Halo Lab’s approach to AI/ML design starts with explainability — how model outputs are surfaced, how uncertainty is communicated, and how users maintain meaningful control when AI is making or influencing decisions.
We design AI and ML product interfaces with those constraints at the centre — dashboards that make model behaviour legible, input flows that set accurate user expectations, and feedback mechanisms that make the AI better over time.
3 main challenges holding back your growth
Outgrown identity
Your company has grown, but the brand no longer reflects scale or direction.
Outgrown identity
Your company has grown, but the brand no longer reflects scale or direction.
Outgrown identity
Your company has grown, but the brand no longer reflects scale or direction.
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Context-free AI output
Predictions surfaced without confidence or explanation — users can’t evaluate or trust the output.
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Engineer-led interface
AI capability built first, UX considered after — adoption fails because the interface does too.
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No human-in-the-loop
Users can’t correct model errors — bad outputs persist and trust erodes with each one.
“All projects in which we’ve involved Halo Lab have been great successes, but their leadership on our brand refresh is worth highlighting.”
“We’ve had feedback from users, investors, and employees, and everyone has said that Halo Lab did a fantastic job with the branding.”
“We’re proud of their branding services. They not only do what we tell them but also make suggestions to help us improve our offerings.”
What we deliver
AI & ML UX design from
explainability to handoff
AI Output Design
Model predictions, recommendations, and results presented clearly — with confidence and context.
Explainability UX
Why the model decided what it decided — visualised in a way users can actually understand.
Uncertainty & Error States
Low confidence, edge cases, and model failures — designed before engineering encounters them.
Human-in-the-Loop Design
Override, correction, and feedback flows — giving users meaningful control over AI outputs.
AI Dashboard Design
Data-heavy dashboards — metric cards, model performance, and insight surfacing for AI products.
Input & Prompt Design
Input forms, query builders, and prompt interfaces — designed for how users actually frame requests.
AI Product Design System
Component library and design system — AI state patterns, tokens, and documentation for your stack.
Engineering Handoff
Annotated Figma files, AI interaction specs, and uncertainty state documentation ready for build.
Our most ambitious work
How we work
Our process for your
AI & ML UX/UI design
Start with a strategy call where we align on your goals, challenges, and priorities, and set a clear direction for the entire branding process.
3–4 Days
Research & analysis
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Model Behaviour & Explainability Mapping
We map what the model does, what it can’t do, and where users need to trust, override, or correct it — before any interface decisions are made.
3–5 Days Explainability map
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Information Architecture
We structure model output hierarchy, control surfaces, and feedback entry points — validated before wireframing begins.
3–5 Days IA structure
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Wireframes
We wireframe key AI flows — input, output, explanation, and feedback — validated before full visual design begins.
3–5 Days Wireframes
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UI Design & Design System
We design every screen for every model state and uncertainty level — and build the component library alongside each feature.
1–3 Weeks UI designs
Engineering Handoff
Annotated Figma files, uncertainty state specs, and component documentation — your engineering team builds without ambiguity.
2–3 Days Handoff
Start with a strategy call where we align on your goals, challenges, and priorities, and set a clear direction for the entire branding process.
3–4 Days
Research & analysis
Industries we serve
UX/UI design for
diverse industries
Healthcare
UX/UI design for hospitals, clinics, and health systems.
Financial Services
UX/UI design for banks, insurance firms, and financial platforms.
Logistics
UX/UI design for logistics, transport, and supply chain companies.
Real Estate
UX/UI design for property developers, agents, and management firms.
Education
UX/UI design for schools, universities, and e-learning platforms.
Web3 & Blockchain
UX/UI design for Web3 startups and blockchain-based products.
Wellness/Fitness
UX/UI design for wellness brands, gyms, and fitness studios.
Information Technology
UX/UI design for tech companies, software products, and platforms.
6 reasons why clients
choose Halo Lab
Team with industry depth
Strategy before design
Custom-only approach
Expertise for complex needs
Clear, collaborative process
Flexible value for any budget
100+ verified
love letters
“Halo Lab is very organized in planning to achieve the goals within the set deadlines. We feel they were part of our internal team project.”
“Their experts have a very diverse range of skills and always find a solution to exposed issues. The communication process was clear.”
“During the collaboration, Halo Lab prioritized our people, always taking into account our input and ideas and giving all team members a voice.”
“The Halo Lab team unified branding across our patients’ app, showcasing professionalism, convenience, and consistency.”
“Halo recreated our existing portal with a modern and beautiful experience, greatly increasing usability and delight for the end user.”
“Halo Lab is brilliant. We were impressed with their timely delivery, cost-effectiveness, and the people who knew what they were doing!”
“The value we get from Halo Lab and their quality designs sets them apart from others. They are the right mix of price, talent, and style.”
“We gave the Halo Lab team a chance, and we immediately knew they were the ones we wanted to work with.”
“The Halo Lab work pleasantly surprised us, and bringing in their professional design team has been a worthy investment for us.”
“When collaborating with Halo Lab, we felt that communication was straightforward and that the deliverables fit what we expected.”
“Halo Lab has helped us with a rebrand and defined a new name, logo, and color palette. We’ve had a great experience working with them.”
12 years
We’ve built one of the most trusted agencies
150+
Specialists in design, engineering & product management
78%
Returning clients in Europe & North America
FAQ
Why invest in branding services?
Why invest in branding services?
What does AI/ML UX design include?
Model output design, explainability UX, uncertainty and error state design, human-in-the-loop flows, AI dashboards, input and prompt interfaces, a design system, and an engineering handoff.
How do you design for AI explainability?
We map what the model outputs, where confidence varies, and what users need to understand to act on results — then design explanation patterns that make model behaviour legible without overwhelming the interface.
How do you design uncertainty states for AI products?
Uncertainty is treated as a design requirement — low confidence scores, edge case outputs, and model errors all have designed states. Users see when the AI is unsure, not just when it’s confident.
Do you design human override and feedback flows?
Yes. Override, correction, and feedback mechanisms are designed as first-class features — giving users meaningful control and closing the loop so corrected outputs improve the model over time.
How long does AI/ML UX design take?
Most AI product design engagements take 8 to 14 weeks from model behaviour mapping to engineering handoff, depending on product complexity, number of model outputs, and whether a full design system is in scope.
Do you design data labelling interfaces?
Yes. Annotation interfaces, task queues, labeller dashboards, and quality control flows are designed for accuracy and speed — the UX quality of labelling tools directly affects the quality of model training data.
Can you work with our existing AI infrastructure?
Yes. We design the interface layer on top of your existing model outputs and APIs — we don’t need to understand the model internals to design an interface that makes its outputs usable and trustworthy.
Can you redesign an existing AI product?
Yes. AI product redesigns are common — we audit how model outputs are currently presented, identify trust and adoption failure points, and redesign with explainability and control as primary constraints.
Can you also develop the AI product interface?
Yes. Full-stack development is available following design — one team handling both design and engineering so the component architecture in Figma maps directly to the production codebase.