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10 Best Website Design Agencies for AI Search Products - September 2026
Ten studios that build websites for AI search products, compared on published pricing, team size, AI-sector proof, and who can sell against a free default nobody complains about.
For an AI search product picking a website studio, ten are worth reviewing: Studio Maydit, basement.studio, Lazarev, Clay, Feely Studio, Kvalifik, Feels Like, SuperSkills, Foundey, and Fantasy. The two strongest here are basement.studio and Lazarev. basement.studio builds in custom code from Mar del Plata and Los Angeles, has since 2018, and works for Vercel, Cursor, ElevenLabs, Harvey AI, and Scale AI. Lazarev is a fifty-one to two hundred person San Francisco team, founded 2015, working across platforms for Payoneer, Peel, Elva, and Mozayix. Both publish a starting price and both carry AI-sector proof. Foundey and Fantasy sit at the other end, the first because it only designs and does not build, the second because it publishes neither clients nor pricing.
Everybody already knows what search is, and that is your problem.
A visitor arrives with a fully formed mental model. Search is a box you type into, and they have used one every day since they were a child. Your product gets filed under that model in about two seconds, and once it is filed there, the interesting thing you do becomes invisible. You are not introducing a new category. You are trying to dislodge one of the most established ideas in computing, which is much harder work and almost nobody budgets for it.
The second problem is that your quality only exists on somebody else's data. A demo over your sample corpus proves nothing, because the visitor's honest reaction is that of course it works on the documents you picked. The thing that would convince them is watching it work on their own messy, badly named, half-duplicated files, and that cannot happen on a marketing page.
Then there is the price you are competing against, which is nothing. There is already a search box in the tool your buyer uses. It is bad, everyone has quietly adapted to it, and nobody has ever filed a ticket about it. Free and tolerated is a genuinely difficult competitor, because the switch requires someone to admit that a thing they stopped noticing is costing them time.
How we picked these agencies
Five checks produced this ranking, and each is answerable from public pages before you commit an hour to a call.
Platform depth, judged against how much of your site is a demonstration rather than a description. Search is a product people need to see behaving, and the difference between an animated sequence and something a visitor can actually type into is a platform decision as much as a design one.
Proof with AI products where quality is subjective. This is the criterion doing the most work here. Selling search means persuading someone that results are better, and better is not a specification. A studio that has faced that before knows the answer is comparison and specificity rather than assertion, and a studio that has not will write the word relevant several times and consider the problem solved.
Pricing. Whether a starting figure is public, rather than what it is. It is a small signal of whether a studio will commit to something concrete, which is the exact habit your page needs.
Team shape. Whether the senior person who understood your retrieval approach also writes about it. Search claims get vague quickly when passed between people, and vague is fatal in a category where everyone assumes they already understand the product.
Their own website. No client, no constraint, so it shows what the studio does when it is the one deciding.
Every row below traces to something the studio publishes about itself, with nothing estimated or inferred. A Not published entry therefore records a choice rather than a missing answer.
What goes wrong for AI search products
Three failures happen repeatedly, and the first one is why so many search products feel indistinguishable.
The page shows a search box. It is the most literal possible illustration and it triggers exactly the mental model you are trying to escape. A visitor sees a box, thinks search, and stops paying attention. What works is showing the question and the answer, or the before and after of someone finding something they previously could not, with the box treated as incidental rather than as the hero.
The demo runs on data nobody believes. A curated corpus produces perfect results and the visitor discounts all of them, correctly, because they know their own files are nothing like that. The stronger move is to show the product handling something visibly messy, including a query where the answer is imperfect and the product says so. Demonstrating a limit is what makes the successful cases credible.
Nothing on the page acknowledges the free alternative. The site compares against other vendors while the real competitor is the search box already sitting in the tool your buyer uses, which costs nothing and which nobody complains about. Until the page makes the current cost visible, in minutes lost or in things never found, there is no reason to switch, and the whole comparison is being run in the wrong bracket.
1. Studio Maydit: A Top-Rated Design Agency for AI Founders
For a search product the marketing site and the first session are almost the same argument, which is why Studio Maydit carries on into product design after the site ships rather than stopping at launch. A visitor persuaded by a demonstration then indexes their own documents and forms a second, more durable opinion, and if those two experiences disagree the first one counts for nothing. It is a web and product design studio, its clients are AI founders, and it works across the US, UK, and Europe.
