TIF
_edited.png)
AI-powered document intelligence platform for International Logistics & Trade — transforming fragmented paperwork into a structured, validated, decision-ready system.
CASE STUDY - 02
AI-Assisted SaaS - Document Validation - Operational Dashboard
TYPE
SCOPE
End-to-end - Desktop - Complex Data Systems - Design System
ROLE
UX/UI Designer — Solo project
INDUSTRY
Logistics & International Trade
FOCUS
Operational efficiency · Data accuracy · Decision intelligence
DELIVERABLE
Hi-fi prototype - Design system
1. Empathize
The platform is designed for operations staff and administrative teams in logistics and international trade companies that manage high volumes of critical documentation daily. These expert users require accuracy, real-time visibility, and operational efficiency in fast-paced, compliance-heavy environments.
Target audience:
Goals
-
Manage and review logistics documentation and structured data quickly and reliably.
-
Identify document inconsistencies before they become operational issues.
-
Maintain clear visibility into the status of each shipment and its associated documentation.
-
Reduce manual review time and eliminate fragmented workflows.
Pain points
-
Repetitive, time-consuming manual reviews that are prone to human error.
-
Information scattered across multiple sources and systems.
-
Difficulty reusing, searching, or analyzing data extracted from documentation.
-
Feeling overwhelmed by the volume of information associated with each shipment, leading to inefficiencies and slower decision-making.
TIF positions itself as a structured, trustworthy technology layer for the global supply chain — built on precision, scalability, and AI-driven efficiency. The visual identity reflects this through a clean, high-contrast interface that favors clarity and control over decorative flourishes, designed for expert users who value data transparency above all.
Its value proposition is built around trust, operational efficiency, and scalability.
Branding definition:
The product:
Digital platform designed for logistics and international trade companies to upload, organize, review, and manage documentation with AI-assisted support — structured around three core modules: Shipments, General Documents, and SKUs & Extracted Data.
2. Define
The problem:
Logistics teams manage critical documentation — invoices, packing lists, certificates of origin, customs declarations — that is frequently fragmented across systems and heavily dependent on manual review. This makes information difficult to access, increases the risk of human error, and slows down time-sensitive, data-driven decisions.
The goal:
To design a modern AI-powered platform that centralizes document management and validation for international logistics, automatically surfacing inconsistencies, reducing manual effort, and delivering real-time visibility into shipment status — enabling operations teams to work with greater speed, accuracy, and confidence at scale.
The solution is structured around three core intelligent modules:
Shipments: Create shipment records, upload documentation, and validate key information through AI-assisted analysis, supported by a clear visual checklist of document status and completeness.
General Documents: A centralized hub for uploading and reviewing any type of document against custom validation rules, with AI generating exportable reports that highlight findings, inconsistencies, and actionable insights.
SKUs & Extracted Data: An intelligent dashboard that consolidates and organizes extracted data from packing lists and invoices, allowing users to search, filter, and analyze information by shipment or SKU for faster decision-making.
Benchmark insights:
To ground the design in market reality, I conducted a competitive audit of leading AI-powered document processing platforms in the logistics and supply chain space. The goal was to identify usability gaps, best practices, and opportunities for differentiation. Each was evaluated against four criteria — accessibility, flow clarity, results visualization, and UI/UX quality — to uncover recurring usability gaps and identify what a stronger, more user-centered solution could look like.
Across all four platforms, two patterns stood out as consistent gaps: dashboards that prioritize raw data over visual clarity, and inconsistent attention to accessibility and detail. This revealed a clear opportunity for TIF — to differentiate through a dashboard that visualizes findings and inconsistencies clearly, paired with an interface built on accessibility-first principles from the ground up, rather than treating them as an afterthought.
3. Ideate
Sitemap:
As this diagram was originally created in Spanish, Claude AI was utilized to provide a precise and optimized English translation, ensuring consistency in terminology, context, and user experience language.
User Flow:
Low-Fidelity Wireframes:
4. Prototype
High-Fidelity Wireframes:
High-fidelity wireframes were developed integrating TIF's visual identity, Material Design components, and accessibility principles — translating validated low-fidelity structures into a polished, production-ready interface.
Mockup
An interactive prototype was built in Figma to simulate real navigation across all three modules, incorporating transitions and loading states to test the end-to-end document validation experience.
5. Going forward
Conduct user testing with operations and administrative teams to validate the accuracy, clarity, and efficiency of the document review flow.
Analyze usage data and satisfaction metrics to identify friction points and prioritize the most valuable features for future iterations.
Expand the AI validation engine to support additional document types and more nuanced inconsistency detection.
Develop a fully responsive mobile experience to support on-the-go validation for field and operations teams.
Continue iterating on the platform as a living system, grounded in User-Centered Design principles and real user feedback.
