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Free expert review. Get UX insights for your SaaS. Book a Free 30-minute review

AI Product Design Agency

Design an AI Product people love to use

AI Interfaces
Data Visualization
ML Workflows

Trusted by 80+ SaaS companies

Common AI Product
challenges we solve

AI Explainability

Users don't see what the model did, why, or how confident it is.

  • Clear model outputs
  • Confidence indicators
  • Decision explanations
AI explainability interface

Trust Calibration

Users over-trust or distrust AI outputs. Bad decisions follow, or they drop the feature.

  • Confidence scoring
  • Human-in-the-loop design
  • Override mechanisms
AI trust calibration interface

Complex Data Workflows

ML pipelines need interfaces non-technical users can navigate.

  • Visual pipeline builders
  • Status monitoring
  • Accessible configuration
ML pipeline dashboard

A decade of product design, working on your AI product

We help AI companies design interfaces that make models accessible and outputs clear.

You get intuitive interfaces that explain model outputs, calibrate user trust, and streamline ML workflows.

80+

B2B SaaS products we've worked on

10+

years of product design experience

4.9

stars on Clutch

What we can do for you

AI output visualization

Interfaces that make predictions, recommendations, and generated content interpretable at a glance.

Prompt & input design

Prompt builders and configuration panels users can navigate without guesswork.

ML pipeline dashboards

Monitoring dashboards for model performance, training status, and data quality.

Human-in-the-loop interfaces

Review, correction, and feedback workflows for guiding model outputs.

Why teams choose Donux

...and not generic agencies or freelancers

Experience

10+ years building products. 80+ SaaS companies. 15+ products launched. We know what works.

SaaS specialization

We focus on B2B SaaS and PLG. Complex flows, product analytics, feature adoption. This is our daily work.

Product mindset

Pretty screens don't matter if they don't solve problems. Like a PM, we balance desirability, feasibility, and viability.

Every startup stage

We've worked with companies from first customers to rapid growth. Our decisions are backed by what we learned on those projects.

Featured Case Study

How Kontai went from idea to MVP in 3 weeks

Kontai, a Startup Bakery venture, helps companies manage and optimize their SaaS subscriptions and spending through a smart spend journal

Read full case study
Kontai AI spend management platform

F.A.Q.

How do you approach designing for AI explainability?
We start by mapping what the model decides and what users need to trust those decisions. Then we design layered explanations. A quick summary for most users, with drill-down for power users. The goal is appropriate understanding for the user's task.
What's different about designing for AI products vs. regular SaaS?
AI products bring unique challenges. Non-deterministic outputs, confidence users must calibrate, evolving model behavior, and human oversight. We design for uncertainty, trust, and the feedback loops that make AI products improve over time.
Can you design for both technical and non-technical AI users?
Yes. Many AI products serve both data scientists who configure models and business users who consume outputs. We design tailored interfaces for each persona while keeping the product cohesive.
How do you handle the 'trust problem' in AI interfaces?
Trust calibration is about giving users the right level of confidence. We use confidence scores, explanations, comparison views, and human override mechanisms. The goal is informed decision-making.
How long does it take to design an AI product?
An MVP prototype takes 3-4 weeks. A full product with research, testing, and design system takes 3-6 months. AI products benefit from iterative sprints because UX evolves as model capabilities change.
Do you need to understand our ML models to design the product?
We need to know what the model does, the inputs it takes, and the outputs it produces. We interview your data science team early, then translate capabilities into user-facing experiences.
What are the two ways to work with Donux?
  • Senior humans for design-led engagements. A dedicated team of senior designers and PMs works alongside your product team.
  • Magic Team for AI-augmented delivery. A managed team of AI agents and senior humans ships features, MVPs, or bug fixes as PRs in your repo, reviewed by senior designers and developers.

We'll help you build the right product, faster

The first step is a quick chat.