Top 10 Best AI Design Agencies in 2026

Agency	Best for	Why it stands out
Fireart Studio	AI companies and AI features in SaaS and fintech that need brand, product UX and code from one team	Rebranded conversational AI company Replicant shortly before its $78M round and redesigned CFI’s platform around its Kaiana AI assistant; 4.9 on Clutch from 40 reviews
MetaLab	Generative AI startups with $100,000+ budgets	Design and engineering partner to Suno, Midjourney and Windsurf; the Suno iOS app reached #11 in the App Store Music chart within 24 hours
Fantasy	Enterprise assistants and agentic interfaces	Redesigned Priceline’s Penny assistant in 8 weeks, with distinct listening, thinking and generating states and copy for when there is no good answer

Hiring an AI design agency is not like hiring a design agency for any other product. A normal product does the same thing every time someone taps a button. An AI product does not: it can be right, almost right or confidently wrong, and the interface has to work in all three cases.

The term also hides several different jobs. Some studios design and build generative AI products end to end. Some specialise in research on how people talk to AI and when they trust it. Some design the brands of AI companies. And a growing group call themselves AI design agencies because they use AI tools in their own process, which is a different service altogether.

I wrote this guide for the people who have to make the call: founders of AI startups, product leaders adding AI to an existing SaaS or fintech platform, and innovation leads at larger companies whose AI pilot now has to work for real customers. At Fireart we design and build digital products, including brands and platforms for AI companies, so I know which parts of this work we are strong at and where another studio is the better choice.

Every agency below is judged on the same five criteria: shipped AI product proof, AI interaction design depth, research and evaluation, build capability and engagement fit. Positions reflect fit for a situation, not a quality score. Where another studio beats us, I say so.

TL;DR

• There is no single best AI design agency, only studios built for different jobs: product studios that design and build, research-led AI UX specialists, embedded designers for AI features in SaaS, and brand studios for AI companies. Pick the job first, then the name.
• The value gap is a product problem as much as a model problem. McKinsey’s State of AI 2026, published in August 2026, found that nearly 9 in 10 organisations use AI regularly, but only 37% attribute any EBIT impact to it.
• People rely on AI more than they trust it. A KPMG and University of Melbourne study of more than 48,000 people in 47 countries found that 66% rely on AI output without checking it, while only 46% are willing to trust AI.
• Disclosure is now part of the interface. Since 2 August 2026, the EU AI Act’s transparency rules require telling people when they are interacting with AI and marking synthetic content, and California’s SB 243 has required a clear AI notice for companion chatbots since 1 January 2026.
• Chat is not automatically the answer. In a July 2026 Gartner survey of 3,566 customers, people were three times more likely to use third-party gen AI tools than a company’s own chatbot for service issues, and company chatbot use has been flat since 2022.
• Vet partners on named, dated AI products, the screens they designed for wrong answers, how they test model output with real users and who writes the production code, rather than on Dribbble shots of chat windows.

Which AI design agency fits which situation?

There is no single best AI design agency, only studios built for different situations. A team that can make a generative AI app feel magical at launch may never have designed the screen that appears when the model gets a customer’s money wrong. A research team that knows exactly how people talk to assistants may not write a line of production code. The first question is which of those problems you have.

Our evaluation criteria

I check the same five things before shortlisting anyone for AI work:

• Shipped AI product proof: Named AI clients and dated work that reached real users. A product you can open beats a portfolio of concept shots.
• AI interaction design depth: How the studio designs for uncertainty: wrong or partial answers, waiting time, sources and confidence, undo, handover to a person, and what an agent may do without asking.
• Research and evaluation: Whether the team tests with real users and real model output, and measures task success rather than demo reactions.
• Build capability: Whether the studio ships production code and works with model APIs, or hands over design files for someone else to build.
• Engagement fit: Team size, budgets and the kind of client each studio suits, from a seed-stage AI startup to an enterprise rollout.

What these criteria do not measure: star ratings, award counts or how often a studio uses the phrase “AI-native”. Three of the studios below have no Clutch reviews at all, and most AI-specific metrics in this field are self-reported, which each card notes.

Why designing for wrong answers outranks portfolio polish

An AI product is judged on its worst answer, not its best one. In Stack Overflow’s 2025 developer survey, 66% of respondents named answers that are almost right, but not quite, as a frustration with AI tools, and 75% said they would ask a person when they do not trust the AI. Almost right is expensive because people find out later, after they have acted on it.

That is why every card below says what each studio has shown about errors, confidence and control. Polished launch screens are easy to find. Evidence of a well-designed failure state is not.

Who this guide is for

• Founders of AI startups who need a product that explains itself in the first session and keeps users after the novelty wears off.
• Product leaders at SaaS and fintech companies adding copilots, assistants or agents to a platform that already has paying users and cannot afford a trust incident.
• Innovation and digital leads at larger companies whose AI pilot has to move into production. Gartner expects over 40% of agentic AI projects to be cancelled by the end of 2027 because of cost, unclear value or weak risk controls.

The 10 AI design agencies covered

• Fireart Studio: AI companies and AI features in SaaS and fintech that need brand, product UX and code from one team.
• MetaLab: Generative AI startups with $100,000+ budgets that need design and engineering together.
• Fantasy: Enterprise assistants and agentic interfaces for large brands.
• Punchcut: Research and design for voice, multimodal and in-car AI.
• The Gradient: Conversational AI products that need model behaviour tested, not only screens.
• Lazarev.agency: Funded AI startups that need a striking, award-level interface.
• Cieden: B2B teams designing their first agent workflows, prototyped in code.
• Eleken: SaaS companies that want an embedded designer for a steady stream of AI features.
• Halo Lab: AI startups that need design and a full-stack build on a seed-stage budget.
• Ramotion: Brand identity and websites for AI companies.

