AI Explainability
Users don't see what the model did, why, or how confident it is.
- Clear model outputs
- Confidence indicators
- Decision explanations
AI Product Design Agency
Design an AI Product people love to use
Make complex AI capabilities understandable. Clear onboarding, useful interactions and a product your customers can trust.
Build with us
Common AI Product
challenges we solve
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
Complex Data Workflows
ML pipelines need interfaces non-technical users can navigate.
- Visual pipeline builders
- Status monitoring
- Accessible configuration
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.
They never miss a touch base and timeline expectation.
Abed el Madjid Nehme Head of Product @Blue.ai
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
Featured Case Study
Kontai, a Startup Bakery venture, helps companies manage and optimize their SaaS subscriptions and spending through a smart spend journal
Before you start.
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?
A first conversation defines the relevant scope, dependencies and investment. For an existing product transformation, we confirm what fits the 90-day delivery window before kickoff. A focused supporting engagement is scoped separately.
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.
See the work.
Selected product design projects. Explore the challenge, the decisions and the results behind each one.
