10+ years
of experience
in UI/UX design
in UI/UX design
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UI UX design services
Become a client
UI UX design services for AI-native B2B products turn a strong model into a product people return to. Lazarev.agency delivers senior product design, AI UX pattern depth, and experience-driven insights from 30+ AI products in production since 2017 — UI UX design services for teams who need their interface to do real work: higher adoption, retention, and demos closing rounds.
- Compress 12 months of design into 4–8
Most B2B product teams can't burn a year on UX before a launch or a round. Our UI UX design services come in as a senior pod, with product UX, design system audit, and demo flows running as one engagement, so the product reaches users when the milestone hits. - Make AI features users notice and trust
Adoption is the number every AI product gets judged on. Our UI UX design services produce copilots and recommendation engines customers reach for in their daily work and usage data climbs from the first release. - Extend the design system you already ownYour team keeps the design direction. We take the volume. Our UI UX design services work inside your tokens, follow your naming conventions, and produce AI UX patterns your team can extend without reverse-engineering a system we invented.
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$500M+
in funding secured
for our clients
for our clients
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120+
awards backing
our excellence
our excellence
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2015
founded, 10+ years
of experience
of experience
/ 04
San Francisco, CA
AI product design agency
/ 05
full-cycle product design
from user research to production-ready design systems
Lazarev.agency designs the best UI for AI products. Officially.
2026 Webby Winner, AI — Visual Design. Three years of Webby recognition for AI product design.
Among our clients:
Awards
& Recognition
& Recognition
Our team’s work was honored with most of the world-known trophies
120+
Awards won all time
CSS Design
Website of the year
Awwwards
Agency of the year nominee
The Webby Awards
The FWA
Awwwards
Red Dot Awards
Behance
the drum awards
Where UI UX work for AI products keeps breaking down
UI UX design services were built for a single product version. AI products release a new version every cycle — sometimes weekly. The interface ages faster than the model behind it. New AI features reach production but stay invisible to users because the menu was designed before they existed. With each release, the sales pitch drifts further from what trial users see.
AI features users can't find
Powerful copilots and recommendation engines sit two clicks deep behind menus designed before the AI launched. Users don't know the features exist, so usage reports stay flat, and Heads of AI explain "low adoption" in QBR after QBR while the C-suite asks where the AI investment went. Standard interface work treats the AI as another menu item and misses the workflow shift underneath.
Founders pitching a product out of sync with the deck
Investors love the AI, but the demo undercuts it. Latency spikes, fallbacks look like errors, and the conversational flow assumes inputs no one types. Founder time goes into rehearsing the demo, build time slips, and the round moves one quarter to the right.
Internal design teams maxed out on tickets
The design lead has a vision for AI patterns: confidence states, override flows, citation surfaces. The team ships incremental UI work while competitors release AI-native experiences. The lead absorbs the design debt every release adds, with no calendar to draft the AI library they keep sketching in their head.
Finished-looking mockups failing in production
Figma files arrive, engineering builds, your product launches, and users hit the real model. The experience collapses under the pressure of hypothetical (and never proven) design solutions. Failure modes were missing from the prototype. The redesign performs worse than the original system because it assumed a perfect model, and no such model exists at inference time.
How we design UI UX for AI products
We start with how users reach value inside an AI product, then design the surfaces getting them there. Pattern recognition across multiple products in production for the last 10+ years lets us spot failure modes before code, and lower the chance the next release lands as another unused toggle in a settings menu.
Design behaviors before screens
Before any pixel, we map who uses the product, what they’re trying to do, where the AI helps, and where it gets in the way. The work starts in workflows, prompts, latency budgets, guardrails, and failure modes. Screens come after the product’s behavior is clear.
Prototype with realistic data and synthetic users
High-risk flows get tested with realistic or synthetic data before engineering touches them. Synthetic user simulations reveal where the interaction breaks, which patterns confuse new users, and which surfaces buyers will sit with live. Risk on uncertain flows drops before the team commits engineering time.
Build a design system AI can scale on
We extend the design system you already own by adding tokens and variants for AI behavior. Internal design leads keep control. Your team launches future AI features faster with less debate and fewer reverts.
Hand off work engineering can build
Annotated Figma, documented states for loading, empty, partial, uncertain, and override. UX QA on staging. Specs your engineers can implement without reinventing AI UX choices on every release. The work moves to code with fewer bugs and tighter feedback loops once usage data starts flowing.
Case studies
UI UX design services in action
The case studies below are proof of what AI-native UI UX design services produce once they reach real users. An AI risk platform grew AI usage after a workflow redesign. A B2B AI copilot raised Series B on a demo-led product. Others turned complex AI systems into tools people trusted and returned to.
The four programs you can engage us for
Why teams come to us for AI product UI/UX
Teams come to us when the model is strong, but users still don’t “get” the product. When the next funding round depends on a demo investors can believe in. When adoption needs to improve before the next QBR. Or when an internal design team needs senior AI UX support without giving up ownership of the system.
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Through a detailed understanding of the client’s platform, Lazarev. was able to create a clean and intuitive UI/UX design that ticked all the boxes. The team was receptive to all requirements and requests and adapted well to timeline changes. They produced accurate mockups at every iterative stage.
