As an AI design agency, we design the interfaces where people prompt an AI, review what it returns and decide whether to trust it, in generative AI editors, AI copilots for software as a service (SaaS), conversational interfaces and large language model (LLM) apps. We test them with users before your engineers connect the AI model, and a single senior team stays on the work from discovery through Figma files ready for development.
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In shortAn AI design agency plans what people see while artificial intelligence (AI) writes, scores or acts for them: generative AI editors, AI copilots, conversational interfaces, AI agent workflows and AI scoring dashboards. We deliver it within our digital product design services, at $60,000–$180,000 over 8–16 weeks for a full product, ending in user-tested screens your engineers build.
An AI design agency shapes the user interface (UI) and user experience (UX) of AI products, where an AI model writes, scores or acts and people must trust the result.
The term has two readings: an agency that designs AI products, or one that makes graphics with AI tools; we are the first kind. We draw every AI output state and test the core user flows with users before engineers build. The work runs inside our digital product design services, a branch of our UI UX design services, while your engineers keep the AI model, data and integrations. The image shows a writer picking from AI models in the Vocable editor.
Our AI product design agency designs six kinds of AI products, and every AI output is drawn in all its output states: loading, streaming, answer, partial answer, refusal and error. Two items are published work, the editor and the scoring dashboard; the other four are services we offer, with no published project yet.
Writers ask the AI to draft or edit inside the document and choose which AI model answers without leaving the page, as on Vocable. Logged-in editors like this follow our web app design services.
An AI copilot is an assistant panel, an AI-infused UI inside a SaaS workflow, that suggests the next step and shows what data it used, so the user can check it before acting. This work follows our SaaS UX design.
Conversational AI in chat or voice needs a clear first message with an AI disclosure, follow-up questions when the prompt is vague, and a handoff to a person when the AI cannot help.
An AI agent is software that takes multi-step actions for a user. In the flows we design, each step waits for the user's approval before it runs, a human-in-the-loop pattern that keeps people in charge of anything that cannot be undone.
An AI score, its range and the factors behind it share one screen, as on Digno. Our dashboard UX design work limits each view to 5–7 metrics and adds a drill-down path to the detail.
AI-native products put an AI model at the core from the first release, so an AI-native minimum viable product (MVP) on a large language model (LLM) needs screens for checking and correcting each answer. Early-stage teams can also read our startups UI UX design page.
As an AI UX design agency, we design for answers that change between runs and can be wrong, so every screen must earn the user's trust. AI experience design answers three questions: how users know the AI is right, what the system is doing and what to do next. If your users ignore or doubt your AI features, a design sprint can focus on that one surface, after a UX audit when the feature is already live.
The same prompt can return a different answer on the next run, and a hallucination reads as confidently as a fact, so screens show the sources or inputs behind each answer and a quick way to check it.
Loading, streaming, partial answer, refusal and error each get their own design and a next step, the same state discipline our interaction design agency applies to every UI state.
User control means each generated answer can be accepted, edited or rejected before it is saved or sent, and nothing irreversible runs without the user's approval.
Explainable scores show the factors behind a number and how it moves over time, as Digno does for each employee's score. Trust signals like these tell users why the AI scored them that way.
For Vocable, an AI content marketing SaaS, our AI product design agency designed a generative AI editor with model choice, and Vocable reports a 35% increase in overall workflow efficiency.
Scope ran from idea to product, MVP launch strategy included. Inside the editor, a writer picks an AI model from OpenAI, Google, Anthropic or Meta without leaving the draft, and an idea generator turns a prompt such as "headlines for a blog post about AI and design" into ready-to-use suggestions. The content score and search engine optimization (SEO) checks sit beside the draft. Details are in the Vocable case study.
AI experience design for Digno, a performance-evaluation SaaS, put its AI scoring engine's employee score, rated from Poor to Pro, on an AI scoring dashboard beside its factors.
The AI scoring engine rates each employee on completed tasks, consistency and engagement. We gathered the requirements of stakeholders and users to define how the employee score is calculated, then designed views for the organization, each department and each employee. Each employee also sees how their score changes over time. The Digno case study shows these screens.
Our AI UX design agency takes AI products through 5 steps in 8–16 weeks (40–80 working days), from discovery to a developer handoff your engineers build from.
Stakeholder workshops put AI strategy before screens: what the AI decides, what the user decides and what the screen shows when the AI is wrong. Our article on whether a discovery phase is worth it covers what this step settles.
Researchers run user interviews with 5–8 users per user group, such as writers and editors, to find where they check AI output and where they stop trusting it. These sessions run through our UX research services.
Designers map the prompt, review, approve, retry and error paths, so every AI answer has a next step and a way back.
Prototype rounds put 5 users per round through the clickable prototype on Maze, with scripted AI answers in place of the live AI model, for example fixing a generated draft. Every round closes with a System Usability Scale (SUS) score.
