Building user trust in systems they can't see
Users can't verify AI decisions the way they verify a calculator. We design transparency layers, confidence signals and explainability patterns that make trust earnable, not assumed.
Disrupting intelligence
UI/UX design for AI products.
Designing human–AI experiences built for clarity, trust and innovation.
Change how people interact with intelligent systems, from AI assistants and generative tools to machine learning dashboards and autonomous platforms, with design that makes the complex feel inevitable. AI products face a unique challenge: the technology is extraordinary, but users won't trust what they don't understand. We design the layer between the model and the human, where clarity becomes competitive advantage.
What we solve
We shape strong digital experiences for artificial intelligence.
Making complex model outputs legible and actionable
AI generates outputs that are probabilistic, multimodal and often ambiguous. We turn raw model outputs into interfaces that non-technical users can read, act on and confidently trust.
Designing for unpredictable and non-deterministic behaviour
Unlike traditional software, AI doesn't always produce the same output twice. We design systems with robust error states, graceful failure modes and correction flows that keep users in control.
Onboarding users to products with no prior mental model
AI products often do things users have never done before. We design onboarding that builds accurate mental models, so users know what to expect, what to ask and how to recover when things go wrong.
Where we work
Optimising every layer of artificial intelligence.
- Generative AI tools & copilot interfaces
- Machine learning & data intelligence platforms
- Chatbots, assistants & voice interfaces
- Intelligent automation & robotics interfaces
How we help
We don’t just design. We build for the sector.
AI research
How users form and break trust with AI systems, studied with real people and real model output before any interface is drawn.
AI design
Interfaces that make model outputs legible, auditable and useful without exposing complexity the user doesn't need.
AI systems
Stable design systems for organisations deploying AI across many teams and fast-changing product surfaces.
AI strategy
Use cases, guardrails and product roadmaps for AI features, grounded in real human need rather than in what the model can do.
AI adoption in the Gulf and the Levant
Few regions have committed to artificial intelligence as publicly as the Gulf. Saudi Arabia has made data and AI a pillar of Vision 2030, with a national authority and a national strategy driving adoption across government and industry; the UAE was one of the first countries anywhere to appoint a minister for AI and has written it into its national agenda for government services, health and mobility; and Lebanon contributes a steady stream of engineers and founders to the region’s AI start-ups.
That ambition shows up in products quickly: government assistants that answer in Arabic, copilots inside banking and telecom apps, forecasting dashboards in energy and logistics, and generative tools for Arabic content. Most of them are judged not on the model but on the first conversation a user has with it.
The design problem is trust. Users here, like everywhere, will not rely on a system they cannot read, and in a bilingual market the system also has to be legible in Arabic, respect local context, and make clear what it knows, what it is guessing and when a human should step in.
How we approach AI products
We start by understanding how your users form and break trust: what they expect the system to do, where the model’s behaviour will surprise them and what happens when it is wrong. That research shapes the mental model the interface needs to teach, and the journeys we design first, the first prompt, the first disappointing answer, the first correction.
We then design the layer between model and human: prompt and input patterns, output presentation, confidence and source cues, review and edit flows, override and escalation paths, and honest empty and error states. Everything is prototyped with real model output rather than placeholder text, because an AI interface that looks right with a perfect example usually fails with the real thing.
How we prototype AI interfaces
AI products cannot be prototyped with static mock-ups alone. We build working prototypes connected to your model or a stand-in, so stakeholders and test participants react to real, variable output, long answers, wrong answers, slow answers, rather than a single polished example. Those prototypes become the basis for usability testing, trust-calibration studies and the production specification your engineers build from. This is how our product design practice works on every AI engagement.
FAQ
Common questions about artificial intelligence UX.
How do you approach UX design for AI products?
AI products introduce a unique design challenge: the system's behaviour isn't always predictable, and users need to understand what the AI is doing and why. We approach this by designing for explainability first, making outputs legible, confidence levels visible and fallback states handled gracefully. We also spend significant time on mental model alignment, ensuring the interface reflects how the AI actually works, not how we wish it did.
Do you have experience designing AI dashboards and data visualisation interfaces?
Yes. We've designed dashboards where data density, hierarchy and at-a-glance readability are critical. Our work covers real-time monitoring interfaces, analytical dashboards and AI output displays, always with a focus on helping users extract the right insight at the right moment, without cognitive overload. We use progressive disclosure to surface complexity only when it's needed.
How do you design for user trust in AI systems?
Trust is earned through transparency and consistency. We design AI interfaces that communicate what the system knows, what it's uncertain about and what it can't do, so users never feel misled. This includes clear affordances for human override, audit trails where stakes are high, and honest empty states. We also avoid over-anthropomorphising AI behaviour, which tends to erode trust once limitations surface.
Can you help us design the UX for a product that uses generative AI?
Absolutely. Generative AI products come with a distinct set of UX challenges, prompt design, output review flows, iteration patterns and managing user expectations around variability. We've worked on interfaces where the AI generates content, recommendations or decisions that users then review and act on. We design the human-in-the-loop experience to feel natural, not like an afterthought bolted onto a model.
How do you measure success for an AI UX project?
Beyond standard usability metrics, AI UX success is measured by trust calibration, whether users rely on the system appropriately, not too much or too little. We track task completion with AI assistance, error correction rates, feature adoption and qualitative signals around user confidence. For generative products, output acceptance rate and edit frequency are strong indicators of how well the UX is setting the right expectations.
Do you design AI products for Saudi Arabia and the UAE?
Yes. Both countries have made AI a national priority, Saudi Arabia through its national data and AI authority and strategy, the UAE through a dedicated ministerial portfolio for AI and a long-running national programme, and Lebanon has a growing community of AI start-ups and engineers serving the region. For the products built on top of that ambition, the design challenge is the same everywhere: people need to understand what the system is doing, in their own language, before they will rely on it. We design the experience layer, prompts, outputs, confidence cues and correction flows, and work with your data and governance teams on what the product is allowed to do.
Can you design Arabic-first AI interfaces?
Arabic and right-to-left layout is a foundation of how we work, not a translation step at the end. AI products add their own layer: Arabic prompts and generated text, dialect variation, mixed-script output with code, numbers or English terms embedded in Arabic sentences, and streaming responses that have to render correctly right-to-left. We design and test these in Arabic from the first prototype, with real model output, not placeholder text, then adapt to English.
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