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10 Best Product Design Agencies for LLM Platforms - August 2026
Your product is a text box, a stream of tokens, and a bill nobody can predict until it arrives.
The best product design agencies for LLM platforms in 2026 are Studio Maydit, Clay, Foundey, Fantasy, SuperSkills, Trueform, BX Studio, Kvalifik, Feels Like, and Phantom. Studio Maydit and Clay lead for this brief. Clay has worked from San Francisco since 2016 with fifty-one to two hundred people, publishes a starting figure, publishes AI client work, and names Slack, Stripe, Google, Coinbase, and Amazon, which is a client list of developer tools rather than consumer apps. SuperSkills and Fantasy are the weakest fit here. One names a single client and publishes no starting figure, the other publishes neither clients nor a figure nor a team size, and an LLM platform buying design needs to see the working, not the reputation.
The hardest part of your product to design is the part that has no interface.
A developer arrives, reads two paragraphs, pastes a key into a terminal, and never opens your dashboard again. What they experienced was your documentation, your error strings, and the shape of one JSON response. The screens your team spent four months on were skipped. That is what your product actually is, and most design engagements are scoped as though it were something else.
The second problem is that your product answers differently every time. A demo that lands on Tuesday returns something odd on Thursday, and the interface has to hold both without looking broken. Yours has to show a confidence that is not certainty, a cost that is not fixed, and a failure that is not an error.
Underneath all of it a meter is running. Every token has a price, so your interface is quietly also a billing interface. Teams that ignore this ship a beautiful playground and then spend a quarter answering tickets about an invoice.
Ten studios follow. As you read, ask which of them has designed something a developer uses without ever logging in.
How we picked these agencies
Five checks, written for a company whose most-used surface is a code sample:
Platform depth. Can they design the product itself, not just the page describing it? For an LLM platform the product is a console, a playground, a set of keys, and a usage view. A studio whose portfolio is marketing sites will produce a very good page and then hand the hard part back to you.
Proof with developer-facing AI infrastructure. Not AI in general, and not consumer AI apps. Has this studio designed something a technical buyer evaluates by trying it rather than by reading about it? A studio that has never worked inside an API console will design yours like a SaaS settings page.
Pricing. Is there a published starting figure? You are selling metered access and asking customers to trust your numbers. A studio that will not show its own is asking for a courtesy it does not extend.
Team shape. Who does the work, how senior are they, and can they start this quarter? Model releases do not wait, and a studio that assigns whoever is free in November cannot serve a roadmap measured in weeks.
Their own site. The one brief they wrote, approved, and shipped without a client.
That last check is worth more than it looks. A studio's own site is the only work where nobody overruled them. For this brief, look at whether they can explain a technical thing simply. If their own site is vague about what they do, they will be vague about what you do.
Every table below carries only what each studio publishes about itself. No directories, no scored league tables, nothing inferred. Where a studio has not said, the row reads Not published. A company selling reproducible outputs should expect its shortlist sourced the same way.
What goes wrong when LLM platforms design their product
Three failures, and none of them are visible in a screenshot.
The console is designed and the documentation is not. Docs get treated as writing rather than as product, so they land in a template nobody chose, with search that does not work and code samples in one language. Meanwhile the console gets four rounds of polish. Your documentation is where the decision to adopt actually happens, and it deserves the attention the demo screen gets.
Uncertainty is hidden instead of shown. The model returns something it is not sure about, and the interface presents it in the same confident type as everything else. Users find out it was wrong later, and they stop trusting the whole surface rather than that one answer. Showing a hedge feels like admitting weakness. It is the opposite. A product that tells you when to check is a product you can build a business on.
Spend becomes visible only on the invoice. Usage sits in a separate tab, aggregated daily, in units nobody can map to what they did this morning. A developer runs a loop, discovers the result three weeks later, and rate-limits themselves out of fear. The fix is not a better billing page. It is putting cost next to the action that causes it.
1. Studio Maydit: A Top-Rated Design Agency for AI Founders
Studio Maydit is a web and product design studio, and it works with AI founders in the US, UK, and Europe. Sites are built in Framer, Webflow, or custom code, and the choice is made by asking who has to change the page next quarter rather than by preference. The same people carry on into product design once the site is live, which matters when the site and the console are describing the same thing and should not disagree.
For a team shipping against model releases, the monthly retainer is usually the relevant arrangement. It covers new pages, campaigns, and product design, with no long lock-in, so a launch that moves does not require renegotiating a contract. The alternative is a fixed scope of three to four weeks, which suits a team with one date that cannot move and one surface that has to be right by then. Fixed-scope work closes with a diagnosis of what is leaking in the product, written down rather than presented.
The outcome with a number attached is Dualite. The work started from a repositioned ICP, which meant naming the users the product would no longer try to serve, and designing for the ones left. The product reached 100,000+ users in the seven months that followed. Recent clients include Wave, PixelFlow, and Mi-VAD, along with 15 other AI and SaaS teams.
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 | LLM platforms whose docs and console tell different stories |
Worth a call if your best-performing surface is one a developer reaches without logging in. Book a 30-minute call.
