Summarize with AI
If your first version has to convince a security team that a model is worth listening to, start with three names: Studio Maydit, Phantom, and Clay. The full top ten, in order, is Studio Maydit, Phantom, Clay, Feels Like, Trueform, Kvalifik, Feely Studio, SuperSkills, Fantasy, and Lighthouse Digital. Phantom and Clay lead the competitors because both have shipped software for organisations where being wrong is expensive. Fantasy and Lighthouse Digital close the list, one publishing nothing you can verify, the other with no AI work at all.
Security buyers are the most suspicious audience in software, and they are right to be. They have been sold dashboards that light up red and mean nothing. So a first version that looks confident is a liability.
The core problem is alert volume. A model that flags things will flag too many, and an analyst working a queue has seconds per item. Version one has to make triage fast: what happened, why it was flagged, how sure the system is, and what to do next, all readable without opening three tabs.
The second problem is proof. An analyst who cannot see the underlying evidence will not escalate on your say-so, because their name goes on the escalation. Every conclusion needs its raw material one click away.
Third, these products live inside a workflow you do not own. Your users already have a ticketing system, a chat tool, and a runbook. A first version that ignores those adds work instead of removing it.
So the studio you want has designed a queue, a detail view, and an evidence trail. Not a hero section with a shield in it.
Each entry below ends with a table you can scan in under a minute.
How we picked these agencies
No fee was paid for a place here and no studio was interviewed. All of it comes from public material: each team's own site, published case studies, and directory records. The same five questions were asked of every one.
- Platform depth. Does the studio design working software with queues, records, and detail views, or mainly marketing sites? A security product is a list of things to look at, and a list is harder to design well than a homepage.
- High-stakes and technical-buyer proof. Has the team shipped for organisations where a mistake is costly, or for buyers who evaluate rather than browse? That teaches understatement, which is the correct register for this market.
- Pricing transparency. Is a starting price published? Security founders are used to vendors who hide numbers. A studio that publishes one is easier to judge and easier to compare.
- Team shape. How many people, and who works on your project? On a short build, one senior person who understands the domain beats a large team that does not.
- The agency's own website. The one project with nobody else responsible.
The fifth check matters here in a narrow way. Look at whether the studio overclaims about itself. A team that describes its own work in superlatives will write your product page the same way, and that tone actively loses security buyers.
Where the facts come from: public studio pages and directory listings as at September 2026, with nothing filled in by correspondence. If a team publishes no headcount or founding year, the row says Not published.
What goes wrong when an AI security startup buys MVP design
Three failures, and each one is caught by an analyst in the first week of a trial.
The dashboard is the product. Version one is a page of charts and a severity donut. It looks impressive in a demo and tells an analyst nothing they can act on. What they need is a working queue with sensible defaults and a fast path into one item. Ask the studio to show you a list view it has designed, not a dashboard.
Findings arrive without evidence. The screen says a thing is suspicious and gives a score. It does not show the log line, the request, or the behaviour that triggered it. An analyst who cannot verify will not escalate, so your product becomes a second opinion nobody acts on. Put the raw evidence one click from every conclusion.
It ignores the tools already in use. The product expects analysts to live inside it, when they live in a ticketing system and a chat channel. Without a way to push a finding outward with its context attached, using your tool costs them time. Design the exit path in version one, even as a simple copy of the summary.
1. Studio Maydit: A Top-Rated Design Agency for AI Founders
Studio Maydit is a web and product design studio working with AI founders in the US, UK, and Europe. It builds in Framer, Webflow, and custom code, then carries on into product design after the site ships. For a security product that continuity is practical: the same team writes the page a buyer reads and designs the queue an analyst works in, so the product does not promise one thing and show another.
There is one published outcome. Dualite reached 100,000+ users within seven months, with design work supporting a repositioned ICP behind part of it. Recent clients include Wave, PixelFlow, Mi-VAD, and 15 other AI and SaaS teams.
Engagements take two forms. Fixed scope runs three to four weeks and suits teams with a launch or a conference date. A monthly retainer suits teams still reshaping the product, covering new pages, campaigns, and product design, with no long lock-in. Fixed-scope work ends with a diagnosis of what is leaking in the product, which for a security tool usually means naming the point in the queue where analysts stop looking.
| 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 | Security teams who need the queue, the evidence, and the handoff designed before a trial |
Bring a screenshot of your alert list and one real finding. Book a 30-minute call.
2. Phantom
Phantom has 51 to 200 people across London and Auckland, founded in 2013, writing custom code, with published AI work for Diageo, SAP, the Financial Times, and Zendesk. SAP and Zendesk are systems where trained staff work a queue all day, which is precisely the interface pattern a security product lives or dies by.
Pricing is not published, and a studio this size runs a structured process that can feel slow against a startup's timeline. Confirm the pace and the named team before signing.
| 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 security teams building a tool analysts will work in all day |
3. Clay
Clay is a San Francisco studio founded in 2016, with 51 to 200 people, a published minimum, and published AI work for Slack, Stripe, Google, Coinbase, and Amazon. Coinbase and Stripe both operate where fraud and abuse are constant, so the team has designed interfaces that treat risk as routine rather than dramatic.
