Studio Scale
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Make powerful AI feel understandable and useful.

AI product design

We design AI experiences that help users know what to ask, understand what the system is doing, judge its output and stay in control when the answer is uncertain.

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Replicate Labs project
Artisse project
Safe AI learning platform for students, teachers and school administrators
AI-assisted influencer discovery and campaign management platform

Selected clients

  • Replicate Labs
  • Influencer Marketing.Ai
  • Teacherspace
  • Get Seconds
  • Framework Island
  • Occasion

Where it gets hard

A capable model does not automatically create a usable product.

Users may not know what to ask, what the system knows, whether an answer is reliable or how to correct it. The product experience needs to turn technical capability into clear expectations, useful interaction and justified confidence.

  • 01

    AI onboarding

    Explain what the product can do, what information it needs and how users can reach a useful first outcome.

  • 02

    Prompt and input UX

    Replace intimidating blank boxes with examples, structure, progressive guidance and inputs suited to the task.

  • 03

    Output clarity

    Organise generated results so users can understand, compare, edit, reuse and act on them.

  • 04

    Trust and explainability

    Show relevant sources, limitations, status and reasoning cues without overwhelming users with technical detail.

  • 05

    Human control

    Give users clear ways to review, revise, reject, regenerate, undo and recover instead of treating AI output as final.

  • 06

    Activation

    Reduce the time and uncertainty between entering the product and experiencing its core value.

Key moments

AI moments we consider

  1. 01Expectation setting before the first prompt
  2. 02Prompt examples and templates
  3. 03Context collection
  4. 04File and data permissions
  5. 05Generation progress and waiting states
  6. 06Uncertain, partial or failed results
  7. 07Sources and confidence signals
  8. 08Editing and refinement
  9. 09Regeneration and version history
  10. 10Human approval
  11. 11Feedback and correction
  12. 12Privacy and data-use explanations
  13. 13Clear next steps after an output

What clients say

Over the past six months, we have worked with five different UIUX designers, but none of them came close to the level of expertise and professionalism that Sevgi has demonstrated. In a field where deadlines are critical and the pace of work can be demanding, she has consistently delivered high-quality work on time. Her ability to manage time effectively and prioritize tasks has been instrumental in keeping our projects on track.

William WuCEO and Founder, Artisse

How we work with AI teams

A focused process, from evidence to launch.

  1. Step 01

    Define the user promise

    We clarify what the system should help users accomplish and where the model’s limitations affect that promise.

  2. Step 02

    Map uncertainty

    We identify where users need more guidance, feedback, evidence or control across the journey.

  3. Step 03

    Design around behaviour

    We create flows and interfaces for successful outputs as well as ambiguity, delay, failure and correction.

  4. Step 04

    Measure understanding and activation

    We define the user behaviours that indicate the product is becoming understandable, trusted and useful.

10+

Regular clients

100%

Job success score (Upwork)

Dribbble Select, top product design company

Recognized by Dribbble Select

Choose the right starting point

Relevant solutions

  • 01

    For products with traction

    Fix the flows that leak revenue

    Onboarding, checkout, activation… We redesign the journeys where users drop off, and measure the difference.

  • 02

    For pre-launch founders

    MVP design, 0 → 1

    We turn your idea into a clear, usable product you can test, pitch to investors, or hand to engineers, typically in 3–6 weeks.

  • 03

    For teams shipping weekly

    New features, designed to ship

    Spec to dev-ready designs with edge cases covered, so your engineers stop guessing and your roadmap stops slipping.

  • 04

    For teams without in-house design

    Your design team, weekly

    A senior design partner embedded in your workflow; Slack, async video, weekly deliveries. Pause or cancel anytime.

Selected work

AI experience shaped around real users not only model capability.

See our work

Other examples

  • Safe AI learning platform for students, teachers and school administrators

    New feature - UX/UI

    Designing a Safer AI Learning Platform

  • AI-assisted influencer discovery and campaign management platform

    New feature - UX/UI

    A Smarter Way to Find and Manage Influencers

  • Intelligence platform that turns market signals into actionable insights

    New feature - UX/UI

    Making Intelligence Easier to Find and Act On

FAQ

Questions founders actually ask us.

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We need enough technical context to design honestly around capability, latency, failure and data constraints. We do not need access to proprietary model code.

No. We also work with SaaS products adding AI to one workflow or feature.

We can improve the product experience around prompts: examples, structure, context gathering, feedback and refinement. Model prompt engineering may require collaboration with your technical team.

We test the surrounding decisions and behaviours across a range of realistic outputs, including uncertainty, failure and correction not one ideal response.

2 client slots open

Turn AI capability into a product people know how to use.

Show us where users hesitate, abandon or lose confidence. We will explain what we would investigate first and which engagement fits the problem.

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