The Numbers, Briefly.
AI products delivered
globally, with creative excellence
USD raised in funding
by clients, proving our success
Countries served
great design knows no borders
Five star reviews
trusted by clients on top platforms
What Makes AI UX Design Different
Groto is one of the few design studios in the world built exclusively for AI products. Designing for AI means solving problems traditional UX playbooks don't name. These are the three shifts we design for that most agencies miss.
AI UX for MedTech AI & Healthtech
Clinical decision support
Patient-facing AI
Diagnostic tools
AI UX for FinTech & Banking AI
Risk copilots
Fraud detection UX
AI advisory tools
AI UX for SalesTech & Marketing AI
UX Code copilots
AI debugging
Developer agent UX
Watch What Happened When We Actually Listened.
Super responsive, detail-oriented, and great planners. Definitely recommend working with this amazing team!
Megan Love
VP Marketing, Axero Solution, USA
Ready to see what we can do for your product?
Founder-led. Every engagement starts with a honest 30-minute conversation.
How We Work
The Groto AI UX Design Process is a four-stage framework developed over 76+ AI product engagements. Each stage is senior-led, timeboxed, and built for founders who need to ship - not explore.
01
AI Product Discovery
We audit current UX, study your model's behavior, and map the real user problem. Deliverable: written strategy document before any design work begins.
02
UX Architecture and AI Flows
Wireframes, prompt surfaces, multi-turn conversation flows, and trust patterns. We stress-test for edge cases AI products always hit — empty states, failed outputs, hallucinations, low-confidence responses.
03
Interface Design and Design System
High-fidelity UI, interaction design, micro-copy for AI states, and a design system built for probabilistic outputs.
04
Testing and Engineering Handoff
User testing on real flows, engineering specs, and a 30-day post-launch support window.
Who We Work Best With
We design AI UX for products across different verticals - each with distinct trust requirements, user behaviours, and compliance constraints.
GenAI and AI-Powered SaaS Startups
Founders building AI-native products, Seed through Series B.
Seed Series B
CTOs and PMs Launching Internal AI Copilots
Teams embedding AI assistants into existing enterprise or product workflows.
Enterprise Teams
Product Leaders Designing for Trust and Compliance
Companies designing AI features for regulated industries.
Regulated Industries
Innovation Teams Integrating AI into Existing Product Suites
Product organizations adding AI features to shipped products.
Existing Products
AI UX Across Industries
We design AI UX for products across different verticals - each with distinct trust requirements, user behaviours, and compliance constraints.
AI UX for MedTech AI & Healthtech
Clinical decision support
Patient-facing AI
Diagnostic tools
AI UX for FinTech & Banking AI
Risk copilots
Fraud detection UX
AI advisory tools
AI UX for SalesTech & Marketing AI
UX Code copilots
AI debugging
Developer agent UX
AI UX for DevTools & Code AI
Pipeline AI
Content generation
Outreach copilots
AI UX for Legal & Compliance AI
Contract review AI
Compliance copilots
Audit tools
AI UX for HR & Recruitment AI
Screening copilots
Interview AI
Workforce analytics
AI UX for EdTech AI
Adaptive learning
AI tutors
Assessment tools
AI UX for Customer Support AI
Agent assist
Deflection UX
Escalation design
Work Directly With the Founder
Harpreet Singh (Harry) is the founder of Groto. He has led UX design for AI and SaaS products since 2022, previously led strategy at Budweiser, Colgate, etc. He founded Groto in 2022 after seeing AI-native founders repeatedly hire generalist design agencies and ship products users couldn't figure out.
AI UX is a new discipline, not a feature add-on. If you're building something AI-native and need senior design thinking from day one, I'd like to talk. Book a 30-minute call with me directly - not a salesperson. We'll look at your product, and I'll tell you honestly if we're a fit."
How We Engage
Groto offers three engagement models based on scope, timeline, and product stage. Every engagement begins with a scoped discovery call.
Focused Sprint
Min Duration: 2-3 weeks
For teams with one clear AI UX problem: a copilot flow, onboarding redesign, or specific feature. Senior-led, fixed scope.
Product Partnership
Min Duration: 4-8 weeks
For end-to-end AI product design — from discovery to shipped UI. Custom scoped by product complexity.
Ongoing Design Partner
Min Duration: 3 months + retainer
For Series A+ teams needing senior AI UX embedded with their product team. Monthly retainer, senior-only.
Just sayin'
Where we spill our design secrets, share what's hot, and occasionally humble-brag about our coolest projects.
FAQ
We’ve heard it all. Here’s everything you need to know before working with us.
What deliverables do we actually receive from an AI UX engagement?