Company Name	Best For	Engagement Model	AI Build Coverage
Fireart Studio	AI companies and AI features in SaaS and fintech	Design and build projects; designers embedded in client teams	Production web and mobile code; LLM integration approach published in its guides
MetaLab	Generative AI startups with large budgets	Dedicated teams, one client at a time	Full product builds, including engineering
Fantasy	Enterprise assistants and agentic interfaces	Enterprise programmes from strategy to build	AI and LLM integration listed as a service; full builds
Punchcut	Voice, multimodal and in-car AI research	6–12 week design sprints with client teams	Prototypes; production engineering not publicly claimed
The Gradient	Conversational AI with tested model behaviour	Research-led design projects from $25,000	Coded prototypes with output testing; production not publicly claimed
Lazarev.agency	Award-level interfaces for funded AI startups	Milestone-based design projects from $100,000	Design handoff; websites in-house; product builds through partners
Cieden	First agent workflows for B2B teams	Hourly projects, sprints and team extension	Coded prototypes and an internal LLM tool; client work mostly handoff
Eleken	Ongoing AI features in SaaS	Monthly design subscription	Front-end code as pull requests; no backend or model work
Halo Lab	Design plus full-stack build on a startup budget	Milestone projects and dedicated teams	Front end, back end and mobile; LLM APIs used in an internal app
Ramotion	Brand and websites for AI companies	Fixed-price brand and product packages	Production websites; model integration not publicly claimed

The cards start with the studios that design and build, move through the research-led and interface specialists, and finish with the embedded, full-stack and brand options.

1. Fireart Studio

AI company brands · AI features in SaaS and fintech · Design plus web and app build

Clutch record: 4.9/5 from 40 reviews

HQ / founded: Warsaw, Poland, 2013; client work across the US, Europe and the UAE

Minimum project: $10,000+ (Clutch); $50–$99 per hour

In-house web and product build: Yes: UX research, UI/UX and product design, web and mobile development

Fireart is a 60+ person design and development studio. Its AI work so far sits in two places: brands for AI companies, and product UX for companies adding AI to a platform that already has users.

Evaluated on the basis of

• Shipped AI product proof: Named and dated, though mostly on the brand and platform side rather than standalone AI apps. The record includes a 2022 rebrand for conversational AI company Replicant, a 2024 platform redesign for CFI Financial Group that gives its Kaiana AI assistant a conversational interface, and product UX since February 2024 for a Chicago company that builds AI-powered personalised health interventions.
• AI interaction design depth: Fireart’s published approach covers explainable AI with visual citations, manual overrides and rules that limit what in-app agents can assemble on screen, as set out in its piece on how AI-driven design is shaping UI/UX. Detailed public write-ups of chat or agent flows are not available yet.
• Research and evaluation: UX research and personas are part of product engagements, and the health client reports that tester surveys rated the new UI well. No public evaluation of AI output quality with users was found.
• Build capability: Ships production code. Clutch lists JavaScript, React, Angular and Node.js as its main development focus, and the studio built a full-stack React MVP with live energy dashboards for Sprightful.
• Engagement fit: Startups and mid-market companies (55% of its client mix on Clutch is mid-market), as a project team or as designers embedded in a client’s own product team.

Differentiator

Fireart has worked on both sides of an AI company: how it looks to buyers and how it behaves for users. For Replicant, the brand had to explain an AI that answers first-line contact-centre calls and hands the hard ones to people. For Revionics, an AI price-optimisation company, Fireart built the client’s UX and research team from scratch and worked on three new platforms. That mix suits companies whose AI already works but whose story and interface lag behind it.

Proof of execution

• Replicant (February to July 2022): brand strategy, a new logo and a visual identity for a contact-centre automation company. Replicant closed a $78 million round shortly afterwards and partly credits the new identity, the work won Gold at the iDesign Awards 2023, and the CMO rated it 5.0 on Clutch.
• CFI Financial Group (2024): a new information architecture across 50+ website pages, 250+ redesigned app screens in light and dark themes, a shared design system and a refreshed home for the Kaiana AI assistant. The case reports a 25% increase in user engagement and retention.
• Revionics (since September 2021, about $250,000): three UX designers and two UI developers embedded in the client’s team, a design system and work on three new platforms. The Director of UI/UX rated the engagement 5.0 on Clutch.

Pricing

Clutch lists a $10,000+ minimum and $50–$99 per hour. Fireart’s guide to AI customer service agent development estimates an agent MVP at about $40,000–$60,000 over 4–6 weeks, and an enterprise-grade agent with custom integrations at 12–16 weeks. Design team augmentation is available for teams that want designers inside their own sprints.

Potential limitation

Not the strongest pick if you want a long public record of standalone generative AI apps; MetaLab and Fantasy show more of that. Fireart’s AI case pages report brand and platform outcomes rather than documenting chat or agent flows screen by screen, so ask for a walkthrough of that work on a call.

My take

Most AI products I review do not need a more impressive model. They need the unglamorous parts done well: onboarding that sets expectations, a screen for when the answer is wrong, and a brand that explains what the AI does in one sentence. That is why I put design and engineering in one team for any AI feature that customers touch, and why our guide to integrating LLMs into business applications spends more time on architecture and UX than on model choice.

Rated 4.9/5 on Clutch, based on 40 reviews.

Fireart sits where an existing business meets AI. The next two studios have the longest public record of products where the AI is the product itself.

2. MetaLab

Generative AI consumer apps · AI startup brand systems · Full product builds

Clutch record: Profile listed, no reviews

HQ / founded: Victoria, British Columbia, Canada, 2006; remote team across 18+ countries; part of the Tiny group

Minimum project: $100,000+ (Clutch)

In-house web and product build: Yes: design and engineering on full builds such as Suno and The Atlantic

Evaluated on the basis of

• Shipped AI product proof: The strongest generative AI client list in this guide. MetaLab’s AI work page names Midjourney, Suno, Windsurf, Modular, Together.ai, Bardeen and Otter, and most of those products are live.
• AI interaction design depth: Not documented. The case pages show polished screens but say little about how the products handle wrong answers, waiting time or user control.
• Research and evaluation: UX research appears as a service tag. No published evaluation of AI output with users was found.
• Build capability: Ships production code. Suno was a full build of product, brand and engineering, and MetaLab says its teams work on one client at a time.
• Engagement fit: Venture-backed startups and enterprises with budgets of $100,000 or more.

