Bei YeUI/UX Designer

An AI assistant for supply chain teams—enabling cross-system queries, structured reports, and confident decisions.

ApoLink AI assistant overview and product context

Research & Insight

Users see value in AI but struggle to trust it, often cross-checking results and preferring structured outputs like tables and charts. Adoption is constrained less by model capability than by trust, transparency, and fit with existing workflows.

Research synthesis: user interviews and insight artifacts

Opportunity

How might we design an AI assistant that users can rely on in real operational contexts?

Problem space: operational context and design opportunity
Solution: multimodal chat with structured summaries, tables, and charts

Key Features

  • AI report generation

    Generate weekly reports with summaries, KPIs, and charts.

  • Flexible visualization

    Switch chart formats based on analysis needs.

  • Context-aware assistance

    Provide answers based on workflow context.

  • Lightweight side panel

    Resizable and works across tools.

  • Mobile accessibility

    Access insights and reports on the go.

Trust patterns: data sources, consistency, and domain-specific language
Feature: AI report generation and flexible data visualization
Feature: context-aware assistance and lightweight side panel
Impact: product engagement and usage in operational workflows
Learning: gap between generated insights and user action

Impact & Learning

65%

Weekly active users

5.6

Avg. interactions / user / week

35%

Reports led to user action

Closing: actionable, trustworthy AI integrated into real workflows

Engagement was strong, but the gap between insight and action remained: successful adoption depends on making outputs not only accurate, but actionable, trustworthy, and woven into real workflows.