Visual design for AI products is a different problem from visual design for standard digital products.
The interface has to signal competence and trustworthiness at first glance — before a user has interacted with anything. It has to communicate system state and confidence without overwhelming users with technical information. It has to make AI behavior feel legible and appropriately predictable to people who may have never used a product like this before.
An ai graphic designer working on AI products isn’t just making things look good. They’re making complex, probabilistic systems feel approachable and credible to audiences that didn’t necessarily sign up for AI.
1. Nataliya Sambir — Linkup ST
Years of experience: 12
Primary focus: UX, Conversion Optimization, Emotional Design
Key services: UI/UX Design, Product Design, UX Consulting
Location: Europe
Nataliya is the kind of ai graphic designer whose visual decisions are always answerable to a business metric. The Emotional-Functional Framework she works within at Linkup ST runs aesthetic and functional tracks simultaneously — visceral design that creates immediate credibility, behavioral design that makes AI interactions clear and predictable, reflective design that makes users feel augmented rather than managed. For AI products where the visual layer is also a trust layer, that three-level approach produces work that goes further than surface quality.
Twelve years of practice. 40+ global recognitions including Red Dot, Webby, and Apple. Work that’s reached 70M+ users worldwide.
Linkup ST UI/UX design services
2. Darya Ebadian — Linkup ST
Years of experience: 5+
Primary focus: UI/UX Design, Product Design
Key services: UI/UX Design, Product Design, Mobile App Design, UI/UX Audit
Location: Kyiv, Ukraine
Darya leads UI/UX at Linkup ST across web, mobile, and product design. As an ai graphic designer, her particular strength is in the translation layer — taking AI capabilities that are technically complex and making them feel visually clear and appropriately approachable to users encountering them for the first time. Background spans Lynksen, Dodotap, and Artman Studio. Works within the Emotional-Functional Framework, which keeps visual decisions accountable to measurable outcomes.
3. Andrii Hadai — Lazarev.Agency
Years of experience: 15
Primary focus: AI Product Design, Creative Direction
Key services: UI/UX Design, Creative Direction, AI Product Interfaces, Design Systems
Location: United States
Fifteen years of design practice, currently heading the department at Lazarev.Agency. The visual portfolio for AI products is worth looking at directly — AI compliance dashboards, robotics control interfaces, smart farming platforms, neuro-sensory robotics UI. These are environments where visual design has to communicate system state, confidence levels, and human-override capability without creating cognitive overhead. European Design Award Gold as Creative Director on Lazarev’s own site.
4. Ben Shih — Miro
Years of experience: 7+
Primary focus: AI Product Design, Growth Design
Key services: AI Feature Design, Onboarding Design, Product Growth, Conversion Optimization
Location: Amsterdam, Netherlands
Portfolio: benshih.design
Ben’s data science background informs how he thinks about the visual layer of AI products — not as decoration but as a communication system that has to convey probabilistic outputs to users who need to trust them. At Miro he redesigned the visual experience of AI feature entry points across the product. At Lokalise he turned a spreadsheet-heavy manual process into an AI-first visual workflow. Available for consulting alongside his staff role.
5. Kirill Lazarev — Lazarev.Agency
Years of experience: 10+
Primary focus: AI Digital Product Design, Startup Design
Key services: AI Product Design, UX Strategy, MVP Design, SaaS Design
Location: San Francisco, CA
Kirill built Lazarev.Agency into one of the more recognized AI design practices globally — 120+ awards including five Webby Awards and six Red Dot Awards, $500M raised for clients through design work. The agency’s visual quality for AI products is consistently strong across fintech, healthcare, Web3, SaaS, and AI-native products. AI design practice active since 2018.
6. Roman Kaminechny — Cieden
Years of experience: 10+
Primary focus: B2B SaaS, AI UX, Design Systems
Key services: UI/UX Design, Design Systems, UX Audits, AI Feature Design
Location: Lviv, Ukraine
As an ai designer with deep B2B SaaS experience, Roman’s visual work for AI products is grounded in the enterprise context — interfaces that have to work for professional users across multiple roles, at different levels of technical sophistication, within products they use eight hours a day. The visual layer has to serve all of them without patronizing the expert users or overwhelming the new ones. Ten years of practice across design systems, UX analytics, and application design.
