Use Case

ATHENA

Venture Support & Scoring Platform — UX Design, Prompt Engineering, Full-Stack Development

Use Case

ATHENA

Venture Support & Scoring Platform — UX Design, Prompt Engineering, Full-Stack Development

Use Case

ATHENA

Venture Support & Scoring Platform — UX Design, Prompt Engineering, Full-Stack Development

Use Case

ATHENA

Venture Support & Scoring Platform — UX Design, Prompt Engineering, Full-Stack Development

My Role

Lead & Solo Designer Full-Stack Developer Testing & QA

My Role

Lead & Solo Designer Full-Stack Developer Testing & QA

Client

R-LABS Canada

Client

R-LABS Canada

Team

CEO - George Carras Head of Technology - Roy Liu Venture Lead - Jameel Somji Brand Designer - Mae Tsui Venture - Siobhan Byrne

Team

CEO - George Carras Head of Technology - Roy Liu Venture Lead - Jameel Somji Brand Designer - Mae Tsui Venture - Siobhan Byrne

Year

2024

Year

2024

Project Details

The Problem

R-LABS set a goal of evaluating 100 venture opportunities annually, but validating a single idea required approximately 3 days of research, analysis, and scoring. At that pace, evaluating 100 opportunities would require nearly 300 days of effort, making it difficult for a small venture team to review opportunities at scale. The challenge was to increase evaluation capacity without sacrificing research quality or decision confidence.

The Solution

I designed and developed Athena, an AI-powered venture ideation and validation platform that helps teams move from opportunity discovery to evaluation in minutes rather than days. Athena generates venture concepts from problem statements, automates approximately 80% of the research and validation process, and provides supporting sources for verification. This allows the venture team to focus their time on validating the remaining findings and making informed investment decisions.

Project Details

The Problem

R-LABS set a goal of evaluating 100 venture opportunities annually, but validating a single idea required approximately 3 days of research, analysis, and scoring. At that pace, evaluating 100 opportunities would require nearly 300 days of effort, making it difficult for a small venture team to review opportunities at scale. The challenge was to increase evaluation capacity without sacrificing research quality or decision confidence.

The Solution

I designed and developed Athena, an AI-powered venture ideation and validation platform that helps teams move from opportunity discovery to evaluation in minutes rather than days. Athena generates venture concepts from problem statements, automates approximately 80% of the research and validation process, and provides supporting sources for verification. This allows the venture team to focus their time on validating the remaining findings and making informed investment decisions.

Project Details

The Problem

R-LABS set a goal of evaluating 100 venture opportunities annually, but validating a single idea required approximately 3 days of research, analysis, and scoring. At that pace, evaluating 100 opportunities would require nearly 300 days of effort, making it difficult for a small venture team to review opportunities at scale. The challenge was to increase evaluation capacity without sacrificing research quality or decision confidence.

The Solution

I designed and developed Athena, an AI-powered venture ideation and validation platform that helps teams move from opportunity discovery to evaluation in minutes rather than days. Athena generates venture concepts from problem statements, automates approximately 80% of the research and validation process, and provides supporting sources for verification. This allows the venture team to focus their time on validating the remaining findings and making informed investment decisions.

Project Details

The Problem

R-LABS set a goal of evaluating 100 venture opportunities annually, but validating a single idea required approximately 3 days of research, analysis, and scoring. At that pace, evaluating 100 opportunities would require nearly 300 days of effort, making it difficult for a small venture team to review opportunities at scale. The challenge was to increase evaluation capacity without sacrificing research quality or decision confidence.

The Solution

I designed and developed Athena, an AI-powered venture ideation and validation platform that helps teams move from opportunity discovery to evaluation in minutes rather than days. Athena generates venture concepts from problem statements, automates approximately 80% of the research and validation process, and provides supporting sources for verification. This allows the venture team to focus their time on validating the remaining findings and making informed investment decisions.

The Journey at a Glance

Problem

Early-stage investors spent hours manually validating startup ideas using documents, scattered research, and gut instinct.

