Highest Demand

AI Product Design & Strategy

Most teams are bolting AI onto existing products. The ones winning are designing for AI from the ground up.

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Investment

From $15,000

Timeline

4–8 weeks

Format

Research + design sprint

Most companies approach AI features backwards: they get a model working, then hand it to design to "make it usable." The products actually winning with AI right now inverted that order — they designed the trust, transparency, and failure-handling experience first, then built the interaction around it. That distinction is the difference between an AI feature users tolerate and one they rely on. This engagement exists because that inversion doesn't happen by accident — it takes a strategy pass before any screens get built, informed by how real users actually form trust in probabilistic systems, not deterministic ones.

Signs You Need This

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

Ideal Fit

• AI-native startups
• Product teams shipping
• LLM featuresEnterprises integrating AI

Not a fit if

You need a specific AI model built or fine-tuned — that's an ML engineering engagement, not a design one.

What’s Included

AI product strategy workshop: use case prioritization and differentiation map

AI UX audit: trust, transparency, explainability, and hallucination handling

Mental model research with real users of AI-powered products

Interaction pattern design for generative, predictive, and agentic AI features

Error state, uncertainty, and edge case UX system

Onboarding flow design for AI-native products and features

The Outcome

AI features that feel purposeful, not bolted-on. Users who trust the output because the design earns that trust at every step.

How It Works

Strategy workshop

A working session to map your AI use cases against user need and technical feasibility, and pick the ones worth building first.

Trust & UX audit

Evaluation of how your product currently handles explainability, error states, and uncertainty — the parts users judge trust on.

Pattern & flow design

Interaction patterns and onboarding flows designed specifically for probabilistic, non-deterministic AI behavior.

Handoff

Documented specs and a walkthrough with your product and engineering team, ready to build from.

Common Questions

How long does an AI product strategy engagement take?

I focus on complex web applications like dashboards, B2B tools, SaaS platforms, and internal systems—especially in industries like healthcare, fintech, telecom, and productivity.

Do you design the AI itself, or just the UX around it?

The UX around it — trust, transparency, error states, and interaction patterns. Model selection and engineering are handled by your team.

What if we haven't decided which AI features to build yet?

That's covered in the strategy workshop — prioritizing use cases and differentiation comes before any design work starts.

How is this different from general product design work?

AI features carry unique UX problems — explaining probabilistic output, designing for failure and uncertainty, and building calibrated trust — that standard product design patterns don't address on their own.

Do you work with a specific AI stack or model provider?

No — the engagement is model-agnostic. It focuses on the user experience layer regardless of which LLM or AI provider your engineering team has chosen.

Open to new opportunities

Ready to design AI features people actually trust?

Book a free 30-minute discovery call

Tell me what you're building.
I'll tell you how I can help and exactly what it will cost.