/ 02
Lazarev. completed the project on time and within budget. They have an admirable structure and process that worked effectively and creatively. The team is also available and responsive despite their huge time zone difference.
The axis our UI UX design work spans
The UI UX design portfolio maps onto a single axis: how dense the data gets and how much weight the user puts on every decision. At the dense end sit AI and ML platforms, fintech, healthcare, and logistics. In the middle: edtech, legal tech, ad/martech, and enterprise SaaS. At the lighter end, where adoption still rides on clarity: real estate, media and content, travel, and Web3. The work scales across the whole axis.
Who we are and why teams like yours work with us
We exist for B2B teams under pressure to turn an AI roadmap into visible product usage, expansion, and a safer story in front of the C‑suite and investors. If design isn’t moving revenue, adoption, or retention, it’s decoration. We design to avoid that. Since 2015, we’ve shipped 600+ products and earned 120+ awards for work on complex, data-heavy tools: fintech platforms, AI copilots, decision engines, and vertical SaaS. Our work has helped clients turn “we have AI features” into “our customers actually use and pay for them.”
We started designing AI products in 2017, long before “AI-native” became a buzzword. With 30+ AI products shipped, we focus on the hard part most teams struggle with: making complex intelligence feel simple, trustworthy, and obviously valuable in a demo, a POC, or a QBR. We’re a 40+ person team of UX strategists, product designers, and analysts who treat design as a business function. Every engagement is anchored to the metrics you care about: AI feature adoption, activation and retention in key accounts, time-to-decision in core workflows, and upgrade/expansion tied to AI-powered plans.
120+
international
industry awards
industry awards
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600+
projects
successfully completed
successfully completed
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We operate on a simple principle: if you're not measuring design against business outcomes, you're wasting money.
What sets us apart from a typical agency or a single in-house hire is pattern recognition at scale. We’ve seen what works – and what quietly kills adoption – across hundreds of AI and data-heavy products. That lets us spot failure modes early, bring proven interaction patterns to your team, and reduce the risk that your next AI release is another unused toggle in a settings menu.
We start with research not because it’s “best practice,” but because designing without understanding your users, your market, and your revenue model is just guessing with nicer pixels. From there, we collaborate with your product, AI, and design leaders to define where AI should show up, how it should behave, and how to make it obvious, safe, and monetizable.
If you’re a Head of AI, Product, or an AI-native founder who needs AI capabilities to be seen, understood, and used now, not someday, we’re built to be that partner.
From kickoff to production-ready release
A predictable rhythm runs every UI UX engagement. We start with what you’ve built, identify the workflows where AI moves the metric, prototype the risky surfaces first, extend your design system to carry the new patterns, and stay through implementation and rollout so the work lands in production the way it landed in Figma.
Until users find the AI, the AI doesn’t exist.
01
Intake and product UX audit
We dig through events, funnels, AI usage, roles, and edge cases together. The audit defines what we redesign and, equally important, what we leave alone. Your design lead signs off on the scope before new work begins.
02
AI UX strategy tied to workflows
We define where AI adds value, when automation, recommendations, or copilots guide a decision, and how conversational AI fits the product journey. Strategy decisions get documented in workflow terms, so the rest of the engagement has a clear north star.
03
Prototyping with synthetic users
High-risk flows go into clickable prototypes with realistic or synthetic data. Synthetic user simulations reveal where confusion appears before engineering builds. Conversational AI flows go through the same rigor. Risk on uncertain parts of the product drops before the team commits build time.
04
Scalable UI and design system
Production-ready UI with tokens, components, variants, and patterns for uncertainty, overrides, and explainability. Enterprise-grade documentation for engineering and product to work with. Components map to your data layer so engineering can instrument usage from launch.
05
Rollout and iteration
Implementation guidance, UX QA on staging, iteration tied to adoption, activation, and expansion. We stay close to the team through the first release window, then phase out as your team owns the system. Usage data and qualitative feedback drive the next round of UX changes.
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FAQ
/00-1
How does Lazarev.agency work alongside our internal design team?
We work under your design lead’s direction, inside your file structure, with your token and naming conventions. The lead stays in the driver’s seat on UX language and IA while we handle the volume: complex AI workflows, data-dense states, edge cases. When the engagement ends, your team opens Figma and understands what they’re looking at without a walkthrough from us.
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What does an engagement cost?
The audit (week one) produces a scope outline. The estimate (week two) attaches a number to that scope. Both happen before design work begins, so the budget locks before the pod commits to any specific surface.
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How do you avoid making things prettier and slower, instead of clearer and more adopted?
Adoption and AI-perception are the metrics. Every design decision ties to a workflow, a user behavior, or a state in the product, and the audit phase agrees on the metrics before new work begins. Finished-looking mockups failing to lift usage are how the last vendor left adoption flat. We measure against that.
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What if our product already has a design system?
We extend the system you have. The first two weeks include a system audit: components, tokens, breakpoints, and AI patterns already in place. New work goes through your conventions. Design leads keep ownership of the system; the engagement adds AI patterns into it instead of building parallel to it.
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