We design and prototype the places where people and AI meet—XR/spatial, voice, and multimodal tools—for clear, low-friction collaboration. Our approach combines user-centered design with rigorous evaluation to ensure AI interfaces are not just functional, but genuinely useful and trustworthy in real-world contexts.
Comprehensive Interface Design
Effective human-AI interaction requires more than technical functionality—it demands interfaces that support human cognition, decision-making, and workflow integration. We design interaction patterns that clarify roles, responsibilities, and hand-offs between humans and AI systems, ensuring seamless collaboration rather than confusion or conflict.
Our interface design encompasses teaming patterns that define who proposes versus who decides, clear hand-off procedures, escalation protocols, and recovery states when things go wrong. We create interaction modes that span spatial and mixed reality environments, voice interfaces, chat-based systems with forms, multimodal combinations of text, voice, and vision, and haptic feedback systems.
Explanation and Uncertainty Handling
One of the most critical aspects of human-AI interfaces is how systems communicate their confidence, limitations, and reasoning. We design explanation frameworks that answer “why this?” questions, present alternatives when appropriate, and provide safe defaults that users can rely on. Our approach ensures that AI systems are transparent about their capabilities and limitations, building trust through clarity rather than obscurity.
We implement confidence indicators that combine numeric and qualitative cues, ensuring users understand both the strength and nature of AI recommendations. Our interfaces include safe fallback actions and graceful degradation when AI systems encounter uncertainty or edge cases.
Agentic Workflows and Task Management
Modern AI systems often operate as autonomous agents, requiring sophisticated workflow management and human oversight. We design agentic workflows that handle task decomposition, approval processes, guardrails, rollback capabilities, and comprehensive audit trails. Our systems ensure that AI agents operate within defined boundaries while maintaining human control over critical decisions.
Our workflow designs include learned prompts that provide in-product hints based on prior successes, while implementing guardrails against risky inputs. We create escalation procedures that hand control back to humans with full context and suggested next actions when needed.
Accessibility and Inclusion
Inclusive design is fundamental to effective human-AI interfaces. We ensure our interfaces accommodate diverse languages, cognitive loads, and interaction preferences. Our designs include assisted interaction modes, offline and low-signal operation capabilities, and support for users with varying technical expertise and accessibility needs.
We implement privacy-conscious design patterns that integrate consent and notice mechanisms directly into the user experience, minimize data collection, and enable local processing where appropriate. Our interfaces respect user privacy while maintaining functionality and performance.
Real-World Evaluation and Iteration
Our design process includes comprehensive evaluation in real-world contexts. We conduct usability testing, comprehension checks, trust assessments, and operational metrics analysis to ensure interfaces work effectively in practice, not just in controlled environments.
We measure decision quality through right-first-time metrics and time-to-decision analysis. Our evaluation includes override and accept rates categorized by risk class, comprehension and trust scores measured at the task level, and near-miss and incident rate monitoring with recovery time analysis.
Implementation Process
Our implementation process begins with discovery phases that include field interviews, workflow shadowing, and failure-mode mapping to identify early risks and opportunities. We then move to design and prototyping phases that create interaction patterns, states, and flows through quick prototypes for mixed reality, voice, and web interfaces.
Pilot integration involves lightweight integration into existing technology stacks or sandbox environments, establishing data contracts and guardrails. In-context evaluation includes usability tests, comprehension checks, trust probes, and operational metrics analysis, with iterative refinement to ensure optimal fit.
Production readiness includes pattern library development, escalation rule definition, accessibility guidance creation, and measured rollout planning. Our deliverables include teaming blueprints that define roles, hand-offs, escalation, and recovery procedures, pattern libraries for explanations, uncertainty handling, confirmations, and error states, and comprehensive evaluation packages with test plans, results, thresholds, and iteration logs.
Target Outcomes
Our interface design aims to achieve higher right-first-time decisions and faster time-to-decision processes. We implement calibrated overrides with accept and decline rates categorized by risk class, ensuring appropriate human oversight without unnecessary friction. User comprehension and satisfaction improve over successive releases through iterative design and evaluation.
We reduce near-misses and improve recovery from errors through robust error handling and graceful degradation. Our interfaces pass comprehensive accessibility checks and maintain high usability standards across diverse user populations and use cases.