MODO’s LLM interface required a design overhaul to feel modern, scalable, and brand-aligned. The existing version lacked visual consistency and fluid structure.
Client Requirement
The client wanted a clean, scalable interface to surface model controls, manage prompts, and make outputs easier to interpret. They needed better affordances for prompt composition, result comparison, and multi-step workflows while keeping the interface approachable for a range of users.
Our Solution
We designed a multi-layered information architecture for control, clarity, and iteration. Interaction design focused on predictable affordances, clear error handling, and lightweight onboarding for new users. We used concept validations, moderated user testing, and heuristics reviews to refine the interface and reduce friction in multi-step workflows.
Industry
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Services
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Timeline
2023- Ongoing
Visual Identity
The MODO design system introduced a modern visual language using a refined palette of purples, greys, and accessible tones to support an AI-driven interface. The use of Roboto typeface paired with structured layouts enhanced readability and user focus, while the consistent color usage established a strong product identity. This system was built to be flexible and scalable, ensuring that as the LLM grows, the design remains intuitive, cohesive, and brand-aligned.
User Interface Highlights
Prompt Composer & Presets – Structured prompt templates, inline suggestions, and history recall to accelerate quality outputs.
Conversation & Session Management – Threaded sessions, saved contexts, and clear rollback affordances for iterative work.
Result Comparison & Explainability UI – Side-by-side output comparisons, metadata overlays, and confidence indicators to improve trust.
Performance & Resource Controls – Clear cost/time tradeoff settings and preview modes to help users choose appropriate model configurations.
Final Output
The redesigned LLM interface makes powerful capabilities approachable. Users can craft prompts faster, compare outputs with confidence, and manage sessions more effectively, improving model adoption and production readiness.
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