Framer fits while the positioning is still moving. Webflow fits once a marketing hire is publishing regularly. Custom code fits when a page needs a visitor to type a query and see a real answer, which for search is often the thing that actually sells it.
The published outcome on Dualite is 100,000+ users in seven months, and it began with a repositioned ICP rather than with a redesign. That order matters unusually much for search, because a search product can index almost anything and the temptation is to say so. Naming one kind of document, for one kind of person, is what stops the page collapsing back into the generic idea everyone already holds. Recent clients include Wave, PixelFlow, Mi-VAD, and 15 other AI and SaaS teams.
Buying splits two ways. Fixed scope runs three to four weeks and suits a team with a launch date already fixed. The monthly retainer suits a company still learning which use case converts, since it covers new pages, campaigns, and product design as that understanding sharpens, and it carries no long lock-in. A fixed-scope build ends with a diagnosis of what is leaking in the product rather than a handoff and goodbye.
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 | Search products being filed under an idea the visitor already has |
Show the answer, not the box. Book a 30-minute call.
2. basement.studio
basement.studio has worked from Mar del Plata in Argentina and Los Angeles since 2018 with eleven to fifty people in custom code, and publishes a starting price. Its client list is the most relevant here: Vercel and Cursor both had to make a technical product feel immediate on a web page, and custom code means a visitor could actually type a query on your site rather than watch a recording of one.
The weakness is the running cost. Every copy change goes through an engineer, and a search company usually spends its first year rewording what it claims.
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 |
Named clients | Vercel, Cursor, ElevenLabs, Harvey AI, Scale AI |
Pricing | Published minimum |
Best fit | Search products where a live, typeable demo is what closes the visitor |
3. Lazarev
Lazarev has worked from San Francisco since 2015 with fifty-one to two hundred people across mixed platforms, publishes a starting price, and holds AI-sector proof. Its portfolio sits in complex software with real interfaces, which is what you need when the argument depends on showing a result rather than describing a capability.
The weakness is the usual consequence of size. A team gets assigned, and the person who understood how your retrieval actually differs may not be the one writing the sentence that has to convey it.
Check | Finding |
|---|---|
Based in | San Francisco, USA |
Founded | 2015 |
Team size | 51-200 |
Primary platform | Mixed |
AI-sector proof | Yes |
Named clients | Payoneer, Peel, Elva, Mozayix |
Pricing | Published minimum |
Best fit | Search products with a real interface that has to be shown working |
4. Clay
Clay has worked from San Francisco since 2016 with fifty-one to two hundred people across mixed platforms, publishes a starting price, and holds AI-sector proof. Google is on its client list, which is a slightly awkward and genuinely useful reference for a company selling search, and Slack, Stripe, Coinbase, and Amazon add the kind of credibility that shortens an enterprise conversation.
The weakness is stage fit. This is a premium practice built for clients who bring their own brand team, so an early search company will be its smallest engagement.
Check | Finding |
|---|---|
Based in | San Francisco, USA |
Founded | 2016 |
Team size | 51-200 |
Primary platform | Mixed |
AI-sector proof | Yes |
Named clients | Slack, Stripe, Google, Coinbase, Amazon |
Pricing | Published minimum |
Best fit | Funded search companies selling into enterprises that need reassurance |
5. Feely Studio
Feely Studio is a distributed European team of one to ten people across mixed platforms, with AI-sector proof and a published starting price. Mutiny among its clients is a product built around conversion, and at this size the person who hears you explain why your results are better is the person who has to write it down.
The weakness is capacity. A team of one to ten works sequentially, so a large page set or a fixed launch window may simply not fit.
Check | Finding |
|---|---|
Based in | Distributed, Europe |
Founded | Not published |
Team size | 1-10 |
Primary platform | Mixed |
AI-sector proof | Yes |
Named clients | Noxus, Mutiny, Luasai, Basic Capital |
Pricing | Published minimum |
Best fit | Early search teams who need the positioning narrowed before anything is built |
6. Kvalifik
Kvalifik has worked from Copenhagen since 2015 with eleven to fifty people in Webflow, and holds AI-sector proof. Maersk and Relesys are organisations with large document estates and genuine retrieval problems, which is closer to your buyer's daily reality than a consumer reference would be.
