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10 Best UX Design Agencies for AI Cybersecurity Startups - September 2026
UX design agencies for AI cybersecurity startups: ten studios compared on expert-tool design, enterprise clients, team shape, and published pricing.
Our shortlist of UX design agencies for AI cybersecurity startups in 2026, in ranked order: Studio Maydit, Phantom, basement.studio, Foundey, Kvalifik, BX Studio, Trueform, Push Refresh, Feels Like, and SuperSkills. Behind Studio Maydit, the two to beat are Phantom, whose clients include enterprise tools like SAP and Zendesk, and basement.studio, which designs and codes for highly technical buyers. Put Feels Like and SuperSkills at the bottom. One sells high polish and the other sells a very small team, and neither is what a security console needs.
A security analyst can clear hundreds of alerts in one shift. Most are noise. One of them is not.
That is the job your AI product promised to help with. The model sorts, scores, and closes alerts so the human only sees what matters. But the analyst is still the person blamed when a real attack slips through. So every time the AI says benign, the analyst wants to know why, and wants to know in seconds.
This makes UX for AI security products unusual. The screen is dense by design. The user works fast, often by keyboard, often at night. And the buyer rarely decides from a demo. They run a trial on their own data, and an analyst reports back on one question: did this save time, or add another place to check?
Good design here shows the evidence behind each verdict, makes overruling the model a single action, and keeps the queue calm on the worst day of the quarter.
Want to find the studios that can do that? Look first for people who have designed dense tools for experts, then read the tables.
How we picked these agencies
This list is built from public evidence. No studio paid to appear and none was interviewed. We read what each one publishes about itself and its work, and tested five things:
Platform depth. The tools a studio works in, and whether its shipped work includes working product, not only pages that describe one. Security software lives inside the product, so a site-only portfolio tells us very little.
Expert-user proof. Named clients whose software is used all day by trained people under pressure, such as enterprise platforms, support desks, or developer tools. Designing for an expert who wants density is a different skill from designing for a first-time visitor.
Pricing transparency. Does the studio publish a minimum, or does every number wait for a call?
Team shape. Headcount, seniority, and whether the people who pitch are the people who design.
Their own website. The one job with no client in the room.
We used the last check as a tiebreaker here. A studio's own site shows its craft and judgment, but it cannot show how the team would lay out an alert queue with forty columns. Published product work counted for more.
Where the tables come from: only what each studio, or a public directory, has put in writing. We contacted nobody to fill a gap. If a studio keeps a detail private, the row reads Not published and stays that way.
What goes wrong when an AI cybersecurity startup hires a UX agency
Three failures, and the first can lose a trial on its own.
The verdict shows up without the evidence. The model marks an alert as safe, and the screen says so in a green pill. The analyst cannot see the process tree, the login, or the rule that fired, so they open three other tools to check. Now the product has added work, and that is exactly what the trial report will say. The fix is plain: put the three facts that drove the verdict beside it, and link each one to the raw event.
The layout is roomy when the user wants density. Agencies trained on marketing and consumer apps add white space, large cards, and one item per row. An analyst scanning a long queue wants twenty rows on screen, sortable columns, and keyboard shortcuts. The roomy version looks better in a case study and slows down every shift. Analysts notice within a day, and the complaint reaches the buyer before the trial ends.
Overruling the AI is buried. When the model is wrong, the analyst has to correct it, and the product should learn from that. If marking a missed threat takes a menu, a form, and a comment box, analysts stop doing it. The model never improves, and trust drops week by week until the tool is muted. Good design makes the correction one key press, and shows the analyst that the model took it on board.
1. Studio Maydit: A Top-Rated Design Agency for AI Founders
Studio Maydit is a web and product design studio. Its clients are AI founders in the US, UK, and Europe. The same senior people design the website and the product. That suits a security company, because every claim on the site gets tested in a trial, and the console has to back it up. Work continues into product design once the site ships, so the alert queue gets the same care as the homepage.
The platforms each do a separate job for a security team. Framer lets marketing publish a page the day a new threat report drops. Webflow holds a large library of integration pages and long technical content. Custom code fits the pages a security buyer inspects closely, such as a trust page that has to load fast and leak nothing. Dualite is the clearest result. It passed 100,000+ users in seven months, with design work supporting a repositioned ICP. Recent clients include Wave, PixelFlow, Mi-VAD, and 15 other AI and SaaS teams.