Problem

Early-stage investors spent hours manually validating startup ideas using documents, scattered research, and gut instinct.

Opportunity

Strong ideas were slipping through, slowing decisions and reducing team alignment.

Opportunity

Strong ideas were slipping through, slowing decisions and reducing team alignment.

Concept

R-LABS asked: What if AI could generate and score ideas to handle 80% of early validation automatically?

Concept

R-LABS asked: What if AI could generate and score ideas to handle 80% of early validation automatically?

My Role

I led the UX design, prompt architecture, and full-stack development of the Athena AI scoring system.

My Role

I led the UX design, prompt architecture, and full-stack development of the Athena AI scoring system.

Build

Using the Perplexity API, I created a structured LLM workflow, stored insights in WordPress database, and built real-time scoring with consistent output logic.

Build

Using the Perplexity API, I created a structured LLM workflow, stored insights in WordPress database, and built real-time scoring with consistent output logic.

Result

Idea evaluation dropped from days to minutes, giving the team fast, research-backed insights for better decision-making.

Result

Idea evaluation dropped from days to minutes, giving the team fast, research-backed insights for better decision-making.

Design & Build

Generate Idea Modal

To keep the interface focused and reduce visual clutter, idea generation and venture scoring were separated into dedicated modal workflows. This allowed users to complete complex tasks without leaving their current context while maintaining a streamlined workspace.

Idea List

Generated ideas are organized in a sortable table designed for reviewing large volumes of opportunities. The problem statement remains visible for context, while selection controls allow users to quickly identify and submit promising concepts for evaluation.

Scored Idea List

Scored ventures are displayed in a sortable and searchable table optimized for comparison. Filters and color-coded indicators help teams quickly identify high-potential opportunities, while low-scoring ventures are automatically removed to keep the workspace focused on stronger candidates.

Venture Details Page

Detailed evaluation reports provide a complete scoring breakdown based on the R-LABS framework. Supporting citations increase transparency by allowing teams to verify AI-generated insights, while actions such as printing, copying, and re-evaluating reports support collaboration and decision-making.

Redesign

Generate Idea

The idea generation workflow was redesigned from a modal-based experience into a dedicated workspace, allowing users to generate and review ideas without leaving the page. The redesign introduced support for both OpenAI and Perplexity, integrated search directly within the primary input area, and established a distinct visual identity for Athena. A new Trending Ideas section was also added to surface high-scoring concepts and inspire further venture exploration.

Score Idea

The scoring workflow was redesigned by separating the problem and solution into dedicated inputs, providing clearer context for AI-assisted evaluation. Key information such as the venture stage, AI model, and activity date was surfaced directly within the score list to improve visibility and reduce navigation. Secondary actions were consolidated into a contextual menu, creating a cleaner interface while keeping common tasks easily accessible.

Idea Details

Key Takeaway

95% Faster Validation

Reduced research and idea-scoring time from 3 days to 10 minutes using structured LLM reasoning and automated scoring.

95% Faster Validation

Reduced research and idea-scoring time from 3 days to 10 minutes using structured LLM reasoning and automated scoring.

Adopted by the Industry Issue + Transformation (I+T) Council

Used internally to demonstrate platform innovation and strengthen the company’s AI strategy.

Adopted by the Industry Issue + Transformation (I+T) Council

Used internally to demonstrate platform innovation and strengthen the company’s AI strategy.

Faster Team Alignment

Standardized scoring eliminated inconsistencies and helped the venture team compare ideas and make decisions more quickly.

Faster Team Alignment

Standardized scoring eliminated inconsistencies and helped the venture team compare ideas and make decisions more quickly.

Future-Ready for Investors

The platform now supports a roadmap for deeper validation, multi-team access, and external investor onboarding.

Future-Ready for Investors

The platform now supports a roadmap for deeper validation, multi-team access, and external investor onboarding.

Let’s Connect

Open to product design opportunities and collaborations.

© 2026 Sae-Hee Shin. All rights reserved.

Let’s Connect

Open to product design opportunities and collaborations.

© 2026 Sae-Hee Shin. All rights reserved.