7. Ioana Teleanu — AI-R Design Studio
Years of experience: 12+
Primary focus: AI Product Design, AI UX Strategy, Consulting
Key services: AI Product Design, AI UX Consulting, Design Leadership
Location: Bucharest, Romania (remote)
Ioana’s visual AI product work includes Clipboard AI at UiPath — Time Magazine Best Invention of 2023 — and the AI experience at Miro where she was the first designer on the AI team. US Design Patents for AI product visual design work. The visual layer she brings to AI products is informed by 12+ years of thinking specifically about how AI behavior gets communicated through interface design. Consulting clients include Anthropic, Framer, Adobe, Notion, ElevenLabs.
8. Iryna Serednia — Cieden
Years of experience: 10+
Primary focus: UX Strategy, Healthcare & AI Product Design
Key services: UX/UI Design, UX Strategy, Product Design, AI Healthcare Interfaces
Location: Cochrane, Canada (remote)
Co-founded Cieden. Her visual design work for AI healthcare products operates under the most demanding constraints in the category — interfaces that have to feel trustworthy and clear to clinical professionals making consequential decisions, within regulatory environments that constrain what can be communicated and how. That level of constraint produces visual design instincts that transfer well to other high-stakes AI business contexts. Available remotely across North American and European time zones.
9. Andrew Sapkowski — Cieden
Years of experience: 6+
Primary focus: Product Design, AI UX, B2B SaaS
Key services: UI/UX Design, Product Design, Design Systems
Location: Ukraine
Product designer at Cieden working on B2B SaaS and AI-enabled products across healthcare, fintech, and enterprise software. His visual work covers the full lifecycle from wireframes through high-fidelity design and design systems — the implementation-ready layer that development teams actually build from. Part of the team actively developing AI visual patterns for integrating AI features into enterprise products without disrupting existing interface conventions users rely on.
10. Pavlo Savchenko — Linkup ST
Years of experience: 8
Primary focus: UI/UX, Conversion Optimization
Key services: Conversion Optimization, UI/UX Design, Product Design, UI/UX Audit
Location: Ukraine
Pavlo’s visual design work is audit-driven — grounded in identifying exactly where the interface loses users and fixing it against specific business metrics. For AI products, that visual audit capability addresses the specific failure modes that erode trust: inconsistent AI state communication, visual ambiguity about what the system is doing, error states that undermine confidence rather than guiding recovery. Eight years at Linkup ST inside the performance model that embeds him in client workflows on an ongoing basis.
How to Find the Right AI Graphic Designer for Your Project
Understand what visual design needs to accomplish for your AI product
For AI businesses, visual design serves multiple functions that don’t exist in standard product design. It needs to signal credibility to enterprise buyers before they’ve interacted with anything. It needs to communicate AI system state and confidence to users who may be skeptical. It needs to make probabilistic outputs feel appropriately certain without over-promising. Define which of these functions matters most before you start evaluating anyone.
Look beyond portfolio aesthetics to visual communication quality
The question isn’t whether the work looks good. It’s whether the visual design communicates clearly under the specific conditions of AI product use — variable outputs, uncertain confidence levels, novel interaction patterns. Ask designers to walk you through how visual decisions in their AI work solve specific communication problems, not just how they achieved a particular aesthetic.
Evaluate their approach to visual trust signals
For AI products, visual trust isn’t about looking premium — it’s about looking appropriately capable and honest. That means visual hierarchy that surfaces AI confidence accurately, error states that communicate clearly without alarming users unnecessarily, and consistency signals that make AI behavior feel predictable. Ask specifically how a designer has approached visual trust in past AI product work.
Check whether they understand the business context of visual design
The best ai graphic designers for AI businesses understand that visual design affects enterprise deal conversion, investor perception, and user adoption simultaneously. They design with all three audiences in mind, not just the end user. Ask how they’ve thought about the business development dimension of visual design for AI products specifically.
Consider design system depth alongside individual design quality
AI products grow. New features get added, new AI capabilities get surfaced, the product expands into new user contexts. A designer who only delivers high-fidelity mockups without a supporting design system creates technical debt that compounds as the product scales. For AI businesses planning sustained product growth, design system capability alongside individual visual quality matters.
Look at how they handle AI-specific visual problems
How does the designer visualize AI processing states without creating anxiety? How do they communicate AI confidence levels visually without overwhelming users with percentages and probabilities? How do they design visual feedback for AI actions that are reversible versus irreversible? These are the questions that separate top AI design companies with genuine AI visual expertise from those applying general design skill to a new category.
