrupted workflows
- Track state across time and agents
- Handle reversals and replans with grace
3. Mental Models, Not Just Screens
We don’t design pages. We design systems of meaning:
- What is a plan?
- What does it mean to delegate something?
- When has a task truly been resolved?
These are design questions.
4. Internal Surfaces Matter
We take Sarah Tavel’s advice seriously: "It’s okay to have a human in the loop. It’s better if it’s your human, not your customer's."
Our internal ops team supervises AI decisions, resolves edge cases, and monitors quality. These tools require the same care as the external product. They are the levers that make AI feel dependable.
5. Make Work Delightful
Design is emotional. Even more so in a field like home care.
We use reward loops, milestones, social reinforcement, and small moments of joy to make invisible progress feel tangible. This is not gamification. It’s respect—for people whose work is often ignored.
Why It Matters
Just as early mobile apps mimicked real-world textures (skeuomorphism) before evolving into native mobile patterns, AI will go through its own transition. Right now, AI systems need scaffolding. They need explanation, supervision, and context. Over time, they will disappear into the background.
Our design work must accelerate that curve. We believe the design challenges in AI are not about novelty. They are about sequencing trust. They are about making delegation possible in domains where mistakes have real consequences.
Sounds interesting? - We're looking for the next Diego Zaks to join us as our Founding Product Designer :)