Differentiator

MetaLab has become a go-to design partner for AI startups that are defining their category, which means it has seen more first versions of generative AI products than almost anyone. The one-client-at-a-time team model also matters: on a product where the interface changes every time the model does, a team that is not split across five accounts can keep up.

Proof of execution

• Suno: product design, brand and engineering for the AI music app. The iOS app launched on 1 July 2024 and, according to a Codrops interview, reached #11 in the App Store Music chart within 24 hours. The work appears among the D&AD 2026 Digital Experience Design winners.
• Windsurf: a brand system for the AI coding tool covering logo, colour, type, motion and product and marketing applications, covered by Codrops in November 2025.
• Midjourney: product design and strategy for Rooms, which helped Midjourney move beyond Discord. Rooms opened for testing in April 2024; Midjourney’s own announcement does not name an agency.

Pricing

Not published on MetaLab’s site. Clutch lists a $100,000+ minimum.

Potential limitation

The wrong choice below a $100,000 budget. The AI case pages are short and mostly undated, so ask specifically how the team designed for errors, latency and trust on one of those products.

My take

A famous AI client list tells you a studio can make a launch look inevitable. It does not tell you what happens in month four, when the model changes and half the prompts behave differently. With MetaLab I would ask who stayed on the account after launch and what they changed.

Where MetaLab’s strength is startups, Fantasy’s is large companies putting agents in front of millions of existing customers.

3. Fantasy

Agentic and generative interfaces · AI-native design systems · Enterprise AI product builds

Clutch record: Profile listed, no reviews

HQ / founded: New York, 1999 (as Fantasy Interactive); remote-first across the US; independent

Minimum project: Not published

In-house web and product build: Yes: AI and LLM integration is a listed service, and the LIV Golf apps, website and backend shipped in 72 days

Evaluated on the basis of

• Shipped AI product proof: Strong and named: Priceline’s Penny assistant, BNY’s Eliza platform and the Art Basel app, plus Amper, the generative music platform Shutterstock bought in 2020. Some credits are shared with other vendors (see proof below).
• AI interaction design depth: The most explicit in this guide. Fantasy’s AI services page describes AI disclosure, showing confidence, points where a human takes over, and agent steps that a person can review, approve or change.
• Research and evaluation: It tests where users trust AI and built its own Synthetic Humans research tool. No published evaluation of AI output quality was found.
• Build capability: Ships production code. The LIV Golf build used AI-assisted design-to-code tooling to ship native apps, a website and a backend in 72 days.
• Engagement fit: Fortune 500 and global brands; its client list includes Google, Nike, Amazon and BNY.

Differentiator

Fantasy treats the moments when AI is slow or unsure as design work rather than engineering leftovers. Penny’s redesign gave the assistant distinct listening, thinking and generating states, motion that covers model latency and recovery copy for when there is no exact match. Few studios publish that level of detail about how an assistant behaves between the prompt and the answer.

Proof of execution

• Priceline Penny (launched November 2025, 8 weeks): brand and motion identity, an avatar with listening, thinking and generating states, latency-covering motion, recovery copy and a Figma toolkit. The scope was design only and no outcome metrics are published.
• BNY Eliza: vision work and an AI-native design system with 60+ components and a three-layer token setup for the bank’s enterprise AI platform.
• Synthetic Humans, Fantasy’s AI research personas, won the Experimental category of Fast Company’s World Changing Ideas in 2024. A Harvard Business School AI Institute write-up describes a BP innovation sprint that took 12 days instead of the usual 10–12 weeks.

Pricing

Not published.

Potential limitation

The wrong choice for seed-stage teams on lean budgets. Several headline AI credits are shared: Fantasy says it designed the Art Basel app’s AI concierge, while Microsoft’s account credits other partners for the AI build. Ask exactly which layer Fantasy owned on the projects you care about.

My take

The Penny case is the one I would show any team designing an assistant. The waiting states and “no exact match” copy decide whether users stay after the first wrong answer. That is far less exciting than an avatar reveal, and far more important.

Fantasy and MetaLab design and build. The next studio’s strength is research, especially when the interface is a voice or a car rather than a screen.

4. Punchcut

Conversational and voice AI · Multimodal and in-car agents · ML tooling UX

Clutch record: Profile listed, no reviews

HQ / founded: San Francisco, 2002; founder-led

Minimum project: Not published

In-house web and product build: Prototypes in-house; no production engineering team described

Evaluated on the basis of

• Shipped AI product proof: Mixed. Work for Google and AWS SageMaker Studio appears to have shipped but is undated, while several assistant projects for Toyota and Samsung are concepts.
• AI interaction design depth: Good. Punchcut’s July 2026 writing covers earning trust step by step, admitting uncertainty, keeping users in control and not using agents everywhere, and its services include agent, conversational and multimodal design.
• Research and evaluation: The strongest in this guide on this point: a voice UX metrics toolkit used to test with consumers for Toyota, ML user archetypes and heuristic evaluation for AWS, and a generative AI research programme run by Punchcut Labs since 2023.
• Build capability: Prototyping is clear; production engineering is not publicly claimed. Plan for a separate build partner.
• Engagement fit: Product leaders at large companies, through 6–12 week design sprints run alongside the client’s team.

Differentiator

Punchcut has designed for voices, cars, TVs and devices since 2002, which is exactly where agents are going next. When an AI has no screen to show its sources or a progress bar, the conversation itself has to carry trust, and that is a skill most screen-first studios have not had to learn.

Proof of execution

• Google: helped bring Gemini into Google experiences, designed, branded and tested a premium assistant with personas, and worked on machine-learning features in Google Photos. No project dates or metrics are published.
• AWS SageMaker Studio: a no-code machine-learning interface and the Bloom design system, grounded in research with ML users, user archetypes and heuristic evaluation.
• Toyota: a proactive multimodal in-car agent concept with a vision video, plus a voice UX metrics toolkit for testing with consumers.

