Travelio
This project solves common e-commerce usability challenges by delivering a seamless shopping experience focused on higher sales conversions.
AIRIAM is an AI-powered knowledge assistant designed to transform internal organizational documents into an interactive, conversational intelligence system. Instead of traditional search-based workflows, users can directly engage with their data through a ChatGPT-like interface that understands context and responds using relevant internal documents.
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PROJECT OVERVIEW
AIRIAM was designed to solve a common enterprise challenge: information is scattered across multiple documents, making it difficult for users to retrieve meaningful insights quickly. Traditional search systems fail to understand context, while generic AI tools often lack access to private organizational data.
To address this, AIRIAM introduces a structured AI system where users can upload documents, select specific files as context, and interact with an AI assistant that responds strictly based on those selected sources. This ensures that every response is grounded, relevant, and traceable.
The system consists of two main modules: an AI Assistant for conversational interaction and a Knowledge Upload system for building an internal document intelligence base.
THE PROBLEM
In most organizations, knowledge is fragmented across PDFs, reports, spreadsheets, and internal systems. Employees often spend excessive time searching for information instead of using it effectively.
Even when AI tools are introduced, they typically lack awareness of internal context, leading to generic or inaccurate responses. This creates a gap between “available data” and “usable intelligence,” resulting in inefficiency, miscommunication, and decision delays.
Challenge
Travelio is a travel booking app for flights, hotels, trip planning, and real-time journey tracking designed for users.
I worked across product design and system refinement, focusing on simplifying core user flows, improving usability, and ensuring a consistent experience across key features.
SYSTEM ARCHITECTURE
AIRIAM is built around two core modules: the AI Assistant and the Knowledge Base system. The AI Assistant handles all conversational interactions, while the Knowledge Base is responsible for storing, organizing, and retrieving uploaded documents.
Users can select specific documents as active context before starting a conversation. Once selected, the AI restricts its responses to the information contained within those files. This creates a controlled reasoning environment where answers remain relevant and traceable.
CORE EXPERIENCE — AI ASSISTANT FLOW
The AI Assistant is designed to replicate the familiarity of ChatGPT while introducing structured context control. Users can enter prompts, attach files, and optionally select documents from their knowledge base to guide the AI’s responses.
Once a document is selected, the system clearly informs the user that the conversation is now grounded in that file. For example, selecting a report like Market_Analysis_2023.pdf sets it as the active context, and all responses are generated based on its content.
This approach ensures that users always understand what the AI is referencing, making the system both powerful and trustworthy.
FEATURE HIGHLIGHTS
One of the key features of AIRIAM is its context selection system, which allows users to dynamically choose which documents the AI should reference. This eliminates ambiguity and ensures precision in responses.
Another important feature is the Knowledge Upload system, where users can add documents to the platform’s internal intelligence layer. Once uploaded, these documents become part of the searchable knowledge base.
Additionally, the system supports a structured RAG-style response mechanism, ensuring that every answer is derived from relevant internal sources rather than generic training data.
UX STRATEGY & DESIGN THINKING
The UX strategy behind AIRIAM focuses on three core principles: clarity, control, and trust.
Clarity ensures that users always understand what data the AI is using. Control allows users to actively define the context of each conversation, rather than relying on passive AI behavior. Trust is achieved by grounding responses in user-provided documents, making outputs verifiable and reliable.
The interface intentionally mirrors familiar chat-based systems to reduce learning friction while embedding advanced enterprise logic underneath.
VISUAL DESIGN SYSTEM
The visual design follows a minimal, modern SaaS aesthetic. The interface prioritizes readability, spacing, and focus, ensuring that the AI interaction remains the central experience.
The design avoids unnecessary complexity and instead uses structured panels, soft contrast, and clean input systems to support long-form interaction. This helps maintain user attention on the conversation rather than the interface itself.
FINAL OUTCOME
AIRIAM demonstrates how AI can be transformed from a generic conversational tool into a structured enterprise intelligence system. It successfully bridges the gap between natural language interaction and document-based knowledge retrieval.
The final experience delivers a scalable foundation for organizations to interact with their internal data in a more intuitive, controlled, and efficient way.
This project helped strengthen my understanding of AI-driven UX systems, particularly around context management, retrieval-based design, and enterprise workflow simplification.
It also reinforced the importance of designing AI interfaces that prioritize trust, transparency, and user control while maintaining simplicity in interaction.
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