Overview

Background

Solution

AI Research

AI Workspace for Vehicle Definition

Category

AI Product Design

Team

Jerry Cai

My Role

Vibecoding

UX Design

Product Design

VARRAY bridges the gap between product ambition and engineering reality. It is a data-driven SaaS tool that aligns early-stage concept research with physical constraints, eliminating development conflicts and market gaps.

Background

VARRAY comes up from my Internship experience as a PM in a leading China EV company

Quick-Paced Vehicle
R&D Cycles

The EV market demands rapid product definition. PMs must lock in critical dimensions in months, leaving zero margin for late-stage physical conflicts.

Limited Engineering Knowledge

Assessing technical feasibility relies heavily on the individual experience of Design Release Engineers (DREs), creating a massive communication efforts for development.

Hard to Visualize 

Product Requirement

PRDs hide physical constraints. It is incredibly difficult to intuitively visualize how pushing a target spec on paper will actually trigger hardware interferences in reality.

VARRAY Helps Automotive Product Teams in the Concept Phase to ...

Define Product
Scope

Transform abstract market demands and user ambitions into quantifiable, data-driven vehicle parameters.

Visualize Product Requirement

Bring data on the spreadsheet specs to life. Intuitively evaluate how data affect the actual vehicle experience.

Simulate System
Constraints

Identify conflicts instantly. Simulate concepts against strict physical limits to prevent costly late-stage redesigns.

Who is VARRAY designed for ?

Vehicle Product Manager

PMs use the AI workspace to conduct cost and effective decision with market demands to finalizing the PRD.

Vehicle Engineer

Engineers use VARRAY to establish physical guardrails, ensuring PMs cannot create impossible designs.

Strategy Researcher

Strategy researchers use the AI-aggregated market data to align decisions with market expectations.

Key Experience Scenario

Daniel's Challenges

Daniel needs to determine whether a 5.6-meter flagship sedan can achieve the desired market presence without introducing unacceptable maneuverability and cost trade-offs.

Currently, his leadership team is kicking off “Project Titan”—a next-generation flagship sedan.

The board demands a massive 5.6M length for ultimate market presence.

Currently, his leadership team is kicking off “Project Titan”—a next-generation flagship sedan.

The board demands a massive 5.6M length for ultimate market presence.

Market Benchmark

Start with Existing Requirements

Daniel creates a workspace by importing the existing PRD for Project Titan. VARRAY extracts its key vehicle targets and turns them into structured, comparable parameters.

Start with Existing Requirements

Rooted in real moments, everyday life, and community, Chevrolet has long designed vehicles around the people who share them. Its dealer communities also actively support animal welfare and pet adoption.

Build a Relevant Competitive Set

Based on the imported requirements, AI identifies comparable flagship sedans and explains why each model is relevant to the benchmark.

Build a Relevant Competitive Set

Based on the imported requirements, AI identifies comparable flagship sedans and explains why each model is relevant to the benchmark.

Visualize the Product Definition

The benchmark data is mapped onto an interactive 3D vehicle model, helping Daniel understand how abstract specifications translate into physical scale and proportion.

Visualize the Product Definition

The benchmark data is mapped onto an interactive 3D vehicle model, helping Daniel understand how abstract specifications translate into physical scale and proportion.

Dynamic Competitive Analyze

VARRAY visualizes the dimensional differences between Project Titan and its competitors, revealing where the concept leads, follows, or falls outside established market patterns.

Dynamic Competitive Analyze

VARRAY visualizes the dimensional differences between Project Titan and its competitors, revealing where the concept leads, follows, or falls outside established market patterns.

Constraint Simulation

Test the Wheelbase Trade-off

Daniel shortens the wheelbase to improve urban maneuverability while preserving the vehicle’s 5.6-meter overall length. VARRAY immediately detects conflicts with the existing chassis architecture and highlights the resulting impact on cabin proportions。

Test the Wheelbase Trade-off

Daniel shortens the wheelbase to improve urban maneuverability while preserving the vehicle’s 5.6-meter overall length. VARRAY immediately detects conflicts with the existing chassis architecture and highlights the resulting impact on cabin proportions。

Explore an Alternative with AI

Instead of compromising the vehicle’s proportions, AI recommends evaluating Rear-Wheel Steering as an alternative way to reduce the turning radius while preserving the original wheelbase.

Explore an Alternative with AI

Instead of compromising the vehicle’s proportions, AI recommends evaluating Rear-Wheel Steering as an alternative way to reduce the turning radius while preserving the original wheelbase.

Research Real-World Adoption

VARRAY investigates RWS adoption across comparable vehicles, analyzing market penetration, technical configurations, and recurring owner complaints.

Research Real-World Adoption

VARRAY investigates RWS adoption across comparable vehicles, analyzing market penetration, technical configurations, and recurring owner complaints.

Interactive Report Generation

AI generate insight and Guide Daniel with visualization on the technical specification

Interactive Report Generation

AI generate insight and Guide Daniel with visualization on the technical specification

Simulate Rear-Wheel Steering

Daniel shortens the wheelbase to improve urban maneuverability while preserving the vehicle’s 5.6-meter overall length. VARRAY immediately detects conflicts with the existing chassis architecture and highlights the resulting impact on cabin proportions。

Simulate Rear-Wheel Steering

Daniel shortens the wheelbase to improve urban maneuverability while preserving the vehicle’s 5.6-meter overall length. VARRAY immediately detects conflicts with the existing chassis architecture and highlights the resulting impact on cabin proportions。

How Daniel Bridged the Gap using VARRAY

Cowork with AI

Used AI to identify RWS as a viable alternative, investigated its market adoption technical configurations.

Ran the Simulatoion

Used the 3D sandbox to virtually test the size of the concept against urban turning constraints.

Secured the Design

Proved the ROI of RWS, locking in the executive ambition without breaking engineering reality.

AI Research

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