Pricing

Not published.

Potential limitation

The wrong choice if you need one partner to ship production code, or if you want public, dated proof before you sign. Much of the AI work is concept-stage or covered by NDA.

My take

Hire Punchcut before you build, not instead of building. A few weeks of research on how people actually talk to an assistant will save a quarter of rework, but you will need an engineering team ready to take the findings straight into code.

Punchcut researches how people talk to an AI. The next studio goes a step further and tests what the AI says back.

5. The Gradient

Conversational AI products · Model behaviour and output evaluation · Working AI prototypes in code

Clutch record: 4.9/5 from 57 reviews

HQ / founded: Lviv, Ukraine, 2015; office in London

Minimum project: $25,000+ (Clutch); $50–$99 per hour

In-house web and product build: Partly: designers build working prototypes in code; full production builds are not publicly claimed

Evaluated on the basis of

• Shipped AI product proof: Good. The work includes the Happy Companies AI coach (2023–2024), confirmed by the client CEO’s Clutch review, and the Lumiere AI video feedback product (2022 to ongoing). Three more AI engagements from 2025–2026 have Clutch reviews: AiQL, Inferoute and Intrepid.
• AI interaction design depth: Good. Case studies document persona consistency and how model choices change the experience, not only the screens.
• Research and evaluation: The deepest public evidence in this guide. Happy Companies went through several rounds of user testing and pilots with more than 200 executives, and the Norvana case reports measured changes in output quality.
• Build capability: Designers prototype in working code, as with an iOS prototype for Norvana. Production engineering at scale is not publicly claimed.
• Engagement fit: Startups and scale-ups with budgets from $25,000. Its 2025 AI projects on Clutch ran at about $42,000 and $50,000. Clutch lists 10–49 employees.

Differentiator

The Gradient treats model behaviour as design material. On Norvana, the team switched from GPT-4.0 to GPT-4.1 because the newer model held the app’s persona better, and added a second “art director” agent that cut broken images from 4 in 7 to 1 in 7. Most agencies show you the screens around the AI; this one shows you what it changed inside.

Proof of execution

• Happy Companies (2023–2024): a conversational AI leadership coach, tested over several rounds and piloted with 200+ executives. The studio reports 78% monthly active coaching use and 86% of users saying their leadership improved; the CEO’s Clutch review covers a $50,000–$199,000 engagement.
• Norvana (2024–2025): an AI health app prototype built in code, with documented model choices and output testing. It was a fundraising demo, not a shipped product.
• Lumiere (2022 to ongoing): an AI video feedback product with a self-reported 20 million+ viewer reactions. Another agency separately claims Lumiere’s brand and website work.

Pricing

Not published on its site. Clutch lists a $25,000+ minimum and $50–$99 per hour.

Potential limitation

A small team. It is the wrong choice if you want one partner to design, build and run a large platform, because production engineering at scale is not part of its public record.

My take

“Did you test what the model says, or only the screens around it?” is the question that separates AI designers from designers who have worked on AI projects. The Gradient answers it with numbers. I would use it to shape and test an AI experience before an engineering team commits to it.

The Gradient and Punchcut lead on research. The next two studios lead on the interface itself, for very different budgets.

6. Lazarev.agency

AI product UX for funded startups · Conversational and agent interfaces · Award-led visual design

Clutch record: 5.0/5 from 19 reviews

HQ / founded: San Francisco Bay Area, 2015; founded by Kyrylo Lazarev, with Ukrainian roots

Minimum project: $100,000+ (Clutch); standard rate of $100 per hour on its own site

In-house web and product build: Websites, including Webflow, in-house; product development through partners

Evaluated on the basis of

• Shipped AI product proof: Strong. Named and dated AI work includes Accern’s Rhea (2024), Elva (2026), VT.news, Suits.ai and Pika AI. Accern’s acquisition by Wand AI in February 2025 is confirmed by Dealroom.
• AI interaction design depth: Moderate to strong. The cases show citations and footnotes, clarifying questions for vague prompts, a draft-and-approve flow and reports that show which agent did which subtask. Methods for handling hallucinations are not described.
• Research and evaluation: Weak in public evidence. Case pages tag UX research without describing it, and only one of 19 Clutch reviews covers AI work: a conversational AI SaaS project from 2022–2023 whose reviewer mentions surveys, interviews and testing.
• Build capability: Design handoff for products, with development through partner firms. No evidence of model integration was found.
• Engagement fit: Funded AI startups with $100,000+ budgets. Its own FAQ gives 8–12 weeks for design and 3–6 months end to end.

Differentiator

Lazarev designs AI products to be noticed. Elva was an Awwwards Site of the Day in June 2026 and, according to the agency’s press release, won a 2026 Webby for Best Visual Design in AI. For a startup that has to make investors and early users understand a new kind of product in one screen, that visual ambition is a real asset.

Proof of execution

• Accern Rhea (2024): a hybrid prompt-and-widget interface for financial research, with footnoted sources, clarifying questions for vague queries and a split-screen report builder. The agency’s link between the design and Accern’s later acquisition is self-reported.
• Elva (2026): brand, onboarding, a voice interaction model and a draft-and-approve flow for a voice-first AI video editor.
• VT.news: AI summaries, a media-bias scale and a topic-aware chat assistant for an AI news aggregator, with a self-reported 85,000 new users in the first month.

Pricing

A standard rate of $100 per hour with milestone-based or custom contracts. Clutch lists a $100,000+ minimum, the highest in this guide alongside MetaLab.

Potential limitation

The wrong choice if you need an in-house engineering team or published evidence of how AI output was tested with users. Much of the public record is awards and funding headlines rather than measured UX outcomes.

My take

Awards are a fair signal of craft and a weak signal of retention. If your AI product’s problem is that nobody understands it from the landing page, Lazarev is worth a call. If the problem is that users try it once and leave, ask them for a case where they changed a metric after launch.

