10+ years
of experience
in UI/UX design
in UI/UX design
/ 01
UI UX consulting
Become a client
At Lazarev.agency, we audit the surfaces where AI products lose users, advise on AI UX patterns missing from the current system, and stay long enough to see the recommendations reach production. The result is palpable product progress with recommendations translated into successfully launched experiences and live workflows.
- Audit findings the team can build from
A product redesign audit shouldn't land as a 47-slide deck nobody on the team can act on. We produce an audit with prioritized recommendations, draft UX patterns for the highest-impact gaps, and a 4–8 week design plan your team can run alone or with us, depending on capacity. - AI adoption gaps surfaced inside the next QBR window
Teams looking for adoption signals get usage-backed audit findings: where users drop, and which features they ignore. The audit lands inside one QBR cycle. The recommendations open the next, with AI surfaces flagged where they're underperforming. - AI patterns ready for your design lead to extendYour design team gets an audit speaking their language: tokens, components, variants, edge cases. Draft UX patterns arrive in your file conventions, with documented tradeoffs. The lead picks the direction, and the team carries the patterns forward without reverse-engineering a vendor's system.
/ 01
$500M+
in funding secured
for our clients
for our clients
/ 02
120+
awards backing
our excellence
our excellence
/ 03
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 consulting fails AI product teams
A traditional UI UX consulting engagement produces three artifacts: the audit, the recommendations, and the final deliverable. All three look right in the review meeting. None of them tell the team what to open in Figma the next morning. The metric the engagement was meant to move stays where it was.
Audits with no AI fluency behind them
A consultancy audits an AI product and applies generic UX patterns designed for static interfaces. Recommendations miss the inference-time UX entirely — failure modes, confidence states, override flows, citation surfaces. The team gets feedback on visuals while the AI behavior keeps undermining the adoption.
Founders billed for a deck they can’t execute
A consulting engagement ends with a strategic deck, prioritized recommendations, and a goodbye email. Founders racing toward the next round have no internal design lead to translate the deck into a working product. Runway burns while the team negotiates with vendors who could execute it.
Recommendations divorced from the design system
The audit prescribes “redesign the onboarding flow” and “add an AI explainability surface”. None of the recommendations come in the team’s tokens, conventions, or component library.
How our UI UX consulting reaches production
Our UI UX consulting ends in a deliverable your team can build from. The audit phase runs on your usage data; the deliverable features draft patterns in your design system conventions; and we stay close to implementation so the recommendations reach production.
Audit grounded in your usage data
We pull your analytics, your funnels, and your AI usage signals before writing a single recommendation. The audit cites the specific workflows losing users and the specific AI features sitting in the dark. Recommendations land where the metric moves.
Draft AI UX patterns inside the consulting deliverable
The audit ships with draft AI UX patterns for the highest-impact gaps — copilot surfaces, citation states, override flows, explainability panels. Each pattern arrives in your file conventions with documented tradeoffs. Your design lead picks a direction, and your team builds from a concrete starting point.
Recommendations expressed in your design system
Every recommendation maps to your design tokens and components. New AI patterns get added as extensions of the current system. The team builds inside the system already owned rather than parallel to a system a consultancy invented.
Execution support, on-demand and optional
For teams looking for the consulting to land in production within the engagement window, we stay through implementation. Direct execution if your team is at capacity; pair-work with your team if the lead wants ownership.
Case studies
UI UX consulting work in action
The audits below turned into production releases tied to tangible business impact. Complex enterprise platforms became easier to navigate, research-heavy workflows dropped from nearly an hour to minutes, and commerce experiences increased engagement, add-to-basket actions, and checkout progression after redesign.
Four shapes UI UX consulting takes
Why teams pick us for UI UX consulting
If you've hired a UI UX consulting firm for your AI UX work and walked away with a deck nobody could build from, you're not the only one. Most teams arriving here have done the same dance, sometimes twice. The retro line we hear when the engagement lands, almost verbatim: "this is the deliverable we hoped to get from every consulting firm before you".
/ 01
Finally, an AI system our users actually want to use instead of avoiding.
/ 02
I've never worked with a vendor operating so well, especially on a creative job.
Where UI UX consulting takes on the deepest work
UI UX consulting at Lazarev.agency takes on three product shapes most often. The first: data-dense control surfaces in fintech, logistics, and healthcare, where adoption depends on clarity. The second: multi-role workflows in enterprise SaaS and legal tech, where every role sees the AI differently. The third: AI-native products where the model team launches features faster than the design team can absorb. Each shape carries its own AI UX failure pattern.
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
/ 02
600+
projects
successfully completed
successfully completed
/ 03
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.
How a UI UX consulting engagement runs
We start with your usage data, surface AI UX gaps with metrics to back them, draft patterns within your system, and stay close to execution so the audit findings reach production. Direct execution or pair-work with your team, depending on capacity.
Findings reach production. Decks don’t.
01
Intake and audit scope lock
The intake exists to narrow the audit. Working sessions with your team help surface which workflows are losing users and where AI features have upside if the UX gets fixed. Analytics, funnel data, and AI usage signals fill in what the working sessions miss. By the end of week two, the audit scope is locked.
02
Audit and AI UX gap mapping
Heuristic review against AI UX patterns, usage analysis on the priority workflows, and gap mapping against the AI features the team wants to highlight. Findings get prioritized by adoption impact and effort to fix. Draft patterns go into your file conventions.
03
Pattern advisory and design system extension
The audit deliverable includes a reusable AI UX pattern library covering the highest-impact gaps. Tokens, components, and variants for AI behavior. Your design lead picks direction; the patterns plug into your existing design system.
04
Execution support
Direct execution if your team is at capacity, or pair-work with your team if the lead wants ownership. The audit findings reach production inside the engagement window. Implementation guidance, UX QA on staging, and instrumentation so adoption signals appear in your analytics.
05
Follow-up audit and iteration
After the first surfaces reach production, we run a follow-up audit on the metrics the engagement targeted. Adoption signals, edit rates, completion rates, time-to-value. The next quarter’s decisions sit on usage data; the consulting loop closes inside one funded cycle.
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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. We handle the audit volume — complex AI workflows, data-dense states, edge cases. Design leads end the engagement with AI patterns inside their existing system.
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Will I get a deck I can’t use, or recommendations the team can act on?
Every audit produces prioritized UI/UX recommendations with draft AI UX patterns in your design system conventions. The recommendations cite specific workflows, specific usage signals, and specific surfaces to design. Founders with no internal design lead get a sequence they can launch. Design leads get a starting point in their own file structure.
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What does UI UX consulting cost?
Engagement size depends on the scope of the audit, the complexity of the AI product, the size of the pod, and the timeline. After a structured intake, you get a concrete estimate.
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Do you only audit, or do you stay through execution?
Both shapes available. Some teams want a focused audit and execute internally; others need the consulting to land in production within the engagement window. For audit-only engagements, the deliverable is designed so the team can execute alone. For audit-plus-execution, we stay close to implementation through UX QA and rollout.
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