You receive a complete, build-ready design package, not just screens. A typical AI UX engagement delivers: user research and journey maps, a validated interaction model for the AI feature, high-fidelity UI designs and prototypes, a component-based design system (Figma), prompt and conversational UX specs, error and edge-case states (hallucinations, empty, loading, failure), trust and explainability patterns, and developer handoff docs with acceptance criteria. Every deliverable is versioned and tied to measurable UX goals so your team can build and ship without guesswork.
Will senior designers actually work on our project, or is it handed to juniors?
Yes, senior designers work directly on your project. Every engagement is led hands-on by a senior product designer with real AI/ML product experience, not a junior team supervised from a distance. The person in your kickoff is the person doing the research, designing the flows, and presenting the work. We keep teams small and senior on purpose, because AI UX involves ambiguous, high-stakes decisions (trust, error handling, model behavior) that require experience to get right the first time.
How do you collaborate with our AI/ML engineering team during the design process?
We embed directly with your AI/ML engineers from day one, treating model capabilities and constraints as core design inputs. In practice this means: joining your standups or a shared Slack channel, co-defining what the model can and cannot reliably do, designing around real latency, token limits, and confidence scores, and reviewing prototypes against live model outputs instead of ideal mockups. We design the interface and the model behavior together, so what we hand off is technically feasible and matches how your system actually responds.
How do you test and validate AI product UX with real users?
We validate AI UX with real users using prototypes connected to live or simulated model outputs, so testers react to genuine AI behavior including errors and uncertainty. Our process combines moderated usability sessions, task-based testing (activation, trust, task completion), and measurement of AI-specific signals like acceptance rate and correction behavior. We deliberately test failure paths, hallucinated or low-confidence responses, to confirm users can recognize, recover from, and stay in control of the AI. Findings are prioritized and fed back into design before you build.
How do you design prompt interfaces and conversational UX?
We design prompt and conversational interfaces around user intent and model capability, not a blank text box. This includes input affordances that guide users toward prompts the model handles well (examples, suggestions, structured inputs), clear system responses, editable and re-runnable outputs, and graceful handling of ambiguous requests. For conversational UX we design turn-taking, memory cues, correction and clarification flows, and visible boundaries of what the AI can do. The goal is to reduce the "blank page" problem and help users get a useful result on the first try.
How do you handle AI errors, hallucinations, or failed responses in the design?
We treat AI errors and hallucinations as first-class design states, not edge cases. Every AI feature we design includes explicit patterns for low-confidence answers, wrong or fabricated outputs, empty results, timeouts, and total failure. Tactics include confidence indicators, source citations users can verify, easy correction and regeneration, clear fallback paths to human or manual options, and honest messaging when the AI is unsure. Designing these states upfront protects user trust when (not if) the model gets something wrong.
How do you design for AI memory and context across sessions?
We design AI memory and cross-session context to be visible, controllable, and trustworthy. Users should always understand what the AI remembers, why it remembers it, and how to view, edit, or delete that context. Our designs cover onboarding memory, persistent context across sessions, per-conversation context, and clear indicators when the AI is drawing on past information. We balance the convenience of personalization against privacy and control, so memory feels helpful rather than surveillant, and users never lose track of what the system knows about them.
How do you build trust and explainability into AI features (citations, confidence indicators)?
We build trust and explainability directly into the interface using citations, confidence indicators, and transparent reasoning. Concretely, we design source links users can verify, visual confidence levels on AI outputs, plain-language explanations of how a result was produced, and clear labeling of AI-generated content. We also design for user control: easy ways to correct, override, or dismiss the AI. Explainability is treated as a UX requirement, because users adopt and rely on AI features only when they can understand and verify what the system tells them.
Do you work with teams building on GPT, Claude, Gemini, or open-source models?
Yes. We work with teams building on GPT (OpenAI), Claude (Anthropic), Gemini (Google), and open-source models like Llama and Mistral, as well as fine-tuned and custom in-house models. Our design approach is model-agnostic: we adapt the UX to each model's real strengths, latency, and failure patterns rather than assuming one behaves like another. Whether you are on a hosted API or a self-hosted open-source stack, we design around how your specific model actually performs in production.
Will you sign an NDA, and how do you handle confidential models, prompts, or datasets?
Yes, we sign NDAs and routinely handle confidential models, prompts, and datasets. Before sharing sensitive material, we execute your NDA (or provide ours) and agree on data handling terms. In practice we work within your access controls, use only the data required for the design work, avoid retaining proprietary prompts or datasets beyond the engagement, and never use your confidential information to train models or in other clients' projects. We are comfortable working inside your security and compliance requirements.
Ready to Build UX That Makes Your AI Click
Groto is the AI UX design partner for founders building the next generation AI products. If you’re designing something AI-native and want senior design thinking from day one.
30 minute. No pitch deck. Just your product and honest advice.