4.9/5.0

76+ AI Products Shipped

AI-Native Since 2022

UX DESIGN SERVICES FOR AI Products

Replies within 1 business day

4.9/5.0

76+ AI Products Shipped

AI-Native Since 2022

UX DESIGN SERVICES FOR AI Products

Replies within 1 business day

4.9/5.0

76+ AI Products Shipped

AI-Native Since 2022

UX DESIGN SERVICES FOR
AI Products

Replies within 1 business day

TRUSTED BY LEADING ENTERPRISE AND STARTUPS

TRUSTED BY LEADING ENTERPRISE AND STARTUPS

The Numbers, Briefly.

0+

AI products delivered

globally, with creative excellence

0+

AI products delivered

globally, with creative excellence

0+

AI products delivered

globally, with creative excellence

0M

USD raised in funding

by clients, proving our success

0M

USD raised in funding

by clients, proving our success

0M

USD raised in funding

by clients, proving our success

0+

Countries served

great design knows no borders

0+

Countries served

great design knows no borders

0+

Countries served

great design knows no borders

0+

Five star reviews

trusted by clients on top platforms

0+

Five star reviews

trusted by clients on top platforms

0+

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.

Probabilistic, Not Deterministic

AI outputs vary between sessions. Our UX designs handle uncertainty, surface confidence levels, and plan for "mostly right - not always right." This is the single biggest reason AI products fail post-launch.

Probabilistic, Not Deterministic

AI outputs vary between sessions. Our UX designs handle uncertainty, surface confidence levels, and plan for "mostly right - not always right." This is the single biggest reason AI products fail post-launch.

Probabilistic, Not Deterministic

AI outputs vary between sessions. Our UX designs handle uncertainty, surface confidence levels, and plan for "mostly right - not always right." This is the single biggest reason AI products fail post-launch.

Trust Is the Real Adoption Metric

Research shows most AI features fail due to low user trust, not low accuracy. We design for explainability, reversibility, and the handoff moment when a user decides whether to trust the model's output.

Trust Is the Real Adoption Metric

Research shows most AI features fail due to low user trust, not low accuracy. We design for explainability, reversibility, and the handoff moment when a user decides whether to trust the model's output.

Trust Is the Real Adoption Metric

Research shows most AI features fail due to low user trust, not low accuracy. We design for explainability, reversibility, and the handoff moment when a user decides whether to trust the model's output.

Prompt-as-Interface

When the input is a sentence, every UX convention breaks. We design prompt surfaces, output controls, regeneration patterns, and multi-turn flows that feel intentional - not magical.

Prompt-as-Interface

When the input is a sentence, every UX convention breaks. We design prompt surfaces, output controls, regeneration patterns, and multi-turn flows that feel intentional - not magical.

Prompt-as-Interface

When the input is a sentence, every UX convention breaks. We design prompt surfaces, output controls, regeneration patterns, and multi-turn flows that feel intentional - not magical.

Our AI UX Design Services

Groto's AI UX design services include AI product design, copilot UX, agent UX, AI UX audits, and enterprise-ready design for AI SaaS. Every engagement is led by senior designers with shipped AI product experience.

AI Product UX Design

For teams shipping net-new AI features or AI-native products.

Discovery and AI feature strategy

End-to-end user flows and wireframes

High-fidelity UI design

Design system for AI interactions

Copilot & Agent UX Design

For products embedding AI assistants, copilots, or autonomous agents.

Prompt surface design

Trust, confidence, and explainability patterns

Multi-turn conversation flow design

Output controls (edit, regenerate, accept, reject)

AI UX Audit

For AI products with shipped features but weak adoption.

Adoption and drop-off analysis

AI-specific UX heuristics audit

Prioritized fix roadmap with impact estimates

Enterprise-Ready Design for AI SaaS

For Series A+ AI companies selling to enterprise buyers.

Role-based access and admin flows

Dashboard & analytics UX

Explainability for regulated industries

Security and compliance-compatible design

MVP to Scale Redesign

For AI startups rebuilding UX post-product-market-fit.

UX debt audit

Engineering handoff specs

Redesign roadmap

Component library and design system

Ask an AI if Groto is right for you.

Founders research with AI now. So go ahead - ask ChatGPT whether Groto is the right AI UX partner for your product. We'll stand by the answer.

Work That Proves It

Since 2022, Groto has designed UX for 76+ AI and SaaS products, working with AI founders from pre-seed to Series C.

