Most teams approach AI features backwards: they get a model working, then hand it to design to "make it usable." The products actually winning with AI inverted that order — they designed trust, transparency, and failure-handling first, then built the interaction around it. This is grounded in how real users form trust in probabilistic, non-deterministic systems, not deterministic ones — a fundamentally different design problem than typical product UX.
Use Cases
Your AI feature works technically but users don't trust or adopt it
You're shipping LLM features without a clear differentiation story
Error states and edge cases feel like an afterthought, not a design
Leadership is asking "what's our AI strategy" and no one has a confident answer
Ideal For
• Product teams shipping LLM features
• Enterprises integrating AI
What’s Included
The Outcome
AI features that feel purposeful, not bolted-on — users who trust the output because the design earns that trust at every step.
What It Solves
Industry Approach
Common Questions
AI features carry unique problems — explaining probabilistic output, designing for failure and uncertainty, calibrated trust — that standard patterns don't address alone.
The UX around it: trust, transparency, error states, interaction patterns. Model and engineering work stays with your team.
As a core design surface, not an edge case — error, uncertainty, and low-confidence states get designed with the same rigor as the primary flow.
Yes — Industries/AI-Native describes the domain expertise; Consulting/AI Product Design & Strategy is the specific engagement, scope, and pricing.
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Currently taking new clients · Typical start: 1–2 weeks from contract