10+ years
of experience
in UI/UX design
in UI/UX design
/ 01
AI consulting services
Become a client
Most AI consulting ends before the product reaches real usage. We connect strategy to execution by defining where AI belongs inside existing workflows, how the system behaves at inference time, and how the experience drives adoption among the customers paying for it.
- A prioritized AI roadmap designed to hold up
Our AI consulting delivers a prioritized roadmap with risk-adjusted bets, capability-readiness scores, and adoption metrics attached to each initiative. Founders walk into board meetings and investor reviews with an AI story that holds up against scrutiny. - AI ROI you can show in the next QBR
Every AI initiative carries an adoption number into executive reviews. The consulting picks the workflows where AI moves the metric, designs the surfaces moving it, and instruments usage so the next review opens with adoption data leadership recognizes. - AI behavior standards your team will follow Your team gets a reusable AI pattern library inside their system: copilots, recommendations, confidence states, override flows, and explainability surfaces. The work hands off in your conventions, so future AI releases land without reverse-engineering decisions a vendor made once.
/ 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
Why most AI consulting keeps disappointing businesses
Founders, Heads of AI, and design leads describe the same disappointment with traditional AI consulting. The strategy lands on a slide and never reaches the product. Pilot wins inside a single workflow, and production comes to a halt everywhere else. The consultancy leaves before usage data exists, so nobody knows what worked.
Winning pilots and failing products
A consulting team runs a successful pilot on one workflow, books the case study, and leaves. The same pattern can’t be applied across the rest of the product because nobody designed the underlying UX. Heads of Product end the year with one celebrated pilot and twelve frozen feature requests.
Founders sold strategy without execution
Founders pay for an AI roadmap, then spend the next six months realizing product execution requires a second engagement with a second vendor. Runway disappears between strategy and a working demo while the team waits for execution guidance in vain.
Strategy decks disconnected from inference-time reality
Slides describe AI as a deterministic feature: “the model recommends X, the user accepts”. Inference time looks nothing like that. Latency, hallucinations, partial responses, and tool failures show up in week one of production. The strategy didn’t account for them, so the team has to rebuild the UX after the consultants leave.
Internal design leads handed AI requirements with no patterns
A consulting team specifies what the AI should do. The design lead figures out how it should look and behave. Without a reusable AI pattern library, every feature becomes a one-off. The lead absorbs the design debt every AI release adds, with no calendar to write the patterns down.
How AI consulting comes together at Lazarev.agency
Most AI consulting stops one layer above the product. We bring the missing layer beneath the strategy: prototypes, pattern libraries, and engineering-ready specs built for implementation within the same engagement. Strategy and execution share the same team and the same accountability.
Multi-stakeholder discovery before recommendations
Sessions across Product, AI, Engineering, and GTM run in parallel rather than in sequence. Decisions get pressure-tested against every function before strategy locks. The artifact comes back with a cross-functional sign-off attached.
A decision framework your team keeps after we leave
Not every workflow benefits from AI. We hand over a decision framework, i.e., workflows mapped against AI suitability and lift potential, your team can use for the next AI decision after the engagement ends. The framework outlasts the deck.
Working prototypes attached to every recommendation
Every strategic recommendation comes with a clickable prototype wired to realistic or synthetic data. Stakeholders run them; procurement reviews them; the strategy gets validated outside the room before engineering touches it. Consulting becomes something teams can interact with, pressure-test, and evaluate before a single sprint begins.
Co-piloted execution alongside your internal teams
We work with your Product, AI, and engineering leads on the same surfaces, in your file structure, on your timeline. The engagement leaves your team able to extend the strategy on their own.
Case studies
Cases of AI strategy and execution running together
Fintech AI risk platforms increasing usage after an integrated strategy and UX engagement. B2B AI copilots raising Series B on a demo-led product built in 3 months. Enterprise dashboards where time-to-decision drops and win rates climb on deals where the new AI UX gets demoed.