Artificial Intelligence

Web Design

SaaS

Transforming UX & conversion for AI dubbing

See how Groto redesigned Camb.ai AI dubbing platform—real-time, 140+ languages, user-friendly UI, and higher engagement for global content creators

Artificial Intelligence

Web Design

SaaS

Designing an AI-Driven Hiring Experience

If you thought you couldn’t get smart hiring and smooth UX in one app - think again. Groto built Pathways’ AI-powered B2B hiring platform from scratch. 

SaaS

Designing a PR-First AI Tool That Thinks Like a Teammate

Who says powerful AI has to feel complicated? We redesigned Barista into a PR-first platform that works like a teammate, helping PR teams move faster with clearer

Watch What Happened When We Actually Listened.

Megan Love

VP Marketing, Axero Solution, USA

VP Marketing, Axero Solution, USA

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.

01

No commitment required

02

Founder answers — not a salesperson

03

Response within 1 business day

AS RECOGNISED BY

TRUSTED BY LEADING ENTERPRISE

AND STARTUPS

  • 100% Job Success

  • 100% Job Success

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

Duration: 3-5 days

We audit current UX, study your model's behavior, and map the real user problem. Deliverable: written strategy document before any design work begins.

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.

Duration: 3-5 days

02

UX Architecture and AI Flows

Duration: 1 week

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.

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.

Duration: 1 week

03

Interface Design and Design System

Duration: 2-3 weeks

High-fidelity UI, interaction design, micro-copy for AI states, and a design system built for probabilistic outputs.

03

Interface Design and Design System

High-fidelity UI, interaction design, micro-copy for AI states, and a design system built for probabilistic outputs.

Duration: 2-3 weeks

04

Testing and Engineering Handoff

Duration: 3-5 days

User testing on real flows, engineering specs, and a 30-day post-launch support window.

04

Testing and Engineering Handoff

User testing on real flows, engineering specs, and a 30-day post-launch support window.

Duration: 3-5 days

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

GenAI and AI-Powered SaaS Startups

Founders building AI-native products, Seed through Series B.

Seed Series B

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

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

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

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 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 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 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 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 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 HR & Recruitment AI

Screening copilots

Interview AI

Workforce analytics

AI UX for EdTech AI

Adaptive learning

AI tutors

Assessment tools

AI UX for EdTech AI

Adaptive learning

AI tutors

Assessment tools

AI UX for Customer Support AI

Agent assist

Deflection UX

Escalation design

AI UX for Customer Support AI

Agent assist

Deflection UX

Escalation design

  • ChatGPT

  • Claude

  • Gemini

  • Meta

  • Mistral

  • DeepSeek

  • Qwen

  • Gemma

  • Midjourney

  • Pinecone

  • Langchain

  • Vercel

  • StableDiffusion

  • Whisper

  • ChatGPT

  • Claude

  • Gemini

  • Meta

  • Mistral

  • DeepSeek

  • Qwen

  • Gemma

  • Midjourney

  • Pinecone

  • Langchain

  • Vercel

  • StableDiffusion

  • Whisper

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.

Why Founders Pick Groto Over Larger Agencies

The same reasons AI-native founders choose a boutique over a generalist shop or an enterprise consultancy.

Generalist Design Agencies

WHAT YOU GET:

AI is one of many services; added on, not built in

Account manager layer, junior designer execution

6-10 weeks with review cycles

Volume-driven, attention split across accounts

Design screens, not the AI stack

Enterprise Consultancies

WHAT YOU GET:

Broad AI strategy, rarely hands-on product UX

Layers of consultants; designers you never meet

3-6 months, enterprise pace

High cost, heavy process, slow decisions

Strategy decks, limited build fluency

WHAT YOU GET:

AI-native - we design AI products (copilots, agents, AI SaaS)

The founder, directly - senior-only team, no handoffs

Focused sprints in 15-30 days

Boutique depth, senior attention on every pixel

We speak prompts, context windows, latency, hallucination, RLHF

How We Engage

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.

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.

Focused Sprint

For teams with one clear AI UX problem: a copilot flow, onboarding redesign, or specific feature. Senior-led, fixed scope.

Min Duration: 2-3 weeks

Product Partnership

Min Duration: 4-8 weeks

For end-to-end AI product design — from discovery to shipped UI. Custom scoped by product complexity.

Product Partnership

Min Duration: 4-8 weeks

For end-to-end AI product design — from discovery to shipped UI. Custom scoped by product complexity.

Product Partnership

For end-to-end AI product design — from discovery to shipped UI. Custom scoped by product complexity.

Min Duration: 4-8 weeks

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.

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.

Ongoing Design Partner

For Series A+ teams needing senior AI UX embedded with their product team. Monthly retainer, senior-only.

Min Duration: 3 months + retainer

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.

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.

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.