Vera AI Platform
Designed the foundational AI interaction layer used by 50K+ daily active users.
Problem
Users interacting with AI-powered tools faced unpredictable, inconsistent experiences. There was no unified interaction model — each feature used its own patterns for input, feedback, and output, leading to confusion, low trust, and poor adoption despite strong underlying model capabilities.
Context & Constraints
Vera was an early-stage AI platform serving enterprise knowledge workers. The challenge was designing interaction patterns that felt trustworthy and transparent while the underlying models were still being refined — meaning outputs could be unpredictable, latency varied, and confidence levels shifted.
- Model response times ranged from 200ms to 12 seconds
- Enterprise compliance required full auditability of AI outputs
- Users ranged from AI-native to deeply skeptical
- Cross-functional team of 12 across 3 time zones
Key Decisions
Confidence-aware UI as a trust mechanism
Rather than presenting all AI outputs with equal visual weight, we introduced a confidence spectrum — outputs styled differently based on model certainty. High-confidence answers appeared definitive; lower-confidence suggestions were presented as options, inviting user judgment. This increased user trust scores by 38%.
Progressive loading with semantic feedback
Instead of a generic spinner, we designed a streaming feedback system that showed users what the AI was doing — "Searching knowledge base...", "Cross-referencing policies...", "Drafting response...". This made variable latency feel intentional and informative.
Composable interaction primitives
Built a system of reusable AI interaction patterns — prompt scaffolds, inline suggestions, conversational threads, structured outputs — that product teams could compose into features without redesigning the interaction model each time.
Solution
A unified AI interaction layer consisting of composable patterns, a confidence-aware output system, and semantic loading states. The platform provided a consistent, trustworthy foundation that product teams could build on — reducing time-to-ship for new AI features from 6 weeks to 2 weeks.
Impact
- 50K+ daily active users on the platform within 6 months
- User trust scores increased by 38% after confidence-aware UI
- New AI feature time-to-ship reduced from 6 weeks to 2 weeks
- Adopted as the design standard across 4 product teams
Reflection
Designing for AI taught me that trust is the product. When outputs are unpredictable, the interaction layer becomes the primary mechanism for user confidence. The confidence-aware UI was initially controversial internally — some worried it would highlight model weaknesses — but it ended up being the single biggest driver of user trust and adoption. Transparency, done well, builds more confidence than polish alone.