7. Cieden

Agent and workflow UI for B2B · AI prototypes in code · Voice and multimodal flows

Clutch record: 4.9/5 from 46 reviews

HQ / founded: Toronto, Canada, 2016; offices in Wilmington, Delaware and Lviv, Ukraine

Minimum project: $10,000+ (Clutch); published rates from $50 per hour

In-house web and product build: Partly: coded prototypes and an internal AI tool; client work is mostly design handoff

Evaluated on the basis of

• Shipped AI product proof: Moderate. The AI work is recent and dated (Tandom and Way2B1, both published in December 2025), but it is mostly MVPs, prototypes and an internal tool. Its only AI review on Clutch dates from 2022.
• AI interaction design depth: Strong on paper. The agent UI case shows editable plans before an agent runs, stop and rerun controls, visible failure states and permissions explained in plain language. Its AI design page covers confidence levels and “I don’t know” states.
• Research and evaluation: Moderate. Coded prototypes were tested hands-on with accountants using realistic data, and a voice prototype was tested live against latency targets. No sample sizes are published.
• Build capability: Coded proofs of concept (one used Next.js, Convex and ElevenLabs) and an internal CRM running on Claude with 24 tools. Client delivery is mostly Figma files mapped to a component library.
• Engagement fit: B2B and SaaS teams that need one to three people for roughly $10,000 to $100,000.

Differentiator

Cieden prototypes AI features in code on real data before engineering starts, using tools such as Cursor, v0 and Lovable. With AI, a static mock-up hides the hard part: you cannot judge a summary, a suggested plan or a voice turn without seeing real output. Testing a working prototype catches that early and cheaply.

Proof of execution

• Tandom (about two months, published December 2025): a chat-built AI agent tool for logistics where each conversation produces a step-by-step plan users can edit before it runs, with stop, rerun and failure states.
• Way2B1 (about two years, ongoing): an AI assistant that summarises documents and pre-fills onboarding data in wealth-management software, tested with accountants through coded prototypes. The studio reports decision time falling from weeks to days.
• FinPilot: a voice-and-text loan journey prototype for an unnamed consumer finance app, covering consent prompts, turn-taking and language switching. A proof of concept, not a shipped product.

Pricing

Published on its pricing page: $50 per hour for project work ($80 for US and Canada-based designers), $7,200 per month per person for team extension, a $2,000 UX audit, a $7,000 two-week design sprint and $6,500 for product discovery.

Potential limitation

A team of about 50, and its AI case studies all date from late 2025 with no client reviews for them yet. Not the pick if you need proof of AI products running at scale.

My take

Cieden’s editable agent plan is the pattern I wish more teams copied. Letting people see and change what an agent is about to do costs one extra screen, and it removes most of the fear of pressing “run”. For a B2B team adding its first agent, that is a sensible, affordable place to start.

8. Eleken

AI features inside B2B SaaS · Embedded subscription designers · Review and correction flows for AI output

Clutch record: 4.9/5 from 128 reviews

HQ / founded: Kyiv, Ukraine, 2015; US entity in Delaware

Minimum project: $10,000+ (Clutch); subscriptions from $4,599 per month

In-house web and product build: Front end only: React or HTML delivered as pull requests; no backend or model integration

Evaluated on the basis of

• Shipped AI product proof: Good. 14 of its 76 published cases are tagged AI, with named clients such as Panjaya, Zaplify (now Andsend), Aampe and RedOwl, and two AI engagements from 2024–2025 are reviewed on Clutch.
• AI interaction design depth: Good in specific cases: an error summary panel that lets users skip non-critical AI errors, regeneration of a single segment, an approval step before an AI-drafted budget is sent, and progress messages for generations that take up to two minutes.
• Research and evaluation: Mixed. Panjaya started with a three-day audit and moderated interviews with translators, and Zaplify ran weekly UX experiments on live data. Most cases describe no formal testing.
• Build capability: Front-end code delivered into the client’s repository. The client connects the backend and the model.
• Engagement fit: SaaS startups and scale-ups that want one embedded designer at a predictable cost of roughly $55,000 to $144,000 a year.

Differentiator

Eleken sells design like software: a fixed monthly subscription, a published price, a three-day free trial and one month’s notice to cancel. For a SaaS company adding AI features every sprint, a designer who stays inside the team is often more useful than a big project that ends at launch.

Proof of execution

• Panjaya (January to June 2025): AI dubbing and lip-sync review tools, including an error summary panel, single-segment regeneration, variant comparison and segment comments, based on interviews with translators.
• Zaplify, now Andsend: playbooks that steer AI-written outreach by intent, tone and audience, plus explanations of why each lead was recommended. Activation rose from 20–25% to about 40%, as reported by the agency.
• Aampe: a designer embedded since September 2023 in an AI marketing-messages platform. Aampe’s $18 million Series A in December 2024 is independently confirmed, though the case does not prove the design caused it.

Pricing

Published on its pricing page: $4,599 a month for a part-time designer, $6,599 for a full-time designer and $11,999 for two, with a two-month minimum.

Potential limitation

A generalist SaaS design shop where AI is about a fifth of the portfolio. It does not build backends or integrate models, and a single embedded designer may lack the depth to define an AI product strategy from scratch.

My take

Most AI work inside SaaS is not a new product. It is a dozen small features that each need a review step, an undo and a sensible empty state. A steady embedded designer handles that better than a studio that wants to reinvent the product, which is exactly the job Eleken is built for.

Eleken stops at the front end. The next studio builds the whole stack at a similar price point.

