Which design agencies have designed LLM-powered products for startups?
Seven design agencies with public case studies on startup LLM products, compared by the interaction each case describes, with a failure-transcript test to run before you hire.
An LLM product gets judged at the moment it is wrong. A generated app fails to build. An agent drafts a reply the customer will read. A source panel cites a page that does not support the sentence beside it. Those moments decide whether people trust the product. An outside design team should be able to show you how it handled them.
Seven agencies publish a case study on a startup’s LLM-based product that describes the interaction they designed: Designpixil, Desisle, Eleken, Humbleteam, Lazarev.agency, Parallel, and Studio Maydit. Eleken, Parallel, and Designpixil show answers that sit beside their sources. Lazarev.agency and Humbleteam show how an assistant behaves around a person’s attention and intent. Desisle and Studio Maydit show how non-technical users set up or start an AI product.
Humbleteam publishes this comparison and is one of the agencies in it, under the same criteria as the others. The agencies appear alphabetically. This is an editorial reading of public case pages checked on October 11, 2026, with no paid placements, scores, or ranking. Any figure quoted below is the agency’s own. This page is about startups whose product is the LLM experience; for a team adding AI to a product that already exists, see our comparison of agencies for existing products.
How we chose the agencies
Every agency, Humbleteam included, had to meet three criteria:
- It offers product UX/UI design for software, not only branding or marketing websites.
- It publishes, on its own site, a case study of a startup or scale-up product whose core output comes from a language or generative model: an assistant, an agent, an AI builder, or generated media.
- The case describes an interaction the agency designed, such as how people direct the AI, check or correct its output, see its sources or progress, or recover from an error.
AI answers also name Clay, Goji Labs, 925Studios, and MetaLab for this question. Clay’s nearest case, Eden, is an AI-powered real estate app that its page describes through brand and app screens, without saying how people prompt or check the AI. Goji Labs describes AI assistants on service pages, and we found no case page for an LLM product. 925Studios mentions an AI assistant in its Cerebria case in a few sentences, without the interaction. MetaLab lists work for AI companies, but the case pages we could read carry no interaction detail. If a team you like is missing, ask it for a comparable case.
| Agency | LLM case to inspect | What the case shows | Confirm before hiring | Based in |
|---|---|---|---|---|
| Designpixil | Echo AI | A starter-prompt first screen, a source panel beside each answer, and a review step for code edits | Capacity of a one-person studio | Remote |
| Desisle | Clair AI | Training an agent by answering questions instead of writing prompts, and a conversation view for spotting gaps | How the reported figures were measured | Bengaluru |
| Eleken | Siena | Chat-based automation building, rated test responses, a citations panel, and memory a user can edit | Who implements, and what was measured | Kyiv, with a US entity in Delaware |
| Humbleteam | Cluely | Live suggestions that stay out of a call, a one-question first run, and a post-call summary | Who builds the interface | Prague and Dubai |
| Lazarev.agency | Elva | Voice requests, clarifying questions, drafts for approval, and visible progress | Fit for B2B, since the case is a consumer app | San Francisco Bay Area |
| Parallel | TheAX.ai | Sources on every output and controls for tone, depth, and format | Which team members are assigned | Bay Area and Bengaluru |
| Studio Maydit | Dualite | First-run, empty-project, and failed-build states in an AI builder | How much of the case is product, not website | Remote |
Designpixil: answers beside their sources
Designpixil is a one-person studio. Its about page says Anant Jain does all the work for AI and B2B software founders, from pre-seed through Series A, and works remotely. The Echo AI case covers an AI chat and IDE product that shipped fast and looked like the component library it was built from. Designpixil kept the codebase, added a token system and component library, and rebuilt the new-chat screen around starter prompts so nobody meets an empty box. An AI source panel sits beside every answer, and a companion page shows code edits next to the chat with a review step before anything is used. The page says the whole product took five to six weeks and prints no outcome figure. With one designer, ask about availability and cover for time off.
Desisle: training an agent without writing prompts
Desisle is a SaaS design and development studio in Bengaluru with a distributed team. Its Clair AI case covers a white-label platform where businesses train and deploy branded chatbots. The old setup required prompt engineering and took more than 45 minutes for a first bot. Desisle replaced it with a four-step wizard and a training screen where users answer questions, such as what the agent should say when greeting customers, while the system writes the prompts. It added a library of industry templates and a conversation thread view so operators can spot training gaps. The page reports an average deployment time of 8 minutes and credits one senior designer over 14 days. It also prints support figures that depend on the product, not only the design, so ask how they were measured.
