Twin AI.

Duration
Dec 2025-Jan 2026

Location
Mountain View, CA

Task
AI Adoption
AI Agent Onboarding
Dashboard

Driving adoption of an agentic AI system for automated customer acquisition and lead management @Trademarkia.

During my internship at Trademarkia (a legal-tech firm), I designed workflows for Twin AI, focused on enabling the trustworthy adoption of AI-mediated client intake.

Highlight:
– A versatile motion framework we now reuse across client projects
– Visual storytelling principles tailored for abstract AI products
– A repeatable model for communicating technical innovation with clarity

Impact & Value

A toolkit for always-on intake SaaS

I Crafted trusted and low-friction onboarding UX workflow. Launched in 30 days, onboarded 10+ pilot groups and 500+ users, achieving 42% higher client conversion. The workflow I designed scaled from a single voice-based acquisition touchpoint to an AI operating system for trillion-dollar regulated industries.

What it brought us:
– A versatile motion framework we now reuse across client projects
– Visual storytelling principles tailored for abstract AI products
– A repeatable model for communicating technical innovation with clarity

MAIN CHALLENGE

Enabling professionals to trust an AI agent to represent their identity, expertise, and tone in high-stakes client interactions.

Transforming relationship-driven client intake into AI-mediated automation is less a technical challenge than a trust and behavior shift. Because the agent speaks as them, adoption depends on whether it feels like trusted delegation rather than loss of control.

Design Progress.

FEATURE 1

Segmented Voice Agent Setup for Scenarios

In the first-time onboarding scenario, where agents need to set up their first AI twin quickly, we designed preset and reusable voice options to reduce setup friction.

As agents move beyond onboarding into more advanced usage, where trust and personal branding become critical, we introduced high-fidelity voice cloning to capture each agent's unique timbre and emotional inflection.

FEATURE 2

Low-Friction Flow to Equip AI with Property Knowledge

Users can select specific properties from their database to "teach" the agent the relevant details. By linking pricing, location, and size directly to the agent, the AI operates with a grounded, business-accurate knowledge base, rather than generic conversational intelligence.

FEATURE 3

Visual Script Editor to Control AI Conversations

To support users without technical backgrounds, we designed a visual, node-based script editor.

Granular Control: Users can toggle questions on/off, drag to reorder the conversation flow, and edit specific wording to match their personal style.

Visual Mapping: A node-based canvas showing the journey from the "Start" greeting to "Lead Qualification".

FEATURE 4

Embedding AI into CRM for Lead Management

The workflow culminates in a high-efficiency Leads Dashboard, ensuring a seamless handoff from AI to Human.

AI-generated conversation summaries and a lead claim priority score eliminate the need for agents to replay long recordings, dramatically improving follow-up efficiency and decision-making speed.

Evaluation.

TESTING & EVALUATION

Copilot study with 11 U.S. professionals showed strong interest and generated actionable feedback.

In moderated virtual sessions, professionals configured an AI agent, reviewed onboarding outputs, and evaluated performance in simulated client calls. Feedback highlighted strong interest alongside needs for clearer onboarding, greater behavioral control, and higher fidelity to professional tone and expertise.

ITERATION

Enable ongoing visibility of agent behavior

The initial dashboard focused on agent setup and status. We evolved it to surface ongoing activity, call outcomes, and conversation previews, making AI actions visible and inspectable.This improves trust by showing what the agent is doing on the user’s behalf.

ONGOING IMPACT

The story does not end here...

The workflow I designed scaled from a single voice-based acquisition touchpoint to an AI operating system for trillion-dollar regulated industries.

Twin AI began as Trademarkia’s voice intake agent and became the foundation for GetBill — the company’s new AI operating layer for regulated industries. As it integrated into CRM, communication, and onboarding workflows, the agent expanded from a single touchpoint into continuous operational support.