K1st BUILD

Build AI Models from Domain Knowledge

k1st-cover

Company

Role

Product Designer

year

2022 – 2023

Overview

In industries like manufacturing and predictive maintenance, AI engineers often struggle with fragmented data and inefficient processes. K1st BUILD changes the way models are developed by leveraging expert knowledge as the foundation, rather than relying on vast datasets. This new approach enables engineers to create more accurate and adaptive models without the usual delays caused by incomplete or noisy data.

As the Founding Product Designer, I led the user research, built a scalable design system, and ensured that engineers could seamlessly integrate expertise into their workflows.

The Challenge: Data Limitations Slowing AI Engineers

AI engineers frequently face three key issues:

  1. Data Scarcity: Incomplete or noisy data limits the accuracy of models.

  2. Manual Preprocessing: The process of cleaning and organizing datasets is labor-intensive and time-consuming.

  3. Disconnected Expertise: It’s difficult to translate domain knowledge into models, leading to fragmented and slow communication with experts.

These challenges resulted in inefficient workflows, making it difficult for engineers to meet the demands of predictive maintenance and similar applications.

Approach: Centering AI Development on Expertise

To overcome these limitations, K1st BUILD was designed to enable AI engineers to build models by directly capturing and applying expert knowledge rather than relying solely on vast datasets. Working closely with engineers like Zhang Yu-san from CNA (China & Northeast Asia Company by Panasonic Group), I explored their workflows and identified the key pain points.

The key components of this approach included:

  • Knowledge Capture: Engineers could input domain-specific insights, bypassing data-heavy processes and improving model accuracy. This shifted the focus from cleaning data to using real-world expertise.

  • Knowledge-to-Model Translation: K1st BUILD facilitated the conversion of expert knowledge into AI models through symbolic logic, reducing reliance on raw data while ensuring nuanced insights were reflected in the models.

  • Dynamic Model Updates: Engineers could continuously refine models as new knowledge became available, ensuring adaptability without starting from scratch.

By addressing these pain points, K1st BUILD allowed engineers to build models faster and more accurately. Before, they struggled with delays due to data shortages and disjointed communications with domain experts. After, the system enabled real-time integration of expert knowledge, reducing development time and improving model precision.

Prototype Feedback and Iterative Refinement

Early prototypes of K1st BUILD were tested with both engineers and domain experts. Engineers found the UI intuitive and appreciated how easily they could incorporate expert insights into models without manual intervention. Domain experts reported that the tool allowed them to provide real-time feedback, streamlining collaboration between them and the engineering teams.

Working closely with front-end engineers, I ensured a smooth handoff by developing a clear design checklist. Within three months of joining Aitomatic, I created a design system for K1st BUILD.

Impact: Faster, More Accurate Model Development

Before K1st BUILD, model-building was often slow and lacked the precision needed for critical applications. After implementing K1st BUILD, engineers gained access to a system that allowed them to integrate expert knowledge in real time, leading to faster development and more accurate models.

  • 30% Faster Model Creation: Engineers reduced time spent on data preparation by directly incorporating expert knowledge.

  • 15% Increase in Model Accuracy: By leveraging domain-specific insights, models became more accurate, leading to better predictions and outcomes.

  • Enhanced Collaboration: The tool bridged the gap between engineers and domain experts, fostering a smoother and more efficient collaboration.

Reflection

K1st BUILD was about letting experts drive the AI development process, not just relying on data. It was refreshing to see how much value we could get from human expertise instead of just more data.

The challenge was in designing a system that made it easy for engineers to input and apply expert knowledge without slowing them down. The biggest lesson for me was that designing for human expertise means building tools that work the way people think, not just the way machines process data.

Knowledge Base

Knowledge Base

Knowledge Base

Capture Knowledge

Capture Knowledge

Capture Knowledge

Knowledge from Files

Knowledge from Files

Knowledge from Files

Structured Knowledge Base

Structured Knowledge Base

Structured Knowledge Base

Translating Knowledge

Translating Knowledge

Translating Knowledge

Domain-Structured Knowledge

Domain-Structured Knowledge

Domain-Structured Knowledge

Building Model

Building Model

Building Model

Data from Knowledge

Data from Knowledge

Data from Knowledge

All Models

All Models

All Models

Model Details

Model Details

Model Details

Model Versions

Model Versions

Model Versions

Data Base

Data Base

Data Base