SAIP: A Generative AI-Driven Enterprise Knowledge Platform
Services
Web Design /
Dashboard Design
Client
S2W.Inc
Date
October 2024
Project Overview
SAIP is an enterprise knowledge platform that integrates generative AI with internal data to deliver tailored insights and seamless knowledge access. Designed with a customized RAG system, SAIP provides document search, summarization, and intelligent Q&A features, helping organizations transform scattered information into structured, actionable knowledge. By aligning technical precision with user-centered design, SAIP enhances productivity and fosters smarter collaboration across the enterprise.
Project Highlights
Goal
SAIP aims to leverage industry-leading experience to flexibly address customer needs, solve problems, and deliver a tailored platform that creates tangible value through its services.
Outcome
We analyzed what generative AI tailored to enterprise environments should look like, identified solutions to meet user needs and enhance work efficiency, and explored ways to support seamless internal collaboration and knowledge sharing through a customized RAG system and an effective sharing network.
Challenge
The final deliverables focused on delivering a user-friendly interface tailored to S2W's specific requirements and an efficient admin page, maximizing the user experience for building internal knowledge assets. Additionally, we proposed a solution that enhances the value of enterprise data utilization.
Project Timeline
Explore Challenge
In the early stage, we applied the How Might We method to reframe problems from a fresh perspective, focusing on the underlying user needs. By generating diverse questions, we were able to uncover multiple potential solutions and broaden the scope of innovation.
Key Insight
In the next phase, we conducted a focus group interview (FGI) with nine members from different departments. Through this collaborative session, we transformed ambiguous problems into clear and actionable design requirements.
UI Wireframe
Redesigned the ASM navigation to improve visibility and hierarchy, enabling analysts to grasp asset status at a glance, and developed an advanced search wireflow that supports incremental filtering and live feedback based on real analyst workflows, reducing friction and enhancing decision-making in large-scale asset environments.