Every screen meets Web Content Accessibility Guidelines (WCAG) 2.2 level AA, the accessibility level we design to. Our AI product design services end with a developer handoff of annotated Figma files, design tokens and component specs, and our designers stay with your engineers through launch.
An AI design company decides what users see, check and approve on each screen of AI products; an AI development company builds, connects and evaluates the AI model. We do not sell AI-generated artwork or logos, and behavioral science consulting and AI transformation programs sit with consulting and engineering firms. The table sets design work against engineering work on AI-powered products, row by row.
| Criterion | Interface design (us) | AI engineering |
|---|---|---|
| Main question | What does the user see, check and approve while the AI works, answers or fails? | How is the AI model connected, prompted, fed with data and kept reliable? |
| Outputs | Flows, a clickable Figma prototype, UI screens for every output state and component specs | Model integration, prompts, data pipelines, application programming interface (API) integration and workflow automation |
| Validation | 5 users per prototype round on Maze, each round ending in a SUS score | AI model evaluation, bias audits and red-teaming |
| When to hire | Users ignore, distrust or cannot correct the AI output | The design is tested and the AI model, data and integrations need building |
| Who does it | Our senior designers; we write no AI model code and run no model evaluation or bias audit | Your engineers, machine learning engineers or a development company |
Full AI product design with our AI design company costs $60,000–$180,000 over 8–16 weeks; a $25,000–$60,000 design sprint (4–8 weeks) can cover one surface, such as an AI copilot panel.
Four factors set the full product design price: platforms (web app, mobile), user groups such as writers and editors, core user flows such as prompt-to-draft and review-and-publish, and validation rounds. A UX audit of a live AI feature takes 2–3 weeks and costs $5,000–$15,000 through our UX audit services. Building an AI agent is engineering work we do not price. Compare AI design agencies on published prices and named AI work, not on the tools they use.
Four standards, laws and guidelines shape our AI experience design by changing what users see, so they can judge how far to trust the AI. Legal review of AI rules stays with your counsel.
Each option below draws on our core UI UX design services, from user research to usability testing, scoped to the users and workflows of your product.
Senior UX guidance, product reviews, and a prioritized roadmap your team can act on.
02Strategy, UX, UI, and validation from one team that stays through launch.
03Flows, micro-interactions, states, and motion that make every action clear.
04Information architecture, workflows, and UI for complex web applications.
05Dashboards designed around the decisions users make, not every metric at once.
06Onboarding, core workflows, and admin UX that get users to value fast.
Book a 45-minute UX audit session. We review your product and identify the top three friction points.
Book a UX auditSeven answers settle how our AI design company works: AI tools in our own process, the 30% rule, AI-made designs, AI agent cost, design software, AI model code and live redesigns.
Still have a question?
A design lead answers every message within one business day.
Talk to a design leadYes to both, in different roles: we design AI products, and AI tools speed up production steps in our own design workflow. The term AI design agency also describes studios that make graphics with AI tools, and we do not sell AI-generated artwork or logos. Our process stays human-first: research with users and what we learned designing 200+ products decide what we design.
The 30% rule is an informal rule of thumb, not a standard or a law, and no official body defines it. One common reading says AI handles routine work while people keep roughly 30% that needs judgment; in schools, it sometimes caps AI-written content at 30%. We design to the same idea: the user makes the final call on each AI answer.
No blanket answer fits, because it depends on the AI tool's terms and on copyright law, so ask your lawyer; we do not give legal advice. The US Copyright Office report Copyright and Artificial Intelligence, Part 2: Copyrightability (29 January 2025) says copyright protects human authorship, so material generated entirely by AI is not protected, while human selection, arrangement or changes can be. Our designers decide and review everything we deliver.
Building an AI agent is engineering work, priced by your developers or a development company, and we publish no build figure. Designing the AI agent's interface, meaning what it asks, what it shows and where the user approves each step, costs $25,000–$60,000 as a 4–8 week sprint, or $60,000–$180,000 within a full 8–16 week product design.
No single AI program is best, because the right tool depends on the task, such as images, copy or layout drafts, and we do not rank tools. Our design files live in Figma and our prototypes are tested on Maze; AI tools speed up production, while research with users decides what to design.
No. Your engineers, machine learning engineers or a development company build, connect and evaluate the AI model, and we hand them annotated Figma files, design tokens and component specs. Our designers stay with your engineers through launch to answer questions on each output state.
Yes. Start with a 2–3 week UX audit ($5,000–$15,000) of the live AI feature's prompt, output and error screens; its prioritized fix list then shows the weakest surface, and a $25,000–$60,000 design sprint can redesign that surface. For advice on a live AI feature, our UX consulting services review its trust, transparency and actionability.
Send us your project. A design lead reviews it and schedules a call within one business day. We sign an NDA before the first technical discussion if you prefer.