9. Halo Lab

Design plus full-stack build · AI health and deep-tech interfaces · Brand and product for AI startups

Clutch record: 4.9/5 from 100 reviews

HQ / founded: Founded in Odesa, Ukraine, 2013; Clutch lists Dubai as HQ, and the studio lists offices in Warsaw and Boston

Minimum project: $10,000+ (Clutch); projects from $5,000 on its site; $25–$49 per hour

In-house web and product build: Yes: front end, back end, React Native and QA

Evaluated on the basis of

• Shipped AI product proof: Moderate. About 20 of 133 portfolio projects are tagged AI, with named clients such as Vixiv, OmniBuds and Upstrima, but the cases are undated, carry few metrics and include some branding-only work and investor demos.
• AI interaction design depth: Claimed more than shown. Its AI/ML design page promises low-confidence states, override and correction flows and a 3–5 day model behaviour and explainability mapping step, but the case studies document little AI-specific UX.
• Research and evaluation: Mostly competitor benchmarking and audits. No usability testing of AI output is described.
• Build capability: Strong. In-house front end, back end and mobile development on Node.js, React, Next.js, React Native and AWS, with QA.
• Engagement fit: Startups that want one vendor for brand, product design and build, typically $10,000 to $200,000 over 8–14 weeks.

Differentiator

Halo Lab offers a 150+ person team that covers brand, UX and production code at $25–$49 per hour, the lowest Clutch rate band in this guide alongside Eleken. For an AI startup with real engineering needs and a seed-stage budget, that combination is hard to find.

Proof of execution

• Vixiv (four months): design, a React, TypeScript and Three.js front end and QA for an AI platform that generates lattice designs for 3D printing, delivered on time for the beta. The client’s ML lead, quoted on Halo Lab’s page, reports 70% faster mesh selection.
• OmniBuds, a Nokia Bell Labs spin-off (nine months): 50+ screens, a React Native app with pairing, calibration and real-time vitals, and a Webflow site, tested on real hardware.
• Upstrima: eight user flows and a design system for an AI agent workspace used by oil and gas engineers, handed off weekly to the client’s developers. The product is live.

Pricing

Projects start from $5,000, and its FAQ gives a range of about $5,000 to over $200,000. Design and development are paid by milestone, dedicated teams monthly.

Potential limitation

A broad digital agency where AI is one vertical among many, and its Clutch reviews are dominated by websites and branding. The wrong choice if you need proven depth in AI interaction design or measured AI UX outcomes.

My take

For a working AI front end on a startup budget, Halo Lab is a practical option. I would still budget for one specialist research sprint, with The Gradient or Punchcut for example, before a cheaper build team commits to an interaction model.

The last studio on the list does not design AI interactions at all. It designs how AI companies look.

10. Ramotion

AI startup brand identity · Marketing sites for AI companies · Product UI redesign

Clutch record: 4.9/5 from 29 reviews

HQ / founded: San Francisco, 2009; Clutch also lists New York, Los Angeles and Austin

Minimum project: $50,000+ (Clutch); published packages from $60,000; $150–$199 per hour

In-house web and product build: Yes for websites (Next.js, React with Contentful, Webflow); engineering teams available from $45,000 a month

Evaluated on the basis of

• Shipped AI product proof: Named AI clients on its AI work page, including Descript, Murf AI, Speak, Borea AI and Flatfile. The work is mostly brand identity and marketing websites rather than product interfaces.
• AI interaction design depth: Not publicly claimed in the reviewed sources.
• Research and evaluation: Not publicly claimed for AI products in the reviewed sources.
• Build capability: Ships production websites and offers engineering teams; model integration is not publicly claimed.
• Engagement fit: Funded AI startups preparing a launch or a round, with brand budgets from $60,000.

Differentiator

Ramotion publishes fixed prices for its main packages, which no other brand studio in this guide does. For an AI startup that has to look credible to enterprise buyers and investors before the product is mature, a known price and scope removes a month of back-and-forth.

Proof of execution

• Murf AI (November 2024 to ongoing): brand identity and a Webflow website for the AI voice generator, reviewed on Clutch in May 2025.
• Descript: brand identity and a website on Contentful and React, followed by about three years of website support.
• Borea AI: brand and a Next.js website, delivered in about two months by a team of 17.

Pricing

Published on its startup packages page: brand creation $60,000, brand redesign $85,000, product redesign $70,000, discovery from $25,000 a month and engineering teams from $45,000 a month.

Potential limitation

The wrong choice if the hard problem is how your AI behaves in the product. Its AI record is brand and marketing sites, so pair it with a product studio for the interface.

My take

AI companies are hard to tell apart: gradients, a spark icon and the word “intelligent” on every homepage. A brand that says plainly what the product does is worth more than a clever mark, and Ramotion’s AI work mostly does that. Just do not expect a brand studio to solve a retention problem, which is why our own branding work for AI companies such as Replicant sits next to product design.

How this list was assessed

Every agency was checked in October 2026 against the same five criteria, using its own case studies, Clutch profiles and reviews, client announcements and independent sources such as Dealroom, Fast Company, Codrops and Awwwards. Each studio had to show named AI work. Where a client or another vendor credits the same work to someone else, the card says so.

No numeric scores appear in this guide, and that is deliberate. Three of the ten studios have no Clutch reviews at all, and only a handful of the reviews that do exist concern AI work. A star average would reward the studios that collect reviews, not the ones that design AI well.

Most AI metrics in this field come from the agencies themselves, so the cards mark them as self-reported. Where a studio’s public record does not evidence a criterion, the card says so instead of guessing. Absence of a claim is not a failure, but it is information you should have before you sign.

What was left out

• Studios that call themselves AI design agencies because they use AI tools to produce marketing creative faster, such as Superside. That is a useful service, but a different job from designing an AI product.
• Clay, whose brand and product craft is excellent, but whose public portfolio includes only two undated AI case studies.
• Large consultancies such as frog, whose recent shipped AI work for named clients is hard to verify from public sources.
• Freelancers and marketplace listings, however good, because this guide compares teams that can staff a full engagement.

Why Fireart is first

Fireart sits first because this is our blog, and because brands and product UX for AI companies, and AI features inside platforms that already have users, designed and built by one team, are the work we do. That placement is a statement of fit, not of rank. MetaLab and Fantasy have longer public records of generative AI products, The Gradient and Punchcut go deeper on research and evaluation, and Eleken and Cieden offer cheaper ways to start.

What does an AI design agency actually do?

An AI design agency designs how people use, understand and trust a product whose output is generated rather than fixed: generative AI apps, assistants and copilots, agents that act on a user’s behalf, and AI features inside existing software. The good ones also design the parts nobody demos: what happens while the model is thinking, when it is wrong, and when a person needs to take over.