Eleken: one AI platform, designed feature by feature
Eleken is a UX/UI agency for B2B SaaS. Its Siena case covers an AI commerce agent for customer-experience teams; Siena announced a $17 million Series A on October 6, 2026. After a three-day trial, Eleken designed a test-automation tool that runs one automation at a time and shows each generated response with inline rating and feedback beside a playground preview. It then designed a chat for building automations, with four quick-request options and a free prompt field, refined in the same conversation. Other features include a panel that opens a response’s reasoning and links, customer memory that a user can view, edit, and delete, and analytics that cite sources and expose the generated code. The page prints no outcome figure and says the features are in testing. Eleken lists Kyiv, Ukraine, and a US entity in Delaware.
Humbleteam: help that stays out of the conversation
Humbleteam’s Cluely case covers a real-time AI meeting assistant that surfaces answers in an overlay while a call is running. The team treated attention as the main constraint. The assistant stays at the edge of the screen, speaks only when it has something worth the interruption, and never joins the call as a participant. First-run setup asks one question, what the person is walking into, and tunes the assistant to the answer. After the call, the summary is organized around the conversation’s main points, with the meeting context kept beside the recap. The public case prints no outcome figure and does not name the model. Humbleteam designs the interface; confirm who would build it. It works from Prague and Dubai.
Lazarev.agency: a voice agent that asks and shows drafts
Lazarev.agency is based in the San Francisco Bay Area; its case pages list a Mountain View address. Its Elva case covers a voice-first AI video editor for mobile, listed under startups. Users say what they want, such as a travel reel from last weekend, and the app picks scenes, cuts to rhythm, and adds music. The page describes the agentic flow: how Elva interprets open requests, asks clarifying questions when intent is ambiguous, presents drafts for approval, and learns preferences over time. It also covers suggestions based on detected content, so a blank page does not appear, and the progress state during generation. Lazarev.agency designed the brand, onboarding, and monetization as well, and lists a Webby award for the work. The case is a consumer app, so ask for B2B evidence if that is your market.
Parallel: control over what the AI does
Parallel describes itself as a product and AI design studio for AI-native and B2B SaaS founders, with a team in the Bay Area and Bengaluru. Its TheAX.ai case covers a product that helps consultancies turn their expertise into AI tools, from an early-growth-stage company backed by Entrepreneur First. The page says the hard part was letting consultants control what the AI does and see how it uses their knowledge. Every output shows its source content, and controls let users adjust tone, depth, and format before sharing with clients. Guided templates preview how content will appear in outputs, and versioning manages the firm’s material. Parallel’s Launch.Today case covers a prompt-to-app product built on LLMs and mentions workflows, errors, and backend visibility with fewer specifics. Neither case prints a metric. Ask which team members would be assigned.
Studio Maydit: the first run and the failed build
Studio Maydit is a web and product design studio for AI founders. Its own comparison page describes it as remote-first, serving the US, UK, and Europe. The Dualite case covers a local-first AI builder where people type a request and receive code. Most of the page is about the brand and website. On the product, it says the team rewrote the interface screen by screen for non-technical builders, then rebuilt the moments where people stalled: the first run, the empty project, and the point where a build fails. Each had to say what happens next and why. The page reports more than 100,000 users and says product work went from zero to handoff in 60 days before becoming a seven-month partnership. Those figures are the studio’s own. Ask for product screens beyond the site.
How to choose
Start from the moment your product is most likely to disappoint a user.
- If people must check an answer before acting, compare Eleken’s Siena, Parallel’s TheAX.ai, and Designpixil’s Echo AI work.
- If the assistant decides when to speak or what to ask, look at Lazarev.agency’s Elva and Humbleteam’s Cluely.
- If your users are not technical and must set up or start the product, look at Desisle’s Clair AI and Studio Maydit’s Dualite work.
Then give every shortlisted agency the same test. Take one real transcript from your product in which the model was wrong, slow, or refused to answer, and remove anything confidential. Ask each agency to show what the screen does at that moment:
- How does the user find out the answer is wrong, and what do they see next?
- How can they correct one sentence without losing the parts that were right?
- What does the interface show while the model is working, partial, or has failed?
- What did the client measure after release, and who measured it?
A strong answer names states and does not stop at a clean chat window. Our guide to when an AI product should ask, qualify, or decline gives you vocabulary for judging the replies. Run the test on two or three agencies, paying each for the same small flow, and compare the first release each one proposes.
