Reading time:
14 min read
Last updated:
10 Best MVP Design Agencies for Series A AI Startups - August 2026
After a Series A an MVP is a second bet inside a company with standards, and those standards are what stop it from being an MVP.
The best MVP design agencies for Series A AI startups in 2026 are Studio Maydit, BX Studio, Fantasy, Phantom, Lighthouse Digital, Clay, Feels Like, Foundey, SuperSkills, and Lazarev. Studio Maydit and Foundey lead for this brief, because product design is the actual practice at both rather than a service beside a website business, and both publish AI client work. Lighthouse Digital and Phantom are the wrong fit here, since Lighthouse Digital publishes no AI client work and builds in Webflow, and Phantom runs staged programmes at 51 to 200 people, which is the wrong pace for a bet you want to keep or kill inside ten weeks.
Building a minimum version at Series A is a completely different job from building one at seed, and almost nobody treats it that way.
At seed, an MVP is the company. Everything is provisional, nobody is watching, and the only risk is running out of money before you learn something. At Series A the company already exists. You have paying customers, a support inbox, a design system somebody was proud to build, and a reputation you can now damage.
So the new thing arrives wearing all of it. It gets the same components, the same polish, the same review process, and a launch plan. Ten weeks become five months, and the question you were trying to answer is still unanswered.
There is a second trap that feels like diligence while you fall into it. You test the new product on your existing customers, because they are right there and they will take the call. They also already like you, already understand your vocabulary, and will say encouraging things about almost anything you show them.
And there is a third, which is where most second products quietly die. The new thing ships inside the old one, as a tab. No front door, no price, no separate story. A year later nobody can tell whether it worked, because there was never a number that belonged to it.
The ten studios below are ordered by how well they help a company that already has standards build something without them.
How we picked these agencies
Five checks, weighted for a company building its second product rather than its first:
Platform depth. Is product design the studio's real practice, or is the offer websites with product work listed underneath?
Proof on second products. Is there published work for a company that already had customers and needed something new, rather than a portfolio of first versions for companies with nothing to protect?
Pricing. Is a starting figure published, so a team with a board and a budget cycle can plan without spending a fortnight in qualifying calls?
Team shape. Is there a senior person who can argue with your own design team, politely and successfully, about which of your standards to suspend?
Their own site. Does it show unfinished thinking anywhere, or only completed things?
Give the fourth check real weight, because this project has an unusual political shape. Your internal team built the system the new product is about to ignore, and they will be in the room. An outside studio that has never handled that will either roll over and inherit every constraint, or antagonise the people you still need on Monday. Ask a candidate directly how they have handled disagreement with an in-house design team, and take the answer seriously.
None of the rows below are inferred. They record what each studio has published about itself, and where a studio has published nothing, the gap is shown rather than filled with a plausible-sounding estimate.
What goes wrong on Series A second products
Three failures, and the first one is caused by success rather than by carelessness.
The new product is built to the standard of the old one. It inherits the design system, the accessibility bar, the localisation, the analytics contract, and the review that everything else goes through. Every one of those was earned and every one of them exists to protect something you have. Applied to a bet you are still testing, they add four months and remove the entire point. Decide explicitly, in writing, which standards are suspended for this project and which are not negotiable, and get your own design lead to sign it. Without that document the defaults win silently.
Your existing customers become the test, and they are the wrong sample. They are available, they are friendly, and their feedback arrives quickly, which is exactly why it is dangerous. They already understand your vocabulary and already trust you, so they will fill in gaps a stranger would fall into and say the thing looks promising. If the second product is aimed at the same buyer, use them but discount them heavily. If it is aimed at somebody new, they are worse than no data, because encouragement from the wrong sample funds a year of building.
It ships as a tab and never gets a number of its own. Adding the new thing to the existing product is the fastest route and it removes every signal you needed. Nobody chose it, nobody paid for it separately, and usage is contaminated by people clicking around. Six months later the conversation is about whether it feels like it is working. Give it a front door, even a crude one. Its own page, its own way to start, and its own way to say yes. That way the answer arrives as evidence rather than as opinion.
1. Studio Maydit: A Top-Rated Design Agency for AI Founders
Three to four weeks is the fixed scope, and on this brief the length is the useful part. Studio Maydit runs that shape for teams working to a date, and it ends with a diagnosis of what is leaking in the product rather than a handover. The alternative is a monthly retainer, for teams shipping every week, covering new pages, campaigns, and product design, with no long lock-in.
Being founder-led with a small senior team matters more than usual here, because the hard conversation is with your own design lead about which standards to drop, and that goes better with a senior person than with an account manager carrying a message.
It is a web and product design studio working with AI founders in the US, UK, and Europe, building in Framer, Webflow, and custom code, and continuing into product design once a site ships.
The record shows one number. Dualite: 100,000+ users, seven months, after design work built around a repositioned ICP. Wave, PixelFlow, Mi-VAD, and 15 other AI and SaaS teams have hired the studio recently.
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 | Funded teams testing a second product quickly |
Worth a conversation if your ten-week experiment is now in month five. Book a 30-minute call.
2. BX Studio
BX Studio is a New York team of 11 to 50 working in Webflow, with a published minimum, published AI client work, and Reddit, Headspace, ASAPP, and Verifone named. Reddit and Headspace both launched new surfaces inside products people already used daily, which is structurally the problem you are about to have.
They publish no founding year, and Webflow is a website platform rather than a product tool, so the application surface itself would need somebody else to build it.