There is a second meaning. Some agencies call themselves AI design agencies because they use AI to produce design faster. That can lower the cost of marketing creative, but it does not make a studio good at designing AI products. Ask which of the two a studio means before the first call ends.

Start with the moment the AI is wrong

Most AI briefs start with the happy path: the perfect prompt and the perfect answer. Real usage starts somewhere else. In the KPMG and University of Melbourne study, 56% of people said they had made mistakes at work because of AI. The interface decides whether that mistake is caught on screen or by a customer.

The table below shows which kind of studio usually owns each part of the work, and what each part forces into the brief.

Deliverable	Who usually does it	What it forces into the brief
AI product strategy and discovery	Fantasy, Punchcut, Fireart Studio	Which tasks the AI should do at all, and what success looks like
Conversational and voice interfaces	Punchcut, The Gradient, Lazarev.agency	Turn-taking, a consistent persona, handover to a person
Agent workflows	Cieden, Fantasy	Editable plans, approval steps, permissions, stop and rerun
AI features inside an existing product	Eleken, Fireart Studio	Review and undo flows, error states, fit with the current design system
Full product design and build	MetaLab, Fireart Studio, Halo Lab	Model APIs, latency budgets, evaluation, analytics
Brand identity for an AI company	Ramotion, Fireart Studio	Explaining what the AI does in one line, standing apart from look-alike AI brands

Disclosure is now part of the spec

Since 2 August 2026, Article 50 of the EU AI Act has required providers to tell people they are interacting with an AI system unless that is obvious, and to mark synthetic audio, images, video and text in a machine-readable way, as Cooley’s summary sets out. The EU’s digital omnibus on AI, adopted in July 2026, delayed the high-risk deadlines but not these transparency rules; generative AI systems already on the market before August have until 2 December 2026 for the marking duty.

In California, SB 243 has required companion chatbots to show a clear and conspicuous AI notice since 1 January 2026, with reminders at least every three hours for users known to be minors. Users want this too: in a September 2025 Pew survey, 76% of Americans said it is very important to know whether content was made by AI or a person, while 53% were not confident they could tell.

Two things to settle before you brief anyone

• Write down the three most likely wrong answers your AI will give in its first month, and what the user should see when each one happens.
• Decide who owns evaluation: who checks model output before launch and after every model or prompt change, and how it will be measured.

Why do AI products stall after the demo?

They stall because the demo was never the product. A demo shows the best answer to a prepared question. A product has to handle every question, every day, at a cost the business can carry.

The numbers are stark. MIT NANDA’s 2025 study of enterprise AI found that 95% of organisations were getting zero measurable return from gen AI despite $30–40 billion of enterprise spending. S&P Global found that 42% of companies abandoned most of their AI initiatives in 2025, up from 17% a year earlier.

The pilot was never designed for real users

MIT’s researchers listed poor user experience among the top barriers to scaling, and found that while 70% of people prefer AI for drafting emails, 90% prefer a human for mission-critical work. A pilot that never met a real user’s messy question has not really been tested.

Almost right is worse than wrong

An obviously wrong answer gets ignored. An almost-right answer gets used. Air Canada learned this in February 2024, when a tribunal held it responsible for its chatbot’s wrong advice on bereavement fares and rejected the argument that the chatbot was a separate entity. In April 2025, Cursor’s AI support bot invented a one-device policy; the company refunded affected users and began labelling AI-written replies.

Nobody budgeted for running it

Each token keeps getting cheaper, but agents use far more of them. Gartner expects providers’ inference costs to fall by more than 90% by 2030, yet notes that agentic tasks use 5–30 times more tokens than a chatbot, so total spend is expected to rise. Evaluation is the other forgotten line: every prompt or model change needs re-testing.

What clients say when it works

“All the designers and developers from Fireart Studio working on our team are fantastic.”

Matthew Law, Director of UI/UX, Revionics (Clutch review, 5.0)

Revionics builds AI price optimisation, and that engagement has run since 2021. AI products need long design relationships, because the product changes every time the model does.

Two things to do this week

• Collect 50 real questions or tasks from users or support tickets, and run them through your prototype before anyone designs a new screen.
• Put a cost ceiling per active user per month in the brief, so the design team knows how many model calls each flow can afford.

How do you design an AI feature users trust?

Design for calibrated trust, not maximum trust. Users should trust the AI exactly as much as it deserves on a given task, which means showing what an answer is based on, how sure the system is and how to fix it.

People are less forgiving of machines than of each other. In a Medallia report from March 2026, more than 40% of consumers said human errors are more forgivable than AI errors, and only 7% said the opposite.

Design the wrong answer first

In a June 2026 ACI Worldwide survey, 60% of UK adults said they would stop using an AI shopping agent after a single mistake. The recovery path, the clarifying question, the “I’m not sure” state and the quick correction are core screens, not edge cases. Fantasy’s Penny redesign and Eleken’s error panel for Panjaya are good public examples.

Show the source and the plan

Footnoted sources, as in Lazarev.agency’s work for Accern, let users check an answer in seconds. For agents, show the plan before it runs: Cieden’s editable step lists let people change what an agent is about to do. Pew found that 61% of Americans want more control over how AI is used in their lives, and a visible, editable plan is the most direct way to give it.

Keep a person in the loop where it matters

An approval step costs one click and prevents the most expensive mistakes. Gartner’s September 2025 survey found that only 19% of IT application leaders highly trust vendors’ hallucination protection. Decide early which actions an agent can take alone and which need a human.

Measure, then keep measuring

NN/g’s August 2026 guidance on evaluating AI output stresses multiple inputs and repeated runs, because the same prompt can produce different answers. That is design work too: someone has to define what a good answer looks like for each user goal.

How we handle it at Fireart

We design explainable AI with visible citations and manual overrides, and we set rules for what in-app agents can assemble on screen. For CFI, the Kaiana assistant was refreshed together with a shared design system for the whole platform, so it speaks the same visual language as the rest of the app. Our three UX rules for conversational UI cover the basics, and a UX audit before a redesign shows where users already hesitate.