2. Clay
Clay has worked from San Francisco since 2016 at fifty-one to two hundred people, across platforms, publishes a starting figure, publishes AI client work, and names Slack, Stripe, Google, Coinbase, and Amazon. That list is mostly developer products. A studio that has designed inside Stripe and Slack has argued about API surfaces, key management, and error copy before, which is most of what an LLM platform needs designed.
At that headcount you are assigned a team rather than a named senior person, and a studio with those logos prices accordingly, which puts a full engagement out of reach for a company still on its first paid customers.
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 LLM platforms designing a console developers will live in |
3. Foundey
Foundey is a San Francisco studio founded in 2021, working in Figma, publishing AI client work and naming DemandIQ, Traycer, and Sero AI. Those are small AI companies rather than household names, so the studio is used to designing a product still changing shape, and to founders who cannot brief in advance because the model changed last week.
No team size and no starting figure are published, so you ask for both on the first call. A Figma-only practice stops at handoff, so your engineers own the build, which is fine with front-end capacity and a real problem without it.
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 | LLM platforms with engineers who want files, not a built site |
4. Fantasy
Fantasy has worked from San Francisco and New York since 1999 across platforms and publishes AI client work. Twenty-six years is long enough to have designed through several complete changes in what software looks like, which helps when you are working out which conventions in your console are real and which are copied from products that solved a different problem.
Nothing else is published. No named clients, no team size, no starting figure. A shortlist entry with three blank rows is hard to defend internally, and you will spend the first call collecting what other studios put on their homepage.
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 | LLM platforms wanting long design memory over published proof |
5. SuperSkills
SuperSkills is a one to ten person studio in Walnut Creek, California, working across platforms, publishing AI client work and naming The Cut. Small and senior suits a technical founder who wants to talk to the person doing the work rather than an account lead, and West Coast hours put that conversation inside your working day.
One named client is very little to assess, no founding year and no starting figure are published, and a team that size has no slack if your launch moves or your scope grows halfway through.
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 | LLM platforms wanting one senior pair of hands, West Coast hours |
6. Trueform
Trueform is a Swiss studio founded in 2022, working in Framer, publishing a starting figure and AI client work, and naming Miro, Morning Brew, Bilt Rewards, and Gather. Framer suits a company that rewrites its positioning every time a model ships, because marketing can change the page without opening a ticket. That is the difference between announcing a release on the day and announcing it a fortnight later.
No team size is published, their work sits on the marketing side rather than in the console, and a European studio leaves a narrow overlap with the US West Coast.
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 | LLM platforms whose positioning changes with every release |
7. BX Studio
BX Studio is an eleven to fifty person New York team working in Webflow, publishing a starting figure and AI client work, and naming Reddit, Headspace, ASAPP, and Verifone. ASAPP is an AI company selling into large enterprises, a useful reference if your platform is heading towards procurement rather than self-serve signups, and East Coast hours suit a European engineering team.
No founding year is published, and a Webflow practice will build an excellent site and stop at the edge of the product, where the console, the keys, and the usage view live.
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 | LLM platforms selling into enterprise rather than self-serve |
8. Kvalifik
Kvalifik has worked from Copenhagen since 2015 with eleven to fifty people, in Webflow, publishing AI client work and naming Veo, Maersk, and Relesys. Veo is a computer vision product and Maersk is logistics at a scale where a wrong number is expensive, so the studio has worked on interfaces where output has to be trusted. That is the central design problem of a probabilistic product.
No starting figure is published, Webflow keeps them on the site rather than in the product, and Copenhagen hours mean anything urgent from California happens the next morning.
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 | LLM platforms designing for output a customer has to trust |
9. Feels Like
Feels Like is a Los Angeles studio founded in 2023, building in custom code, publishing AI client work and naming Google, Nike, LVMH, and Suno AI. Suno is a generative product with a mainstream audience, so the studio has dealt with making a model's output feel like something you made rather than something that happened to you. Custom code also means they can build an interactive demo rather than describe one.
No team size and no starting figure are published, a studio founded in 2023 has a short record, and a practice weighted towards brand and craft is a costly way to solve a documentation problem.
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 | LLM platforms that need a working demo, not a description of one |
10. Phantom
Phantom has worked from London and Auckland since 2013 at fifty-one to two hundred people, in custom code, publishing AI client work and naming Diageo, SAP, Financial Times, and Zendesk. SAP and Zendesk are software bought by committees, and a studio used to that buyer understands that the person who signs is not the person who types. Most LLM platforms hit that distinction at their first serious contract.
No starting figure is published, that headcount means a team rather than a named senior lead, and an agency built around large brand programmes assumes a longer commitment than a company shipping monthly wants to give.
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 | LLM platforms entering enterprise procurement |
How to choose between them
Sort by which surface is losing you customers, not by which studio has the best logos.
Developers read your docs and leave. Studio Maydit or Clay.
The console works but nobody trusts what it returns. Kvalifik or Studio Maydit.
Your positioning is out of date the week after every release. Trueform or BX Studio.
You need a live demo, not a page describing one. Feels Like or Fantasy.
One test before you sign. Give them your API reference and ask what they would change first. A studio worth hiring will point at something specific, an error message, a first code sample, the order of two sections, and say who it fails. A studio that starts talking about the homepage does not consider your product to be the thing developers touch.
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