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MEL · CONVERSATIONAL AI

mel

Turning real-time AI conversation into an emotionally alive experience.

MEL is a live AI companion experience where conversation, expression, and emotion evolve in real time. I designed the end-to-end interaction model—from character creation and voice selection to emotional feedback, gifting, and dynamic video—so users could shape a companion that feels responsive, expressive, and uniquely their own.

Role
Product Designer
Scope
UX/UI · Interaction · Design System
Duration
2024–2025
Platforms
iOS · Android · Web
MEL live AI companion video conversation on an iPhone

03PRODUCT INSIGHT

WHAT MADE THE CHARACTER FEEL ALIVE

A believable AI characterneeded more thana good response.

Conversation, voice, expression, and visual context had to respond together—consistently and in real time—for the character to feel truly alive.

CONVERSATIONEXPRESSIONPRESENCE

04CHALLENGE 01 · VOICE CONVERSATION UX

TURN-TAKING CLARITY

Voice made the AI feel alive. State clarity made it usable.

A live exchange can become ambiguous quickly. The interface had to make speaking, listening, waiting, and response timing legible without interrupting the conversation.

  1. 01
    Problem

    Voice, text, and visual response competed for attention.

  2. 02
    Principle

    One dominant state at a time; transition before decoration.

  3. 03
    Outcome

    Speaking, listening, waiting, and responding became distinct states.

05CHALLENGE 02 · CREATE YOUR OWN MEL

THREE WAYS TO BEGIN

Creating your own MELneeded more than oneway to begin.

Some users wanted a polished character immediately. Others had a specific face in mind, while some wanted control over traits such as age, personality, and skin tone. I designed three creation routes—Discover, Photo, and Custom—so users could begin with the level of intent and effort that felt natural to them.

  1. 01
    Problem

    A single creation flow assumed every user already knew exactly what they wanted.

  2. 02
    Principle

    Match the starting route to the user’s level of intent.

  3. 03
    Outcome

    Three routes from inspiration, visual reference, or detailed control to a personalized MEL.

Discover route with curated MEL characters
01

DISCOVER

Start with our strongest curated characters.

Photo route using a visual reference
02

PHOTO

Create a character from a visual reference.

Custom route for selecting individual traits
03

CUSTOM

Define the character through individual traits.

INSPIRATIONREFERENCECONTROL

THE MOST NATURAL STARTING POINT

Users preferred reactingto a complete characterbefore defining one.

Discover became the most frequently entered creation route. Curated presets required the least upfront effort and gave users confidence in the final quality before asking them to define individual traits.

MEL Discover screen with a centered character in a vertical oval
Overlapping oval character cards in MEL Discover
DISCOVERY, ONE CHARACTER AT A TIME

WHY AN OVAL WHEEL?

Extending the brand languagefrom circles into interaction.

MEL’s visual identity was built around circular forms. I extended that language into a vertical oval to better frame portrait-oriented character imagery within a mobile screen. The centered character remained visually dominant, while partial characters at both edges signaled that more options could be explored.

Compared with a grid, the wheel made fast comparison less efficient, but gave each character more visual presence and made browsing feel closer to meeting characters one at a time.

The wheel changed browsing from comparison to discovery.

06CHALLENGE 03 · GENERATIVE VISUAL PRODUCTION

FROM POSSIBILITY TO PRODUCTION

Generated visuals had to bemore than possible.They had to be product-ready.

At the time, generative models could produce striking single images, but character identity, anatomy, composition, and scene continuity were still unreliable.

I worked closely with the ML team to turn visual judgment into a repeatable ComfyUI-based production workflow. I defined the emotional brief, scene hierarchy, composition, continuity, and product constraints, then reviewed and refined the outputs until they could function as real date and location scenes—not just technical demonstrations.

The technology showed what was possible.Our workflow made it usable.

  1. 01 · DEFINETranslate each relationship moment into a visual brief.

    Mood, relationship context, location, camera direction, and product constraints.

  2. 02 · BUILDCreate repeatable generation conditions with the ML team.

    ComfyUI nodes, prompts, references, seeds, and controllable inputs.

  3. 03 · EVALUATEReview every output through a product-quality lens.

    Character identity, anatomy, composition, artifacts, scenario clarity, and UI legibility.

  4. 04 · SHIPAdapt selected visuals for real product scenarios.

    Date scenes, locations, crops, safe areas, and platform-specific formats.

ComfyUI node workflow used to create repeatable generative visual outputs
COMFYUI WORKFLOWRepeatable inputs made visual judgment operational.

07IMPACT & REFLECTION

REPORTED BETA SIGNALS

A more coherent AI experienceshowed stronger engagement signals.

7 → ≈30min

Reported average chat duration

6% lower

Reported onboarding exits

≈1,000

Beta participants

Metrics are retained from the original beta materials and presented as reported directional signals rather than causal proof.

A believable AI companion is not created by model capability alone. It emerges when conversation, voice, character creation, and generated scenes work together as one understandable experience—giving users a reason to stay and continue the relationship.
What this experience opened next

From guidance to sensory feedback

After reducing hesitation in an AI creation flow, I explored a different question: can immediate sound, motion, and haptics make interaction itself worth repeating?

Next project · 03 / 04KKuukTactile interaction · Haptics · 2026