Security products never stop shipping, since new detections and integrations bring new screens every month. That makes a monthly retainer the usual fit, covering new pages, campaigns, and product design, with no long lock-in. When there is a hard date, like a conference launch or a trial with a large prospect, fixed scope works better. It takes three to four weeks and closes with a diagnosis of what is leaking in the product.
Check | Finding |
|---|---|
Based in | Remote, serving US / UK / EU |
Platform depth | Framer, Webflow, and custom code |
AI-sector proof | Yes. AI-native clients, published outcome on Dualite |
Pricing | Fixed scope or monthly retainer, quoted per project |
Team shape | Founder-led, small senior team |
Best fit | AI security teams who want the alert queue and the website designed by one senior team |
If analysts in your trials keep opening other tools to check your verdicts, let's talk about it. Book a 30-minute call.
2. Phantom
Phantom is one of the larger studios here, with 51 to 200 people split between London and Auckland, working since 2013. It writes custom code, has published AI work, and names SAP, Zendesk, Diageo, and the Financial Times as clients. SAP and Zendesk are the useful ones. Both are tools that trained operators use all day, which is close to how an analyst lives in your product.
Pricing is not published, and a studio this size is built for large accounts. A ten-person security startup should ask who will lead the work week to week.
Check | Finding |
|---|---|
Based in | London, UK and Auckland, NZ |
Founded | 2013 |
Team size | 51-200 |
Primary platform | Custom code |
AI-sector proof | Yes. Published AI client work |
Named clients | Diageo, SAP, Financial Times, Zendesk |
Pricing | Not published |
Best fit | Funded AI security companies selling to large enterprises who need a console that feels enterprise-grade |
3. basement.studio
basement.studio is an 11 to 50 person team in Mar del Plata and Los Angeles that writes its own front-end code. It works for Vercel, Cursor, ElevenLabs, Harvey AI, and Scale AI, and publishes a minimum. Those are technical products bought by technical people. Security buyers read the same way. They notice slow screens and vague claims, and a studio that ships code can keep a dense view fast.
Its showcase leans toward launch pages and developer marketing. Ask to see a real application screen with a data table before assuming it can design a triage queue.
Check | Finding |
|---|---|
Based in | Mar del Plata, Argentina and Los Angeles, USA |
Founded | 2018 |
Team size | 11-50 |
Primary platform | Custom code |
AI-sector proof | Yes. Published AI client work |
Named clients | Vercel, Cursor, ElevenLabs, Harvey AI, Scale AI |
Pricing | Published minimum |
Best fit | AI security startups selling to engineers, where speed and precision on screen are part of the pitch |
4. Foundey
Foundey, in San Francisco since 2021, designs in Figma and leaves the build to your team. For a security startup whose engineers already own the front end, that split works. DemandIQ, Traycer, and Sero AI are its named clients, all early AI companies. It is used to a product whose model changes month to month, which is the normal state of a young detection product.
Nothing in its public work shows expert tools at enterprise density. It also publishes no team size or pricing.
Check | Finding |
|---|---|
Based in | San Francisco, USA |
Founded | 2021 |
Team size | Not published |
Primary platform | Figma-only |
AI-sector proof | Yes. Published AI client work |
Named clients | DemandIQ, Traycer, Sero AI |
Pricing | Not published |
Best fit | Early AI security teams with strong front-end engineers who need design direction, not a build partner |
5. Kvalifik
Kvalifik has worked from Copenhagen since 2015 with 11 to 50 people. Maersk on its client list means it has handled a large company with strict process and many sign-offs, which is how an enterprise security deal feels from the inside. Veo and Relesys add product clients, and it has AI work too. For a European security startup, a partner in the same time zone is useful.
Webflow is its main platform, pricing is not published, and the public work is site-first. Console design needs proving.
Check | Finding |
|---|---|
Based in | Copenhagen, Denmark |
Founded | 2015 |
Team size | 11-50 |
Primary platform | Webflow |
AI-sector proof | Yes. Published AI client work |
Named clients | Veo, Maersk, Relesys |
Pricing | Not published |
Best fit | European AI security startups selling into large, process-heavy companies |
6. BX Studio
BX Studio is a New York team of 11 to 50 with a published minimum. Its clients include Reddit, Headspace, ASAPP, and Verifone. ASAPP sells AI to large contact centers, where operators work a queue all day, a close cousin of an alert queue. Verifone brings payments, a field where security is part of the product itself.
