AI is only as useful as the interface that puts it in front of a person. We design products, and the human-in-the-loop screens for copilots and agents, from research to tested prototype.
UX/UI design is our service for researching, prototyping and designing web, mobile and enterprise software, including the interfaces for AI copilots and agents where people review, correct and approve what the system proposes. It is for product and operations leaders who need software their teams will use every day, not just ship.
A model that is right about 95% of the time is a production success and an interface problem. The remaining 5% has to be caught by a person, quickly, without that person losing trust in the other 95%. Most AI features fail here: the output is a wall of text, confidence is invisible, and correcting a mistake takes longer than doing the task by hand.
74% of enterprises run AI in production and half cannot prove it pays off. Buyers now list human-in-the-loop design alongside audit logs and model cards as the governance artifacts they expect a vendor to show. An interface that makes clear what the system did, why, and how to override it is a control, not a nicety.
The basics still apply. Enterprise software competes with the spreadsheet the team already knows. If the new screen adds clicks, hides status or breaks on the warehouse tablet, people route around it and the operating metrics never move.
Evidence about the people who will use the product, before anyone draws a screen.
Structure and behavior users can predict, on mouse, touch and keyboard.
Visualize and validate with users before development starts.
A consistent visual language and a component library engineers can consume.
Human-in-the-loop design: where people see, decide and override.
Responsive web, native and cross-platform mobile, and operational UIs for hard environments.
Find the shortcomings early, and measure whether the redesign fixed them.
A design is finished when people use it. These practices get it there and keep it there.
Task completion, time on task, error rate and override rate are instrumented in the product. Design decisions are revisited against those numbers, not against opinion.
Every consequential AI action has a review surface with the evidence, a one-step override and a record of who decided. Review latency is tracked as an operating metric.
Components live in a shared library used by engineering, so screens stay consistent as the product grows and new AI features inherit the same patterns.
Usability testing does not stop at launch. Real usage over weeks is the most accurate feedback there is, and it feeds the next iteration.
2 engineersdelivered the episode engine over a one-year engagement
Mastory's episode engine: content managers author in JSON or a Flow language, XState runs the story, and ChatGPT-powered characters reply in context.
Under a yearfrom concept to iOS and Android release
An iOS and Android app for Dutch homeowners' associations: invoices, in-app chat, notifications and multi-association profiles, built on Java and Ionic.
Thousandsof construction tenders processed through the platform
From a business idea to one of Denmark's most popular construction platforms: document versioning, rights and tender management, print-vendor integration.
40+US carriers aggregated in the platform
A San Francisco logistics SaaS re-architected to microservices on AWS in two years, with carrier rate comparison and portals used by Fortune 500 shippers.
Product vision, market position and technical feasibility. We learn why the product exists, who it is for and what users think about it today.
Personas and empathy maps from interviews and observation: what users say, think, feel and do when they work. For AI features, where they need to see and where they need to decide.
Cross-functional sessions produce options; the strongest become sketches, wireframes and a clickable prototype. First deliverables within 2 weeks.
Usability testing with selected user groups before wider release, then a design system and specs handed to engineering, often our own product teams.
UX design covers how a product works for the person using it: research, information architecture, user flows, wireframes, prototypes and usability testing. UI design covers how it looks and behaves on screen: visual language, components, states and the design system that keeps it consistent. We deliver both, for web, mobile and enterprise software, and increasingly for the review and approval screens that sit in front of AI copilots and agents.
Research findings and personas, an information architecture and user flows, wireframes, a clickable prototype, usability test results, final UI designs for every screen and state, a UI kit and style guide, and handoff specifications for engineering. For AI features we add the human-in-the-loop flows: what the reviewer sees, how they override, and what gets recorded. Everything is delivered in Figma and documented so your own engineers can build from it.
First deliverables, usually research findings and initial wireframes, arrive within 2 weeks. A prototype ready for usability testing follows in a further two to four weeks, depending on the number of user roles and flows. Full UI design for a mid-sized product runs six to ten weeks in parallel with development, so engineering is never waiting on design. Redesigns of existing products are scoped after the UX audit.
It is how we find out whether the design works before it is expensive to change. We test prototypes with selected user groups, moderated or unmoderated, remote or in person, and watch where people hesitate, make errors or give up. Findings go back into the next iteration. After launch, real usage over time gives the most accurate feedback, and we keep testing against task-completion and adoption metrics.
A structured review of an existing product against usability heuristics, accessibility standards, analytics and, where possible, observed user sessions. It produces a prioritized list of problems with their likely impact on adoption, task completion and support load, and a recommendation on whether to fix, redesign or leave each area. It is the right first step when a product is in use but the numbers say people are not getting through it.
We start from the decision the person has to make, not from the model output. The interface shows what the system proposes, how confident it is, where the evidence came from and what happens if the person accepts. Overriding takes one step and is recorded. Routine cases are handled in bulk so attention goes to exceptions. Feedback from every decision flows back into evaluation. The result is an AI feature people trust enough to keep open.
A 45-minute discovery call. Bring the workflow and the AI feature; we will tell you where people need to see, decide and override, and what it takes to design that.
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