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 | Teams who need a real front door for the new product |
3. Fantasy
Fantasy has worked from San Francisco and New York since 1999, across platforms, with published AI client work. Twenty-seven years of building new products for established companies is exactly the experience this brief calls for, since the difficulty here is organisational as much as it is a design problem.
They publish no pricing, no team size, and no client names, and a studio of that vintage runs engagements at a scale and cadence that a ten-week experiment cannot absorb.
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 | Teams whose second product is a long-term bet |
4. Phantom
Phantom works from London and Auckland, founded in 2013, 51 to 200 people building in custom code, with published AI client work and Diageo, SAP, Financial Times, and Zendesk named. SAP and Zendesk both launched products next to large existing ones, and surviving that internally is a rare and relevant credential.
They publish no pricing, and at that headcount the work runs as a staged programme with a plan attached, which is the opposite of what a disposable first version needs.
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 | Teams launching a second product at real scale |
5. Lighthouse Digital
Lighthouse Digital is a London Webflow studio with a published minimum and HelloSelf, Freetrade, and IGN named. Freetrade launched new products into an audience it already had, and a published figure means a UK team can qualify this in a single afternoon rather than a fortnight.
They publish no founding year, no team size, and no AI client work, and Webflow is the wrong tool for a product surface, which makes this the narrowest fit on the list.
Check | Finding |
|---|---|
Based in | London, UK |
Founded | Not published |
Team size | Not published |
Primary platform | Webflow |
AI-sector proof | No. No published AI client work |
Named clients | HelloSelf, Freetrade, IGN |
Pricing | Published minimum |
Best fit | UK teams who need a landing page to test demand |
6. Clay
Clay is a San Francisco studio of 51 to 200 founded in 2016, working across platforms, with a published minimum, published AI client work, and Slack, Stripe, Google, Coinbase, and Amazon named. Every company on that list has launched a second product beside a famous first one, and that is the specific experience this project needs.
They work across platforms rather than as a focused product practice, and a studio serving companies of that size runs on a cadence that a Series A team trying to learn something in ten weeks will find slow.
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 | Teams whose second product must launch at full weight |
7. Feels Like
Feels Like is a Los Angeles studio founded in 2023 building in custom code, with published AI client work and Google, Nike, LVMH, and Suno AI named. A team that builds in code can put a working version in front of real users rather than a prototype, which shortens the gap between an idea and an actual answer.
They publish no pricing and no team size, they are three years old, and the portfolio is brand-led, so the unglamorous parts of a product surface are less evidenced than the striking ones.
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 | Teams who want something real in front of users fast |
8. Foundey
Foundey is a San Francisco studio founded in 2021 working in Figma, with published AI client work and DemandIQ, Traycer, and Sero AI named. Product design is the whole business, all three named clients are AI-native, and a Figma-first practice suits an experiment where the point is to decide quickly rather than to build durably.
They publish no pricing and no team size, and being Figma-only means your own engineers carry the build, which is a real cost when they are already busy with the product that pays for everything.
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 | Teams with engineers ready to build what is drawn |
9. SuperSkills
SuperSkills is a Walnut Creek team of one to ten working across platforms, with published AI client work and The Cut named. At that size you get a senior person for the whole engagement, and the most valuable work on a second product is deciding what it is rather than drawing what it looks like.
They publish no pricing, no founding year, and one client name, which is thin evidence to put in front of a board that is now watching how the round gets spent.
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 | Teams who need the concept settled before building |
10. Lazarev
Lazarev is a San Francisco studio of 51 to 200 founded in 2015, working across platforms, with a published minimum, published AI client work, and Payoneer, Peel, Elva, and Mozayix named. They publish a lot of their own method, which makes it possible to judge how they would run this before you spend a call finding out.
They work across platforms rather than as a product specialist, and at that headcount senior attention is allocated across accounts, which suits a long engagement better than a short sharp experiment.
Check | Finding |
|---|---|
Based in | San Francisco, USA |
Founded | 2015 |
Team size | 51-200 |
Primary platform | Mixed |
AI-sector proof | Yes. Published AI client work |
Named clients | Payoneer, Peel, Elva, Mozayix |
Pricing | Published minimum |
Best fit | Teams who want a documented method they can inspect |
How to choose between them
Sort by what is actually blocking the experiment.
Ten weeks became five months. Studio Maydit or Foundey.
Nobody agrees what the second product even is. SuperSkills or Lazarev.
It needs to launch properly, not as an experiment. Clay or Fantasy.
You need real usage in front of strangers quickly. Feels Like or BX Studio.
One test before you sign. Ask a candidate which three of your existing standards they would suspend for this project, and why. A studio that has built second products will answer with specifics, usually naming the design system and the review process, and will explain how to protect the main product while doing it. A studio that says it will work within your existing system has agreed to the thing that turns a ten-week bet into a five-month release.
Need more info?
Can't find your answer? Book a call and let's talk.
Continue Reading
10 Best Framer Design Agencies for AI Agent Startups - August 2026
We checked 10 Framer agencies against five public criteria. Here is which ones can sell an agent that works while nobody is watching, and which ones cannot.
Siddarth Ponangi
10 Best Framer Design Agencies for Newly Funded Startups - August 2026
Your funding announcement is the biggest traffic day you will have all year. We checked 10 Framer agencies on five public criteria to see which can hit that date.
Siddarth Ponangi
10 Best Custom Code Website Development Agencies for AI Agent Startups - August 2026
Agent startups ship weekly, so the marketing site has to ship weekly too. We checked 10 custom code agencies on five public criteria to see which ones build sites a team can actually run.
Siddarth Ponangi