Product Design Agency for AI Companies
AI products face a design challenge that nothing else has: the output changes every time. Users need to build trust with something that is not fully predictable. We design AI interfaces that set correct expectations, make outputs actionable, and give users enough control to feel confident without feeling overwhelmed.
Capabilities
Our Approach
Model Understanding
We work with your ML team to understand what the model can and cannot do reliably. The biggest UX failures in AI products come from designing for the demo, not the distribution of actual outputs.
Expectation Design
We design the framing, language, and interaction patterns that set accurate user expectations. Confidence indicators, source attribution, output limitations — all the elements that prevent the "it works like magic until it doesn't" problem.
Workflow Integration
AI outputs rarely stand alone. We design the full workflow: how results get reviewed, edited, approved, and fed back into the system. The goal is making AI a tool in a workflow, not a replacement for the workflow.
Iteration Loops
Ship, measure, learn. AI products need faster iteration cycles because user behavior with AI changes as they learn the tool's capabilities and limits. We build measurement frameworks specific to AI product metrics.
Related Work
Frequently Asked Questions
How is designing for AI different from regular product design?
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The core difference is non-determinism. Traditional software does the same thing every time you click a button. AI products produce different outputs for the same input. This changes everything about how you design feedback, error states, trust signals, and user control. You also need to design for a product whose capabilities change as the model improves — the interface needs to accommodate that evolution.
How do you design trust in AI products?
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Trust comes from three things: transparency about what the AI is doing, control over the output, and graceful failure when it gets things wrong. Practically, that means confidence scores, source citations, edit and override capabilities, and feedback mechanisms. We also design the onboarding experience to calibrate expectations — users who understand the tool's limits trust it more than users who think it is infallible.
How much does AI product design cost?
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AI product design ranges from $60K–$250K. A focused engagement on core interaction patterns and output UX starts around $60K. Full product design with research, prototype testing, design system, and ML team collaboration is $150K–$250K.
Do you work with LLM-based products?
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Yes. We have designed interfaces for conversational AI, document analysis tools, code generation products, and content creation platforms. Each has different interaction patterns — chat-based, canvas-based, inline-assist — and we design based on the user's actual workflow, not the model's architecture.
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