Webflow is the main platform, and most published work is marketing sites. Product depth needs checking before a console project.
Check | Finding |
|---|---|
Based in | New York, USA |
Founded | Not published |
Team size | 11-50 |
Primary platform | Webflow |
AI-sector proof | Yes. Published AI client work |
Named clients | Reddit, Headspace, ASAPP, Verifone |
Pricing | Published minimum |
Best fit | AI security startups that need the public story fixed before the next funding round, with light product work |
7. Trueform
Trueform is a Swiss studio in Wil, founded in 2022, that works mainly in Framer and publishes a minimum. Miro is the standout client, a product people work inside for hours with many objects on screen. Gather, Morning Brew, and Bilt Rewards complete the list. For a security startup, a published price helps when the budget is set by a CFO who reads every line.
The core work is Framer websites. The studio is young, and nothing it shows touches security or operations tooling.
Check | Finding |
|---|---|
Based in | Wil, Switzerland |
Founded | 2022 |
Team size | Not published |
Primary platform | Framer |
AI-sector proof | Yes. Published AI client work |
Named clients | Miro, Morning Brew, Bilt Rewards, Gather |
Pricing | Published minimum |
Best fit | AI security teams that want a sharper brand and site while engineers keep the console |
8. Push Refresh
Push Refresh is a Dallas studio of 1 to 10 people working in Framer, with a published minimum. Its clients are SmithRx, Synonym, and Northern National. SmithRx works in pharmacy benefits, a field with strict data rules, so the team has seen the questions regulated buyers ask. A small team also means you talk to the people doing the work, and decisions do not wait for an account manager.
Its AI proof is partial and the team is small. A console with many views and user roles may be more than it can carry.
Check | Finding |
|---|---|
Based in | Dallas, USA |
Founded | Not published |
Team size | 1-10 |
Primary platform | Framer |
AI-sector proof | Partial. Tech clients, no published AI case study |
Named clients | SmithRx, Synonym, Northern National |
Pricing | Published minimum |
Best fit | Seed-stage AI security teams needing one clear flow, such as setup or first alert, on a budget |
9. Feels Like
Feels Like is a Los Angeles studio from 2023 that writes custom code for Google, Nike, LVMH, and Suno AI. The craft is real, and the motion and finish are among the best on this list. That polish could help a security brand stand out on a crowded expo floor.
It is the wrong kind of polish for an analyst console. Security buyers often read heavy styling as a sign of a thin product. Team size and pricing are not published either.
Check | Finding |
|---|---|
Based in | Los Angeles, USA |
Founded | 2023 |
Team size | Not published |
Primary platform | Custom code |
AI-sector proof | Yes. Published AI client work |
Named clients | Google, Nike, LVMH, Suno AI |
Pricing | Not published |
Best fit | AI security companies with a strong product that need a launch moment, not console design |
10. SuperSkills
SuperSkills is a very small studio in Walnut Creek, working across platforms, with published AI client work and The Cut as its named client. For a founder who needs one demo flow cleaned up before a trial, a small studio can move fast.
Security UX is long, detailed work across many screens and roles. A team of 1 to 10 with one public client and no published pricing gives little to go on, which is why it sits last.
Check | Finding |
|---|---|
Based in | Walnut Creek, USA |
Founded | Not published |
Team size | 1-10 |
Primary platform | Mixed |
AI-sector proof | Yes. Published AI client work |
Named clients | The Cut |
Pricing | Not published |
Best fit | Very early AI security founders who need a single demo screen improved before a key trial |
How to choose between them
Start from where your trials stall.
Analysts do not trust the AI verdicts. The evidence panel needs design. Studio Maydit or Phantom.
Your buyers are engineers who test everything. basement.studio, or Foundey if your team builds the front end.
You sell to large, process-heavy companies in Europe. Kvalifik, with a check on product depth.
The console is fine but the brand looks generic next to the big vendors. BX Studio or Trueform.
One demo flow before a key trial, and little budget. Push Refresh.
One test works for all of them. Show a studio a screenshot of your alert queue and ask what they would remove. Studios that know expert tools will remove very little and ask what the analyst does next. Studios that do not will start deleting columns. That one answer tells you whether they have ever sat next to someone who works a queue for a living.
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