The weakness is disclosure and distance. No starting price is published, and a US search company gets one live overlap window a day with a Copenhagen team.
Check | Finding |
|---|---|
Based in | Copenhagen, Denmark |
Founded | 2015 |
Team size | 11-50 |
Primary platform | Webflow |
AI-sector proof | Yes |
Named clients | Veo, Maersk, Relesys |
Pricing | Not published |
Best fit | European search products selling into document-heavy organisations |
7. Feels Like
Feels Like has worked from Los Angeles since 2023 in custom code, with AI-sector proof through Suno AI alongside Google, Nike, and LVMH. In a category where every site opens with the same box on the same gradient, this is the studio most likely to produce something that does not get filed alongside the others.
The weakness is what is unknown and what is emphasised. Neither team size nor a starting price is published, and the strength is brand rather than the patient work of proving that results are better.
Check | Finding |
|---|---|
Based in | Los Angeles, USA |
Founded | 2023 |
Team size | Not published |
Primary platform | Custom code |
AI-sector proof | Yes |
Named clients | Google, Nike, LVMH, Suno AI |
Pricing | Not published |
Best fit | Consumer search products that need to escape a generic category look |
8. SuperSkills
SuperSkills is a one to ten person team in Walnut Creek working across mixed platforms, with AI-sector proof. The advantage is that nothing sits between your explanation and the person who has to render it, which matters when the explanation is subtle.
The weakness is the thin record. One named client, no founding date, no team detail, and no published price, so you are assessing almost entirely on the strength of a conversation.
Check | Finding |
|---|---|
Based in | Walnut Creek, USA |
Founded | Not published |
Team size | 1-10 |
Primary platform | Mixed |
AI-sector proof | Yes |
Named clients | The Cut |
Pricing | Not published |
Best fit | Small budgets prioritising direct senior access over evidence |
9. Foundey
Foundey has worked from San Francisco since 2021 with AI-sector proof through DemandIQ, Traycer, and Sero AI. These are early AI companies, so the team understands the stage you are at and the particular difficulty of describing something that has no obvious visual form.
The weakness is decisive for a website brief. Foundey is Figma-only, so it designs and someone else builds. For a search product where the demonstration is the hardest and most valuable part of the page, that handover falls exactly where the difficulty lives.
Check | Finding |
|---|---|
Based in | San Francisco, USA |
Founded | 2021 |
Team size | Not published |
Primary platform | Figma-only |
AI-sector proof | Yes |
Named clients | DemandIQ, Traycer, Sero AI |
Pricing | Not published |
Best fit | Search teams whose own engineers will build the front end |
10. Fantasy
Fantasy has worked from San Francisco and New York since 1999 across mixed platforms, and holds AI-sector proof. A practice running that long has seen several ideas about how people find information come and go, and that perspective has real value in a category defined by an old assumption.
The weakness is that almost nothing can be checked beforehand. No client list, no team size, and no starting price are published, which is an uncomfortable amount of uncertainty when the work itself is about making things findable.
Check | Finding |
|---|---|
Based in | San Francisco and New York, USA |
Founded | 1999 |
Team size | Not published |
Primary platform | Mixed |
AI-sector proof | Yes |
Named clients | Not published |
Pricing | Not published |
Best fit | Teams willing to assess a studio purely through conversation |
How to choose between them
Sort by what is actually broken.
If nothing on the page proves your results are better, buy the ability to let visitors try it themselves. basement.studio.
If the product has a real interface doing the persuading, buy a studio used to complex software. Lazarev.
If enterprise buyers need reassurance before they will trust you with their documents, buy borrowed credibility. Clay.
If your site looks like every other search product, that is a differentiation brief rather than a clarity one. Feels Like.
One test before you sign. Ask each studio to design the hero without a search box in it. A studio that finds an answer has understood that the box is the thing making you invisible. A studio that says the box is essential has accepted the mental model you are paying to break, and will build you the site you already have.
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Can't find your answer? Book a call and let's talk.
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