What does AI product design cost, and how long does it take?

Anywhere from a few thousand dollars for an audit or a sprint to well over $100,000 for a full AI product, and from two weeks to most of a year. Scope, who builds and who evaluates drive the spread far more than design hours.

Published numbers, side by side

• Clutch’s September 2026 pricing data puts the average UI/UX project at $84,973 and the average AI development project at $120,595, over about 10 months.
• Entry points published by studios in this guide: Cieden’s $2,000 UX audit and $7,000 two-week sprint, Eleken’s full-time designer at $6,599 a month and Halo Lab’s projects from $5,000.
• Fixed packages: Ramotion’s brand creation at $60,000 and product redesign at $70,000.
• Clutch minimums for the larger studios: $100,000+ for MetaLab and Lazarev.agency, $25,000+ for The Gradient and $10,000+ for Fireart Studio.
• Fireart’s own estimate for an AI customer service agent MVP is about $40,000–$60,000.

Typical timelines

Scenario	Typical timeline	What drives it
Design sprint for one AI feature	Two weeks (Cieden’s sprint package)	Access to real data and real users
Assistant redesign on an existing product	About 8 weeks (Fantasy’s Penny redesign)	Brand, motion and waiting states rather than the model
AI agent MVP	4–6 weeks for a narrow MVP; 12–16 weeks with enterprise integrations (Fireart’s estimates)	The number of systems the agent touches
AI product design, end to end	8–12 weeks of design; 3–6 months including build (Lazarev.agency’s published range)	Build partner, evaluation and launch scope
Full AI development project	About 10 months on average (Clutch)	Integrations, data quality and compliance

Two hidden line items

• Evaluation. Every prompt or model change needs re-testing against real cases. That takes multiple inputs and repeated runs, which is time someone has to be paid for.
• Inference. A flow that calls the model three times per screen can cost more to run each month than it cost to design. Ask your design partner to show the number of model calls per user journey.

How to normalise two quotes

Before comparing prices, ask both agencies the same four questions:

• Does the price include production code and model integration, or only design files?
• Who owns evaluation before launch and after each model change?
• How many AI states are designed in detail: loading, partial, wrong, refused and handed to a person?
• What do the first three months after launch cost?

A quote that is half the price of another usually answers one of those questions differently. Our guide on how to calculate a product design cost breaks down the line items further.

The nearshore question

Several studios in this guide have teams in Ukraine or Poland, with Clutch rates between $25 and $99 an hour against $150–$199 for San Francisco brand studios. The savings are real. They disappear when time-zone overlap is thin or the senior AI experience sits with someone who will not be on your project, so ask for the names of the people who will be.

Chatbot, copilot or embedded AI: which should you build?

Build the AI where the work already happens. A separate chatbot is the default in most briefs and the right answer in fewer cases than most teams expect.

Nielsen Norman Group’s March 2026 usability study of eight site chatbots, including Amazon’s Rufus, found low discoverability, unclear purpose and answers that were often slower than using search or filters. In a January 2026 Salesforce survey, 76% of workers said their preferred AI tools cannot access their company’s data or work context.

Path	Fits when	Real cost you accept
Standalone chat assistant	Questions are open-ended and users already think in questions, as in support or research	Users who do not know what to ask, low discoverability, and every answer is a liability
Copilot in a side panel	Users work in one tool all day and need help drafting or analysing	The copilot is only as good as the data and context it can see
Embedded AI features	The task is repetitive and well defined: summaries, pre-filled fields, suggestions	Less visible to marketing; each feature needs its own review and undo pattern
Agent that acts for the user	Multi-step tasks across systems with clear rules and a clear finish line	Permissions, approval steps, audit logs and a heavier evaluation load

Where chat breaks

Typing a request means users must describe what they want in words, which NN/g calls the articulation barrier. Hybrid interfaces that combine a prompt with buttons, filters and suggested actions lower it. Our piece on dashboard design best practices covers many of the patterns that make embedded AI feel native.

Where agents are the wrong trade

Gartner has warned about “agent washing”, the relabelling of ordinary products as agents, and estimates that only about 130 agentic AI vendors are genuine. In my experience, if a task has fewer than three steps or no clear success state, a good suggestion beats an agent, and it is far cheaper to evaluate.

What should you ask an AI design agency before signing?

These are the questions I would bring to the first call. Each one takes a minute to ask and saves weeks later.

Proof and people

• Which named, live AI products did the people on my team personally work on, and what changed after launch?
• Can we speak to one AI client from the last 18 months?

AI behaviour

• Can you show me the screens you designed for a wrong answer, a refusal and a slow response?
• How do you decide what an agent may do without asking the user?

Research and evaluation

• How do you test model output with real users, and what did you measure on your last AI project?
• Who re-tests the experience when we change the model or the prompts?

Build and compliance

• Do you write production code and integrate model APIs, or who does?
• How will the design meet the EU AI Act’s transparency rules and any AI disclosure laws in our markets?

Pricing and risk

• What do the first three months after launch cost?
• Who owns the prompts, evaluation sets, design files and code at the end?

A partner who struggles with any of these in the first call is showing you something important.

Choosing your AI design partner

Choose the job before the name. If you are a generative AI startup or a large brand with a large budget, MetaLab and Fantasy have the deepest public record. If you need to understand how people will talk to and trust your AI before you build, start with Punchcut or The Gradient. If you need a startup interface that stands out, look at Lazarev.agency; for your first agent workflow, Cieden; for a steady stream of AI features in a SaaS product, Eleken; for a full build on a seed budget, Halo Lab; and if the brand is the bottleneck, Ramotion.

If you are an AI company, or a platform adding AI, and you need brand, product UX and production code from one team, that is the work we do at Fireart, from product design and UI/UX design to web development and MVP development. If you are also comparing studios for SaaS products more broadly, our list of the best SaaS design agencies uses the same method.