Five shapes AI consulting engagements take
Why teams pick us for AI consulting
Teams come to us when the next QBR needs palpable adoption signals, when the internal design team needs AI patterns inside their system, or when a previous consulting engagement ended with a slide deck nobody could build from. Close-out conversations tend to circle the same point: the strategy and the surface that shipped came from the same hands, and the team never had to translate between vendors to get from one to the other.
/ 01
"We saw an increase in engagement metrics, in users and in resume submissions."
/ 02
"Lazarev is top-notch in what they do and they charge accordingly."
Industries we know AI deeply inside
AI and ML platforms, fintech, healthcare, logistics, legal tech, ad/martech, real estate, media and content, Web3, enterprise SaaS — the industries with AI facing dense data, regulatory pressure, and trust constraints. We bring AI consulting where the workflows are complex and the cost of a wrong AI decision is high.
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 an AI consulting engagement runs
Our AI consulting covers discovery, strategy, validation, and advisory rollout inside one engagement. The team running the discovery phase is the same team running the validation and rollout phases, so the recommendation and the decision framework come from the same hands and the same accountability.
Listing AI use cases is easy. Sequencing them around risk isn't.
01
Intake and AI opportunity audit
Stakeholder sessions across Product, AI, Design, Engineering, and GTM. We dig through events, AI usage, roles, and edge cases, then surface the workflows where AI has real upside and the ones where AI keeps creating new failure modes. The audit defines what the engagement focuses on.
02
AI strategy and roadmap prioritization
We agree on the metrics, the workflows, and the AI capabilities. The strategy gets documented in workflow terms with risk-adjusted prioritization, capability-readiness scores, and adoption metrics attached to each initiative. Roadmap decisions get made before the team commits engineering time.
03
Recommendation validation with synthetic users
The highest-risk recommendations get pressure-tested through clickable prototypes wired to realistic or synthetic data. Synthetic user simulations expose where the recommended interaction breaks before engineering touches it. The strategy gets validated outside the room; recommendations get refined before they reach the engineering backlog.
04
Pattern library and decision framework handoff
The engagement produces an AI UX pattern library, behavior standards for copilots and recommendations, and a decision framework for when to apply each pattern. Design leads inherit reusable assets in their conventions; future AI features get designed against the standards instead of from scratch every release.
05
Implementation advisory and adoption tracking
We support the team through the first implementation window: structured check-ins, UX advisory on the highest-risk surfaces, and the adoption measurement framework the C-suite reads at the next review. The advisory phases out as your team takes ownership of the loop.
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FAQ
/00-1
How is this different from a Big Four AI consulting practice?
Big Four consulting tends to stop at strategy and capability assessment. We carry strategy into design, design into production, and production into measurable usage. The team writing the strategy is the team designing the surfaces. The buyer ends the engagement with a working AI product and adoption data.
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How fast can we see AI adoption move?
Inside a 4–8 month engagement, the first AI surfaces typically reach production around month 3 with usage instrumentation attached. Adoption signals show up in the next QBR. For Heads of AI under quarterly pressure, the engagement is structured so something testable lands inside the first quarter.
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What does an AI consulting engagement cost?
Engagement size depends on the complexity of the AI product, the scope of the work, the size of the pod, and the timeline. After a structured intake and audit, you get a concrete estimate.
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How does this work alongside our internal design team?
The consulting embeds alongside your internal teams. Our team brings options and tradeoffs to every structural recommendation. The Heads of AI, Product, and Engineering on the engagement get the same model — scoped decision rights at every phase gate. By the time the engagement ends, the team owns the roadmap, the pattern library, and the decision framework in their conventions.
/00-5
Can we engage you for a discovery phase only, before committing to a full engagement?
Yes. The 6–10 week AI strategy advisory engagement listed under Services runs as a standalone deliverable: opportunity mapping, capability assessment, and a prioritized roadmap with risk-adjusted bets. About a third of our engagements start this way — the buyer decides at the end of discovery whether to continue into a full engagement. The discovery deliverable stands on its own if the team decides not to.
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