Redefining how Deaf and hearing professionals communicate in real time.

Redefining how Deaf and hearing professionals communicate in real time.

Redefining how Deaf and hearing professionals communicate in real time.

I designed elephantalk to explore a question: What would accessibility look like if users could shape it in real time? The result became a participatory communication layer - a system that listens, learns, and evolves with every conversation.

TIMELINE

5 months (2025-2026)

ROLE

Founding team Product Designer (Workflow design, IA, UX, rapid prototyping)

COLLABORATORS

CEO, Front/Back-end developer

COMPANY

Norric Inc.

Overview

What is elephantalk?

Elephantalk is a real-time ASL-to-text translation layer designed for inclusive meetings. It turns sign language into text and accessibility into participation. Built for U.S.-based Deaf and hard-of-hearing professionals, the system learns directly from users through federated feedback, creating a loop where every correction helps the model improve.

During the meeting
Turning translation on

When the meeting starts, the user simply activates the elephantalk widget. With one tap, translation turns on, signing instantly appears as live captions for everyone in the call.

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One-tap toggle for both Deaf and hearing users

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Works across Zoom, Google Meet, and Teams

And if user sees any mistranslation, they can hit the button says "Mark caption as incorrect" and in their local device elephantalk will save their flag for later correction.

When meaning slips
Flagging an incorrect caption

If a caption doesn’t match what the user meant, they can tap “Mark caption as incorrect.”
The system saves this flag locally and learns how the user signs, capturing real context without interrupting the conversation.

By letting users flag and correct interpretations, the system grows more accurate and personal with every meeting, redefining accessibility as a shared process, not a service.

After the meeting
Correcting and improving

After the session, users can review their flagged captions and input the right translation in their own words. Through federated learning, the model updates locally, building a personalized ASL-to-text engine that improves with every interaction.

By letting users flag and refine captions, elephantalk grows more accurate and personal with every meeting, redefining accessibility as a shared process, not a service.

Brand&visual language

Building a design language of elephantalk.

When designing elephantalk’s brand identity, the goal was to make accessibility feel calm, connected, and trustworthy, not technical or assistive. Every visual element was built around the same principles that shaped the product itself: empathy, clarity, and participation.

Systemic Challenge

DDQ work was spread across PDFs, emails, and personal folders. Teams kept rewriting the same answers with no shared source of truth, which slowed everything down and created unnecessary manual work.

DDQ work was spread across PDFs, emails, and personal folders. Teams kept rewriting the same answers with no shared source of truth, which slowed everything down and created unnecessary manual work.

Solution

I designed Norric’s first AI-native workflow to bring everything into one place. Documents become structured and searchable, past answers can be reused, and collaboration, review status, and source history all live in a single flow.

I designed Norric’s first AI-native workflow to bring everything into one place. Documents become structured and searchable, past answers can be reused, and collaboration, review status, and source history all live in a single flow.

Impact

DDQs now move through one centralized system instead of multiple disconnected tools. Teams reduced repetitive work, responded more consistently, and built a reusable knowledge base they could scale over time.

DDQs now move through one centralized system instead of multiple disconnected tools. Teams reduced repetitive work, responded more consistently, and built a reusable knowledge base they could scale over time.