There is no published security-vendor work, and this is a premium studio with very large clients. A seed-stage first version will be a small engagement here, so ask what it staffs those with.
| Check | Finding |
|---|---|
| Based in | San Francisco, USA |
| Founded | 2016 |
| Team size | 51-200 |
| Primary platform | Mixed |
| AI-sector proof | Yes. Published AI client work |
| Named clients | Slack, Stripe, Google, Coinbase, Amazon |
| Pricing | Published minimum |
| Best fit | Funded security teams who need risk shown calmly and at a high standard of craft |
4. Feels Like
Feels Like is a Los Angeles studio founded in 2023 writing custom code, with published AI work and clients including Google, Nike, LVMH, and Suno AI. Building in code rather than a site tool means the team can prototype a real interface with live data, which is the only honest way to test a triage view.
It is young, publishes neither team size nor pricing, and has no security client on the public list. Ask specifically what it has designed that had a working list view in it.
| 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 | Security teams who want a live, coded prototype rather than static screens |
5. Trueform
Trueform is a Swiss studio founded in 2022 working in Framer, with published AI work, a published minimum, and clients including Miro, Morning Brew, Bilt Rewards, and Gather. A Swiss base is a small but real signal for a security vendor selling into Europe, where the work is done is a question buyers actually ask.
Team size is not published and the studio is young. Framer is a marketing tool, so the analyst-facing product is not what this team shows.
| 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 | European security teams who need a credible public site before the product surface grows |
6. Kvalifik
Kvalifik is a Copenhagen team of 11 to 50 founded in 2015 working in Webflow, with published AI work for Veo, Maersk, and Relesys. Maersk is a large enterprise with a real procurement process, so the team has produced work that had to pass an organisation's review rather than just a founder's taste.
Pricing is not published, there is no security client, and Webflow is the main platform. For the analyst-facing half of a product, evidence is thin.
| 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 security teams selling into large organisations who need enterprise-aware messaging |
7. Feely Studio
Feely Studio is a small distributed European team of 1 to 10 with published AI work, a published minimum, and clients including Noxus, Mutiny, Luasai, and Basic Capital. A visible price plus a senior pair of hands is a workable combination for a pre-seed team with a narrow first scope.
No founding year is published and there is no security work. A team this size cannot run analyst interviews, and those interviews are what separate a usable queue from a plausible one.
| Check | Finding |
|---|---|
| Based in | Distributed, Europe |
| Founded | Not published |
| Team size | 1-10 |
| Primary platform | Mixed |
| AI-sector proof | Yes. Published AI client work |
| Named clients | Noxus, Mutiny, Luasai, Basic Capital |
| Pricing | Published minimum |
| Best fit | Early European security teams with a fixed budget who bring their own domain expertise |
8. SuperSkills
SuperSkills is a Walnut Creek team of 1 to 10 with published AI work, whose named client is The Cut. The size means direct access to a senior designer, with no account layer between you and the work.
It ranks eighth on checkable evidence. No pricing, no founding year, and a single named client is not much for a founder whose buyers will ask who built the product and why they should believe it.
| 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 | Early AI teams who want one senior designer and are not yet in enterprise sales |
9. Fantasy
Fantasy has worked from San Francisco and New York since 1999 across several platforms, with published AI work. The studio has genuine longevity and strategic depth, and experience introducing unfamiliar products to large audiences.
It ranks ninth on what can be verified. No client names, no team size, and no pricing are published. Security buyers evaluate vendors by evidence, and a founder in that market should notice when a supplier offers none.
| Check | Finding |
|---|---|
| Based in | San Francisco and New York, USA |
| Founded | 1999 |
| Team size | Not published |
| Primary platform | Mixed |
| AI-sector proof | Yes. Published AI client work |
| Named clients | Not published |
| Pricing | Not published |
| Best fit | Well-funded security teams who will do reference checks privately rather than from a portfolio |
10. Lighthouse Digital
Lighthouse Digital is a London Webflow studio with a published minimum, for HelloSelf, Freetrade, and IGN. The published floor and a UK base make it straightforward to engage for a simple site build.
It ranks last here. There is no published AI work, no team size, and no founding year, and the portfolio is marketing sites. A security product's first version is an analyst tool, and there is nothing in the public record to suggest that is what this studio does.
| Check | Finding |
|---|---|
| Based in | London, UK |
| Founded | Not published |
| Team size | Not published |
| Primary platform | Webflow |
| AI-sector proof | No |
| Named clients | HelloSelf, Freetrade, IGN |
| Pricing | Published minimum |
| Best fit | UK teams who want a straightforward marketing site at a price they can see |
How to choose between them
Sort by where analysts are losing time, not by whose portfolio looks sharpest.
The queue is unusable and nobody works it. You need triage designed. Studio Maydit or Phantom.
Findings look alarming and mean nothing. You need risk shown calmly, with evidence attached. Clay.
You need to test a real triage view with live data. Feels Like.
European buyers are asking where the work happens. Trueform or Kvalifik.
Then run one test on the first call. Ask what they would put on the row of a single alert in a list. A studio that has designed for analysts names four or five fields and explains what each one decides. A studio that has not will describe a colour system, which is how you end up with a product that looks urgent and gets ignored.
